Abstract

Previous studies suggested that miR-146a rs2910164 (C/G) locus was predicted to influence the risk of cancer. However, the relationship of miR-146a rs2910164 locus with colorectal cancer (CRC) susceptibility was controversial. We recruited 1003 CRC patients and 1303 controls, and performed a case–control study to clarify the correlation of miR-146a rs2910164 locus with CRC risk. Subsequently, a comprehensive meta-analysis was conducted to verify our findings. In the case–control study, we suggested that miR-146a rs2910164 variants did not alter CRC risk (CG vs. CC: adjusted P=0.465; GG vs. CC: adjusted P=0.436, CG/GG vs. CC: adjusted P=0.387 and GG vs. CC/CG: adjusted P=0.589), even in subgroup analysis. Next, we conducted a pooled-analysis to identify the correlation of miR-146a rs2910164 locus with CRC risk. In this pooled-analysis, 7947 CRC cases and 12,168 controls were included. We found that miR-146a rs2910164 polymorphism did not influence the risk of CRC (G vs. C: P=0.537; GG vs. CC: P=0.517, CG/GG vs. CC: P=0.520 and GG vs. CC/CG: P=0.167). Our findings suggest that miR-146a rs2910164 C/G polymorphism is not correlated with the susceptibility of CRC. In the future, more case–control studies are needed to confirm our results.

Introduction

In 2018, it was reported that over 1.8 million new colorectal cancer (CRC) cases were diagnosed and 881,000 CRC-related deaths occurred worldwide [1]. In China, CRC ranked both fifth in terms of malignancy incidence and mortality [2]. Trend of cancer incidence indicated that a significant upward trend was found for CRC in developing countries [3]. Previous studies attributed CRC to aging, unhealthy lifestyle and environmental factors [4–7]. It should be very important to understand susceptibility factors of CRC as they could be beneficial for the evaluation of prevention strategies and susceptibility. Accumulating evidences have shown that an individual’s inherited factors also contribute to the development of CRC.

MicroRNA (miR-) involves ∼22 nucleotides. Recently, miRs have been widely investigated and accepted to be implicated in a number of human diseases, especially in malignancy. Adami et al. indicated that miR-146a was overexpressed in primary gastric tumor tissues without the status of chronic Helicobacter pylori infection and expressed a related lower level in progressed gastric cancer [8]. In addition, a recent study reported that miR-146a expression level decreased in progressed CRC patients with lymph node metastasis [9]. Thus, miR-146a was considered to be implicated in the development and progression of cancer [10,11].

In the past decade, several studies have focused on the correlation of miR-146a rs2910164 C/G with various malignancy susceptibility. MiR-146a rs2910164 C/G locus was suggested to be relevant to an increase susceptibility of CRC in several studies [12,13]. However, two publications suggested that miR-146a rs2910164 G allele had a lower susceptibility of CRC compared to those with C allele [14,15]. Notably, several pooled-analyses were performed regarding the relationship of CRC risk with this polymorphism [16–23]. However, the previous observations were diversity or even conflicting. Rong et al. and Liu et al. conducted meta-analyses and found that miR-146a rs2910164 polymorphism may confer risk to CRC [17,19]. Other meta-analyses reported that miR-146a rs2910164 was not related to CRC [16,18,20–23]. The controversial findings might be due to the limited sample sizes. The aim of the present study was to explore the potential correlation of miR-146a rs2910164 polymorphism with CRC risk more extensively. We first conducted a case–control study focusing on the correlation of miR-146a rs2910164 with the risk to CRC. Subsequently, we carried out a meta-analysis to further determine the relationship between miR-146a rs2910164 locus and CRC risk.

Materials and methods

Case–control study

We recruited 1003 sporadic CRC patients between October 2014 and August 2017. They were diagnosed via pathology in Fujian and Jiangsu provinces (China). The mean age of CRC patients was 61.10 ± 12.17 years. In addition, 1303 healthy subjects with a mean age of 61.40 ± 9.61 years were included as controls. The CRC cases and controls were matched by sex and age. The detailed information have been presented in our previous study [24–27]. After giving a written consent in the present study, all participants were questioned the information of risk factor and individual history. The Ethics Committee of Jiangsu University approved protocol of the present study.

Ethylenediamine tetraacetic acid-anticoagulated blood sample was stored in a −80°C refrigerator. Genomic DNA was isolated from lymphocytes by using a DNA Kit (Promega, Madison, U.S.A.). MiR-146a rs2910164 variants were analyzed by a SNPscan Kit. To confirm the obtained results, 4% DNA samples were randomly selected and reciprocally tested by another person. The reproducibility of genotyping was 100%.

Genotype frequency of miR-146a rs2910164 variants and risk factors were compared by using χ2 test. Crude and adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated to predict the association of the miR-146a rs2910164 variants with risk to CRC. A P<0.05 (two-tailed) was used to determine the significance. Hardy–Weinberg equilibrium (HWE) was used to confirm whether the distribution of miR-146a rs2910164 variants accorded with Mendelian inheritance patterns by a Chi-square software. All the comparisons were conducted using SAS 9.4 version software (SAS Institute, Cary, U.S.A.).

Meta-analysis

To verify the correlation between miR-146a rs2910164 locus and the risk of CRC, we subsequently carried out a systematic review and meta-analysis. All studies focusing on the association of miR-146a rs2910164 polymorphism with CRC were identified by a widely internet-based search of China Biology Medicine (CBM), PubMed and Embase databases (published up to March 31, 2019) with the terms of: (miR-146a or hsa-mir-146a or miRNA-146a or MicroRNA-146a or rs2910164) and (colorectal or colon or rectum) and (polymorphism or SNP or variant) and (cancer or carcinoma). Other potential papers were obtained by searching of the references of in eligible publications.

The following included criteria were used: (a) studies based on the association between miR-146a rs2910164 and CRC risk; (b) genotype data were presented; and (c) the genotype distribution in controls consistent with HWE. Two authors (S. Zhang and J. Jiang) extracted the following information independently: the surname of first author, year of publication, country, ethnicity of participant, the region of CRC, method of genotyping and frequencies of miR-146a rs2910164 variants in cases and controls.

Crude ORs with their 95% CIs were harnessed to evaluate the strength of correlation between miR-146a rs2910164 and CRC risk. Chi-square-based Cochran’s Q-test and I2 test were carried out to assess the heterogeneity among the eligible studies, which were defined as statistical significance if I2>50% or P<0.1. The pooled ORs were calculated by a random-effect model (DerSimonian and Laird method), if we found significant heterogeneity [28,29]. Otherwise, the fixed effects model (the Mantel–Haenszel method) was used [30]. Sensitivity analysis was conducted by one-way method, which removed an individual study in turn to address the stability of our findings under all genetic models. In addition, Begg’s test and Egger’s test were used to assess the bias of publication. A P<0.1 was defined as the level for significance of publication bias. The Newcastle–Ottawa Scale (NOS) was used to assess the quality of the included studies. Galbraith radial plot was harnessed to identify the source of heterogeneity. All statistical tests were conducted by using the STATA software (Version 12.0; Stata Corporation, College Station, Texas). A P values (two-sided) less than 0.05 was defined as statistically significant association between miR-146a rs2910164 and CRC risk. By using Power and Sample Size Calculator software [31], power value (α = 0.05) of this meta-analysis was calculated.

Results

Case–control study

This case–control study consisted of participants enrolled in a study conducted in Jiangsu and Fujian provinces (China) from October 2014 to August 2017. The detailed information of the present study was described in our previous studies [24,25]. Briefly, patients with 431 colon cancer and 572 rectum cancer were recruited. CRC cases were unrelated and confirmed histologically. Non-cancer subjects were included as controls from the same populations with age and sex matched. And all subjects were unrelated Chinese Han populations. Table 1 lists demographic information and risk factors that were collected in the present study. Table 2 summarizes the corresponding information of miR-146a rs2910164. The distribution of miR-146a rs2910164 variants in control group is consistent with HWE (P=0.706). Data of genotypes and environmental factors are summarized in Supplementary Table S1.

Table 1
Distribution of selected characteristics in CRC cases and controls
VariableCases (n=1003)Controls (n=1303)P
n%n%
Age (years ± standard deviation) 61.10 ± 12.17 61.40 ± 9.61 0.496* 
Age (years)     0.605 
  <61 451 44.97 600 46.05  
  ≥61 552 55.03 703 53.95  
Sex     0.867 
  Male 620 61.81 801 61.47  
  Female 383 38.19 502 38.53  
Smoking 259 25.82 265 20.34 0.002 
Alcohol use 174 17.35 136 10.44 <0.001 
BMI (kg/m2     
  <24 670 66.80 688 52.80 <0.001 
  ≥24 333 33.20 615 47.20  
Site of tumor      
  Colon cancer 431 42.97    
  Rectum cancer 572 57.03    
VariableCases (n=1003)Controls (n=1303)P
n%n%
Age (years ± standard deviation) 61.10 ± 12.17 61.40 ± 9.61 0.496* 
Age (years)     0.605 
  <61 451 44.97 600 46.05  
  ≥61 552 55.03 703 53.95  
Sex     0.867 
  Male 620 61.81 801 61.47  
  Female 383 38.19 502 38.53  
Smoking 259 25.82 265 20.34 0.002 
Alcohol use 174 17.35 136 10.44 <0.001 
BMI (kg/m2     
  <24 670 66.80 688 52.80 <0.001 
  ≥24 333 33.20 615 47.20  
Site of tumor      
  Colon cancer 431 42.97    
  Rectum cancer 572 57.03    
*

Two-sided student t test;

Two-sided χ2 test

Bold values are statistically significant (P<0.05).

BMI, body mass index

Table 2
Primary information for MiR-146a rs2910164 C/G polymorphism
Genotyped SNPsMiR-146a rs2910164 C/G
Chromosome 
Function non_coding_transcript_variant 
Chr Pos (NCBI Build 38) 160485411 
MAF* for Chinese in database 0.430 
MAF in our controls 0.372 
P value for HWE test in our controls 0.706 
Genotyping method SNPscan 
% Genotyping value 98.87% 
Genotyped SNPsMiR-146a rs2910164 C/G
Chromosome 
Function non_coding_transcript_variant 
Chr Pos (NCBI Build 38) 160485411 
MAF* for Chinese in database 0.430 
MAF in our controls 0.372 
P value for HWE test in our controls 0.706 
Genotyping method SNPscan 
% Genotyping value 98.87% 
*

MAF: minor allele frequency

HWE, Hardy–Weinberg equilibrium

Table 3 shows the genotype distribution of miR-146a rs2910164 in two groups. MiR-146a rs2910164 variant frequencies were 37.55% (CC), 47.96% (CG) and 14.49% (GG) in CRC patients and 39.69% (CC), 46.23% (CG) and 14.08% (GG) in controls. In the studied populations, minor allele frequency (MAF) of miR-146a rs2910164 C/G polymorphism was 0.372, which was similar to SNP database for Chinese populations (MAF = 0.430, Table 2). We found that there were no significant association between miR-146a rs2910164 polymorphism and susceptibility of CRC (CG vs. CC: crude P=0.317; GG vs. CC: crude P=0.520, CG/GG vs. CC: crude P=0.300; GG vs. CC/CG: crude P=0.780). Adjustments for selected risk factors (e.g. age, sex, BMI, smoking and drinking), the null association was also found (CG vs. CC: adjusted P=0.465; GG vs. CC: adjusted P=0.436, CG/GG vs. CC: adjusted P=0.387 and GG vs. CC/CG: adjusted P=0.589; Table 3).

Table 3
Logistic regression analyses of associations between MiR-146a rs2910164 C/G A polymorphism and risk of CRC
GenotypeCases (n=1003)Controls (n=1303)Crude OR (95%CI)PAdjusted OR* (95%CI)P
n%n%
MiR-146a rs2910164         
CC 368 37.55 516 39.69 1.00  1.00  
CG 470 47.96 601 46.23 1.10 (0.92–1.31) 0.317 1.07 (0.89–1.29) 0.465 
GG 142 14.49 183 14.08 1.09 (0.84–1.41) 0.520 1.11 (0.86–1.44) 0.436 
CG+GG 612 62.45 784 60.31 1.09 (0.92–1.30) 0.300 1.08 (0.91–1.28) 0.387 
CC+CG 838 85.51 1,117 85.92 1.00  1.00  
GG 142 14.49 183 14.08 1.03 (0.82–1.31) 0.780 1.07 (0.84–1.36) 0.589 
G allele 754 38.47 967 37.19     
GenotypeCases (n=1003)Controls (n=1303)Crude OR (95%CI)PAdjusted OR* (95%CI)P
n%n%
MiR-146a rs2910164         
CC 368 37.55 516 39.69 1.00  1.00  
CG 470 47.96 601 46.23 1.10 (0.92–1.31) 0.317 1.07 (0.89–1.29) 0.465 
GG 142 14.49 183 14.08 1.09 (0.84–1.41) 0.520 1.11 (0.86–1.44) 0.436 
CG+GG 612 62.45 784 60.31 1.09 (0.92–1.30) 0.300 1.08 (0.91–1.28) 0.387 
CC+CG 838 85.51 1,117 85.92 1.00  1.00  
GG 142 14.49 183 14.08 1.03 (0.82–1.31) 0.780 1.07 (0.84–1.36) 0.589 
G allele 754 38.47 967 37.19     
*

Adjusted for age, sex, BMI, alcohol use and smoking status.

In subgroup analysis, we found that miR-146a rs2910164 variants did not alter the susceptibility of colon cancer (CG vs. CC: adjusted P=0.863; GG vs. CC: adjusted P=0.880, CG/GG vs. CC: adjusted P=0.846 and GG vs. CC/CG: adjusted P=0.926; Table 4) or rectal cancer (CG vs. CC: adjusted P=0.420; GG vs. CC: adjusted P=0.337, CG/GG vs. CC: adjusted P=0.324 and GG vs. CC/CG: adjusted P=0.484; Table 4).

Table 4
Stratified analyses between MiR-146a rs2910164 polymorphism and CRC risk by site of tumor
GenotypeControls (n=1303)Colon cancer cases (n=431)Crude OR (95%CI)PAdjusted OR* (95%CI)P*Rectum cancer cases (n=572)Crude OR (95%CI)PAdjusted OR* (95%CI)P*
n%n%n%
MiR-146a rs2910164               
CC 516 39.69 165 39.01 1.00  1.00  203 36.45 1.00  1.00  
CG 601 46.23 200 47.28 1.04 (0.82–1.32) 0.742 1.02 (0.80–1.30) 0.863 270 48.47 1.14 (0.92–1.42) 0.230 1.10 (0.88–1.37) 0.420 
GG 183 14.08 58 13.71 0.99 (0.70–1.40) 0.960 1.03 (0.73–1.45) 0.880 84 15.08 1.17 (0.86–1.58) 0.322 1.17 (0.85–1.59) 0.337 
CG+GG 784 60.31 258 60.99 1.03 (0.82–1.29) 0.803 1.02 (0.82–1.28) 0.846 354 63.55 1.15 (0.94–1.41) 0.188 1.11 (0.90–1.37) 0.324 
CC+CG 1117 85.92 365 86.29 1.00  1.00  473 84.92 1.00  1.00  
GG 183 14.08 58 13.71 0.97 (0.71–1.33) 0.852 1.02 (0.74–1.40) 0.926 84 15.08 1.08 (0.82–1.43) 0.572 1.11 (0.83–1.47) 0.484 
G allele 967 37.19 316 37.35     438 39.32     
GenotypeControls (n=1303)Colon cancer cases (n=431)Crude OR (95%CI)PAdjusted OR* (95%CI)P*Rectum cancer cases (n=572)Crude OR (95%CI)PAdjusted OR* (95%CI)P*
n%n%n%
MiR-146a rs2910164               
CC 516 39.69 165 39.01 1.00  1.00  203 36.45 1.00  1.00  
CG 601 46.23 200 47.28 1.04 (0.82–1.32) 0.742 1.02 (0.80–1.30) 0.863 270 48.47 1.14 (0.92–1.42) 0.230 1.10 (0.88–1.37) 0.420 
GG 183 14.08 58 13.71 0.99 (0.70–1.40) 0.960 1.03 (0.73–1.45) 0.880 84 15.08 1.17 (0.86–1.58) 0.322 1.17 (0.85–1.59) 0.337 
CG+GG 784 60.31 258 60.99 1.03 (0.82–1.29) 0.803 1.02 (0.82–1.28) 0.846 354 63.55 1.15 (0.94–1.41) 0.188 1.11 (0.90–1.37) 0.324 
CC+CG 1117 85.92 365 86.29 1.00  1.00  473 84.92 1.00  1.00  
GG 183 14.08 58 13.71 0.97 (0.71–1.33) 0.852 1.02 (0.74–1.40) 0.926 84 15.08 1.08 (0.82–1.43) 0.572 1.11 (0.83–1.47) 0.484 
G allele 967 37.19 316 37.35     438 39.32     
*

Adjusted for age, sex, smoking status, alcohol use and BMI status.

Meta-analysis of MiR-146a rs2910164 locus and CRC

Next, we conducted a pooled-analysis to identify the correlation of miR-146a rs2910164 with CRC risk. At first, 96 abstracts were collected by searching CBM, EMBASE and Pubmed databases. Figure 1 summarizes the process of meta-analysis. Some publications contained stratified analysis, we treated these subgroups separately [14,32–34]. Table 5 shows the characteristics and miR-146a rs2910164 genotypes of included case-control studies. Finally, 7947 CRC cases and 12,168 controls were included to analyze the relationship of miR-146a rs2910164 with CRC risk [12–15,32–41].

Flow diagram of the meta-analysis of the association between MiR-146a rs2910164 polymorphism and CRC risk

Figure 1
Flow diagram of the meta-analysis of the association between MiR-146a rs2910164 polymorphism and CRC risk
Figure 1
Flow diagram of the meta-analysis of the association between MiR-146a rs2910164 polymorphism and CRC risk
Table 5
Characteristics and genotypes of the included studies in meta-analysis for MiR-146a rs2910164 polymorphism
StudyPublication yearEthnicityCountryRegion of CRCSample size (case/control)Genotype methodCaseControlHWENOS
GGCGCCGGCGCC
Hezova et al. 2012 Caucasians Czech Republic Mixed 197/122 Taqman 115 70 12 124 79 Yes 
Min et al. 2012 Asians Korea Colon 251/502 PCR-RFLP 31 144 76 69 245 188 Yes 
Min et al. 2012 Asians Korea Rectum 184/502 PCR-RFLP 28 87 69 69 245 188 Yes 
Chae et al. 2013 Asians Korea Rectum 176/568 PCR-RFLP 23 87 66 121 282 165 Yes 
Chae et al. 2013 Asians Korea Colon 221/568 PCR-RFLP 38 93 90 121 282 165 Yes 
Lv et al. 2013 Asians China Mixed 353/540 PCR-RFLP 54 230 47 96 274 143 Yes 
Ma et al. 2013 Asians China Rectum 588/1,203 Taqman 229 CG/CC : 359* – 397 CG/CC 806* – Yes 
Ma et al. 2013 Asians China Colon 559/1,203 Taqman 215 CG/CC : 344* – 397 CG/CC 806* – Yes 
Vinci et al. 2013 Caucasians Italy Mixed 160/160 HRM 86 57 17 100 65 13 Yes 
Kupcinskas et al. 2014 Caucasians Lithuania and Latvia Mixed 193/428 Taqman 140 50 275 134 15 Yes 
Mao et al. 2014 Asians China Mixed 554/566 SNPscan 70 291 186 85 271 205 Yes 
Hu et al. 2014 Asians China Mixed 276/373 PCR-RFLP 34 82 84 44 187 142 Yes 
Dikaiakos et al. 2015 Caucasians Greece Mixed 157/299 PCR-RFLP 48 101 21 120 158 Yes 
Ying et al. 2016 Asians China Mixed 1358/1079 MassARRAY 223 655 473 163 529 383 Yes 
Zhang et al. 2016 Asians China Mixed 106/53 PCR-RFLP 28 62 16 10 25 18 Yes 
Lindor et al. 2017 Caucasians Australia, U.S.A. and Canada Mixed 899/204 Not available G: 1390  C: 408 G: 320  C: 88 Yes 
Chayeb et al. 2018 Caucasians Tunisia Rectum 57/161 PCR-RFLP 20 31 49 89 23 Yes 
Chayeb et al. 2018 Caucasians Tunisia Colon 95/161 PCR-RFLP 40 46 49 89 23 Yes 
Gao et al. 2018 Asians China Mixed 560/780 PCR-RFLP 130 285 145 149 411 220 Yes 
Our study 2019 Asians China Colon 431/1303 SNPscan 58 200 165 183 601 516 Yes 
Our study 2019 Asians China Rectum 572/1303 SNPscan 84 270 203 183 601 516 Yes 
StudyPublication yearEthnicityCountryRegion of CRCSample size (case/control)Genotype methodCaseControlHWENOS
GGCGCCGGCGCC
Hezova et al. 2012 Caucasians Czech Republic Mixed 197/122 Taqman 115 70 12 124 79 Yes 
Min et al. 2012 Asians Korea Colon 251/502 PCR-RFLP 31 144 76 69 245 188 Yes 
Min et al. 2012 Asians Korea Rectum 184/502 PCR-RFLP 28 87 69 69 245 188 Yes 
Chae et al. 2013 Asians Korea Rectum 176/568 PCR-RFLP 23 87 66 121 282 165 Yes 
Chae et al. 2013 Asians Korea Colon 221/568 PCR-RFLP 38 93 90 121 282 165 Yes 
Lv et al. 2013 Asians China Mixed 353/540 PCR-RFLP 54 230 47 96 274 143 Yes 
Ma et al. 2013 Asians China Rectum 588/1,203 Taqman 229 CG/CC : 359* – 397 CG/CC 806* – Yes 
Ma et al. 2013 Asians China Colon 559/1,203 Taqman 215 CG/CC : 344* – 397 CG/CC 806* – Yes 
Vinci et al. 2013 Caucasians Italy Mixed 160/160 HRM 86 57 17 100 65 13 Yes 
Kupcinskas et al. 2014 Caucasians Lithuania and Latvia Mixed 193/428 Taqman 140 50 275 134 15 Yes 
Mao et al. 2014 Asians China Mixed 554/566 SNPscan 70 291 186 85 271 205 Yes 
Hu et al. 2014 Asians China Mixed 276/373 PCR-RFLP 34 82 84 44 187 142 Yes 
Dikaiakos et al. 2015 Caucasians Greece Mixed 157/299 PCR-RFLP 48 101 21 120 158 Yes 
Ying et al. 2016 Asians China Mixed 1358/1079 MassARRAY 223 655 473 163 529 383 Yes 
Zhang et al. 2016 Asians China Mixed 106/53 PCR-RFLP 28 62 16 10 25 18 Yes 
Lindor et al. 2017 Caucasians Australia, U.S.A. and Canada Mixed 899/204 Not available G: 1390  C: 408 G: 320  C: 88 Yes 
Chayeb et al. 2018 Caucasians Tunisia Rectum 57/161 PCR-RFLP 20 31 49 89 23 Yes 
Chayeb et al. 2018 Caucasians Tunisia Colon 95/161 PCR-RFLP 40 46 49 89 23 Yes 
Gao et al. 2018 Asians China Mixed 560/780 PCR-RFLP 130 285 145 149 411 220 Yes 
Our study 2019 Asians China Colon 431/1303 SNPscan 58 200 165 183 601 516 Yes 
Our study 2019 Asians China Rectum 572/1303 SNPscan 84 270 203 183 601 516 Yes 
*

indicates CG/CC

HWE, Hardy–Weinberg equilibrium

We found that there was no significant association between miR-146a rs2910164 polymorphism and the risk of CRC (G vs. C: P=0.537; GG vs. CC: P=0.517, CG/GG vs. CC: P=0.520 and GG vs. CC/CG: P=0.167; Table 6 and Figure 2). Additionally, subgroup analyses were performed by the ethnicity and region of CRC. Null association between miR-146a rs2910164 and the susceptibility of CRC was also found (Table 6).

Meta-analysis of the association between MiR-146a rs2910164 polymorphism and CRC risk (GG+CG vs. CC, random-effects model)

Figure 2
Meta-analysis of the association between MiR-146a rs2910164 polymorphism and CRC risk (GG+CG vs. CC, random-effects model)
Figure 2
Meta-analysis of the association between MiR-146a rs2910164 polymorphism and CRC risk (GG+CG vs. CC, random-effects model)
Table 6
Meta-analysis of the MiR-146a rs2910164 polymorphism and CRC risk
No. of studyG vs. CGG vs. CCGG+CG vs. CCGG vs. CC+CG
OR (95%CI)PP (Q-test)OR (95%CI)PP (Q-test)OR (95%CI)PP (Q-test)OR (95%CI)PP (Q-test)
Overall 21 1.03 (0.94–1.12) 0.537 <0.001 1.06 (0.89–1.27) 0.517 0.002 1.05 (0.90–1.24) 0.520 <0.001 1.08 (0.97–1.20) 0.167 0.033 
Type              
Colon cancer 1.01 (0.80–1.28) 0.908 0.006 0.98 (0.65–1.48) 0.914 0.040 1.02 (0.69–1.51) 0.926 0.002 1.07 (0.84–1.35) 0.600 0.069 
Rectal cancer 0.98 (0.78–1.22) 0.823 0.021 0.95 (0.59–1.51) 0.815 0.024 0.97 (0.74–1.28) 0.853 0.083 1.05 (0.80–1.36) 0.741 0.037 
Mixed type of CRC 11 1.06 (0.95–1.17) 0.294 0.021 1.17 (0.92–1.48) 0.192 0.044 1.12 (0.87–1.44) 0.373 <0.001 1.09 (0.97–1.22) 0.132 0.176 
Ethnicity              
Asians 14 1.03 (0.94–1.13) 0.547 0.002 1.06 (0.88–1.29) 0.530 0.003 1.09 (0.92–1.29) 0.343 <0.001 1.06 (0.93–1.19) 0.393 0.023 
Caucasians 1.03 (0.84–1.26) 0.796 0.020 1.09 (0.63–1.87) 0.760 0.083 0.95 (0.60–1.50) 0.835 0.080 1.16 (0.96–1.41) 0.129 0.268 
NOS scores              
≥6 19 1.04 (0.95–1.14) 0.396 <0.001 1.08 (0.90–1.30) 0.405 0.002 1.07 (0.91–1.26) 0.419 <0.001 1.09 (0.97–1.21) 0.142 0.030 
< 6 0.91 (0.74–1.12) 0.368 0.712 0.66 (0.30–1.43) 0.291 – 0.66 (0.31–1.41) 0.286 – 0.91 (0.59–1.39) 0.654 – 
No. of studyG vs. CGG vs. CCGG+CG vs. CCGG vs. CC+CG
OR (95%CI)PP (Q-test)OR (95%CI)PP (Q-test)OR (95%CI)PP (Q-test)OR (95%CI)PP (Q-test)
Overall 21 1.03 (0.94–1.12) 0.537 <0.001 1.06 (0.89–1.27) 0.517 0.002 1.05 (0.90–1.24) 0.520 <0.001 1.08 (0.97–1.20) 0.167 0.033 
Type              
Colon cancer 1.01 (0.80–1.28) 0.908 0.006 0.98 (0.65–1.48) 0.914 0.040 1.02 (0.69–1.51) 0.926 0.002 1.07 (0.84–1.35) 0.600 0.069 
Rectal cancer 0.98 (0.78–1.22) 0.823 0.021 0.95 (0.59–1.51) 0.815 0.024 0.97 (0.74–1.28) 0.853 0.083 1.05 (0.80–1.36) 0.741 0.037 
Mixed type of CRC 11 1.06 (0.95–1.17) 0.294 0.021 1.17 (0.92–1.48) 0.192 0.044 1.12 (0.87–1.44) 0.373 <0.001 1.09 (0.97–1.22) 0.132 0.176 
Ethnicity              
Asians 14 1.03 (0.94–1.13) 0.547 0.002 1.06 (0.88–1.29) 0.530 0.003 1.09 (0.92–1.29) 0.343 <0.001 1.06 (0.93–1.19) 0.393 0.023 
Caucasians 1.03 (0.84–1.26) 0.796 0.020 1.09 (0.63–1.87) 0.760 0.083 0.95 (0.60–1.50) 0.835 0.080 1.16 (0.96–1.41) 0.129 0.268 
NOS scores              
≥6 19 1.04 (0.95–1.14) 0.396 <0.001 1.08 (0.90–1.30) 0.405 0.002 1.07 (0.91–1.26) 0.419 <0.001 1.09 (0.97–1.21) 0.142 0.030 
< 6 0.91 (0.74–1.12) 0.368 0.712 0.66 (0.30–1.43) 0.291 – 0.66 (0.31–1.41) 0.286 – 0.91 (0.59–1.39) 0.654 – 

CRC, colorectal cancer;

In this analysis, publication bias was assessed by using Begg’s test and Egger’s linear regression test. And no significant bias was found in any genetic model (Begg’s test: G vs. C: P=0.820; GG vs. CC: P=0.649; GG/CG vs. CC: P=0.649; GG vs. CC/CG: P=0.381; Egger’s linear regression test: G vs. C: P=0.980; GG vs. CC: P=0.761; GG/CG vs. CC: P=0.686; GG vs. CC/CG: P=0.215; Figure 3).

Begg’s funnel plot of meta-analysis (GG+CG vs. CC, random-effects model)

Figure 3
Begg’s funnel plot of meta-analysis (GG+CG vs. CC, random-effects model)
Figure 3
Begg’s funnel plot of meta-analysis (GG+CG vs. CC, random-effects model)

We used one-way method to evaluate the sensitivity of this analysis. The results suggested that any individual study could not materially alter the association between miR-146a rs2910164 and CRC risk (Figure 4).

Sensitivity analysis in dominant model (random-effects estimates)

Figure 4
Sensitivity analysis in dominant model (random-effects estimates)
Figure 4
Sensitivity analysis in dominant model (random-effects estimates)

Since significant heterogeneity among the studies was found, we conducted subgroup analyses and Galbraith radial plot to identify the source of heterogeneity in a dominant model. Because ethnicity and type of CRC could affect the observations of meta-analysis, subgroup analyses were carried out (Table 6). We found that rectal cancer and Asians might lead to major source of heterogeneity. In Galbraith radial plot, we could find five outliers [12,13,35–37], which contributed significant heterogeneity to this analysis (Figure 5).

Galbraith radial plot of meta-analysis (GG+CG vs. CC, random-effects model)

Figure 5
Galbraith radial plot of meta-analysis (GG+CG vs. CC, random-effects model)
Figure 5
Galbraith radial plot of meta-analysis (GG+CG vs. CC, random-effects model)

NOS scores of the included studies were summarized in Table 5. According to the criterion mentioned in previous study [42,43], we found that only two investigations were poor quality studies [36,44]. When we omitted these poor quality investigations, the findings were not changed.

For this pooled-analysis, the power value (α = 0.05) was 0.258 in G vs. C, 0.235 in GG vs. CC, 0.275 in GG/CG vs. CC and 0.579 in GG vs. CC/CG genetic model.

Discussion

In CRC patients, miR-146a expression level decreased in progressed tumors [9]. Since miR-146a plays important roles in the development of CRC, we presumed that miR-146a rs2910164 variants could influence the susceptibility to CRC. Here, we first performed a case–control study and subsequently conducted a meta-analysis on the potential correlation between miR-146a rs2910164 polymorphism and the susceptibility of CRC. In the case–control study, we recruited 1003 sporadic CRC patients and 1303 controls. In meta-analysis, the present study included 21 independent studies with 20,115 subjects (7947 CRC patients and 12,168 controls). Nevertheless, null correlation has been identified between miR-146a rs2910164 locus and the risk of CRC, even in different subgroup.

To date, some studies have been carried out to assess this potential correlation between miR-146a rs2910164 polymorphism and CRC susceptibility in different populations. However, conflicting findings were found. Consequently, we designed a case–control study matching with age and sex. We got a null correlation between this SNP and the risk of CRC. These findings were similar to some previous publications, which the genotype frequency of the miR-146a rs2910164 G allele had no significant difference in CRC cases versus non-cancer controls [35,36,39,40]. It was worth mentioning that the paper was published by Ying et al. [40], where the investigation included more than 1000 CRC patients and 1000 controls. However, other publications, identified a significant associations or even suggested opposite findings. Dikaiakos et al. [15] and Chae et al. [14] reported a decreased risk of the miR-146a rs2910164 G allele to CRC. Recently, two publications found an association of the increased risk with miR-146a rs2910164 G allele [12,13]. It could be interpreted that these investigations were carried out in different ethnicity, region, age and with variable sample size. A recent meta-analysis has found an association for the European populations in the recessive model [45]. Compared with the meta-analysis mentioned above, the current meta-analysis has included more case–control studies with larger sample sizes. Thus, our findings might be more credible.

In the large-scale pooled-analysis, we found that miR-146a rs2910164 might not confer the risk to CRC. However, the interaction between genes and environment factors might be implicated in the occurrence of CRC. Reviewing early publications, some reasons were identified for the conflicting findings. First, different ethnicity may lead to the different results, since the genotype frequencies of the miR-146a rs2910164 were diverse between Asians and Caucasians. For example, in the present study, MAF value of miR-146a rs2910164 (C/G) was 0.404 in Asians, while the value was 0.631 in Caucasians. On the other hand, most of the included case–control studies did not focus on some vital environmental risk factors (e.g., low intake of dietary fiber, alcohol consumption, tobacco use, obesity, overweight and being physically inactive). It is possible that miR-146a rs2910164 has influence the development of CRC, while those environmental factors mentioned above may cover up the role of C→G variation in rs2910164. Thus, in the future, more case–control studies should be designed to explore the relationship of miR-146a rs2910164 on the risk of CRC, especially focusing on the interaction of gene–environmental factors.

In the present study, heterogeneity might be noted, which could significantly affect the explaination of our findings. When we carried out subgroup analyses, significant heterogeneity was found in colon cancer and Asians subgroups. Combined the Galbraith radial plot (Figure 5) and the forest plot (Figure 2) in a dominant model, we identified five outliers [12,13,35–37], which might contribute to prominent heterogeneity. The quality evaluation was used to improve the preent study. In the meta-analysis, we found that only two investigations were poor quality study [36,44]. In subgroup analysis, when we omitted them, the findings were not altered materially.

There are, whereas, some potential limitations in the present study. First, this case–control study only focused on Chinese Han populations. Second, for lack of parameter of risk factors, we only calculate the crude ORs and CIs to assess the correlation between miR-146a rs2910164 and CRC risk in subsequent meta-analysis. Finally, due to the limited participants in subgroups, the power of subgroup analysis might be insufficient.

To conclude, our findings have not supported a relationship between the miR-146a rs2910164 and risk of CRC. The importance of this locus as a genetic predictor for the development of CRC may be very inappreciable and the significance of hereditary screening in healthy individuals may be lack of evidence. In the future, larger studies with well-matched controls are needed to confirm our findings.

Data Availability

The genotypes and environmental factors are summarized in Supplementary Table S1.

Competing Interests

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

Funding

The project was supported by the Application and Basic Research Project of Changzhou City [grant number CJ20180068].

Author Contribution

Conceived and designed the experiments: Z.Q. Performed the experiments: S.Z., J.J., W.T. Analyzed the data: J.J., W.T. Contributed reagents/materials/analysis tools: Z.Q. Wrote the manuscript: S.Z., J.J.

Acknowledgements

We appreciate all subjects who participated in this study. We wish to thank Dr Yan Liu (Genesky Biotechnologies Inc., Shanghai, China) for technical support.

Abbreviations

     
  • CI

    confidence interval

  •  
  • CRC

    colorectal cancer

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • OR

    odds ratio

References

References
1.
Bray
F.
,
Ferlay
J.
,
Soerjomataram
I.
et al.
(
2018
)
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
.
CA Cancer J. Clin.
68
,
394
424
[PubMed]
2.
Chen
W.
,
Zheng
R.
,
Baade
P.D.
et al.
(
2016
)
Cancer statistics in China, 2015
.
CA Cancer J. Clin.
66
,
115
132
[PubMed]
3.
Bode
A.M.
,
Dong
Z.
and
Wang
H.
(
2016
)
Cancer prevention and control: alarming challenges in China
.
Natl. Sci. Rev.
3
,
117
127
PMC4904843
[PubMed]
4.
Jemal
A.
,
Bray
F.
,
Center
M.M.
et al.
(
2011
)
Global cancer statistics
.
CA Cancer J. Clin.
61
,
69
90
[PubMed]
5.
Mathers
J.C.
(
2019
)
Obesity and bowel cancer: from molecular mechanisms to interventions
.
Nutr. Res.
70
,
26
31
6.
Niederreiter
L.
,
Adolph
T.E.
and
Tilg
H.
(
2018
)
Food, microbiome and colorectal cancer
.
Digestive Liver Dis.: Off. J.Italian Soc. Gastroenterol. Italian Assoc. Study of the Liver
50
,
647
652
[PubMed]
7.
Rossi
M.
,
Jahanzaib Anwar
M.
,
Usman
A.
et al.
(
2018
)
Colorectal Cancer and Alcohol Consumption-Populations to Molecules
.
Cancers
10
,
38
PMC5836070
8.
Adami
B.
,
Tabatabaeian
H.
,
Ghaedi
K.
et al.
(
2019
)
miR-146a is deregulated in gastric cancer
.
J. Cancer Res. Therap.
15
,
108
114
[PubMed]
9.
Liu
Z.
,
Li
Y.
and
Luo
Q.
(
2018
)
Relationship between CT signs and the expression of miR-146a in colon cancer
.
Oncol. Lett.
16
,
6598
6602
PMC6176405
[PubMed]
10.
Testa
U.
,
Pelosi
E.
,
Castelli
G.
et al.
(
2017
)
miR-146 and miR-155: Two Key Modulators of Immune Response and Tumor Development
.
Non-coding RNA
3
,
22
PMC5831915
[PubMed]
11.
Labbaye
C.
and
Testa
U.
(
2012
)
The emerging role of MIR-146A in the control of hematopoiesis, immune function and cancer
.
J. Hematol. Oncol.
5
,
13
PMC3342163
[PubMed]
12.
Lv
M.
,
Dong
W.
,
Li
L.
et al.
(
2013
)
Association between genetic variants in pre-miRNA and colorectal cancer risk in a Chinese population
.
J. Cancer Res. Clin. Oncol.
139
,
1405
1410
[PubMed]
13.
Zhang
H.
,
Cui
S.
,
Lin
Y.
,
Yang
Y.
,
Lai
W.
,
Lup
S.
et al.
(
2016
)
Correlation between polymorphism of single nucleotide gene rs2910164 in miR-146a and incidence and metastasis of colorectal cancer
.
J. Shanghai Jiaotong Univ. (Chin. Ed.)
36
,
809
813
14.
Chae
Y.S.
,
Kim
J.G.
,
Lee
S.J.
et al.
(
2013
)
A miR-146a polymorphism (rs2910164) predicts risk of and survival from colorectal cancer
.
Anticancer Res.
33
,
3233
3239
[PubMed]
15.
Dikaiakos
P.
,
Gazouli
M.
,
Rizos
S.
et al.
(
2015
)
Evaluation of genetic variants in miRNAs in patients with colorectal cancer
.
Cancer Biomarkers: Section A Dis. Markers
15
,
157
162
[PubMed]
16.
Alidoust
M.
,
Hamzehzadeh
L.
,
Rivandi
M.
et al.
(
2018
)
Polymorphisms in non-coding RNAs and risk of colorectal cancer: A systematic review and meta-analysis
.
Crit. Rev. Oncol. Hematol.
132
,
100
110
[PubMed]
17.
Rong
G.Q.
,
Zhang
X.M.
,
Chen
B.
et al.
(
2017
)
MicroRNA gene polymorphisms and the risk of colorectal cancer
.
Oncol. Lett.
13
,
3617
3623
PMC5431414
[PubMed]
18.
Xu
L.
and
Tang
W.
(
2016
)
Associations of Polymorphisms in mir-196a2, mir-146a and mir-149 with Colorectal Cancer Risk: A Meta-Analysis
.
Pathol. Oncol. Res.: POR
22
,
261
267
[PubMed]
19.
Liu
X.X.
,
Wang
M.
,
Xu
D.
et al.
(
2015
)
Quantitative Assessment of the Association between Genetic Variants in MicroRNAs and Colorectal Cancer Risk
.
BioMed. Res. Int.
2015
,
276410
PMC4452836
[PubMed]
20.
Xie
W.Q.
,
Tan
S.Y.
and
Wang
X.F.
(
2014
)
Effect of a common genetic variant microRNA-146a rs2910164 on colorectal cancer: a meta-analysis
.
J. Digestive Dis.
15
,
647
653
[PubMed]
21.
Wu
Y.
,
Hao
X.
,
Feng
Z.
et al.
(
2015
)
Genetic polymorphisms in miRNAs and susceptibility to colorectal cancer
.
Cell Biochem. Biophys.
71
,
271
278
[PubMed]
22.
Du
W.
,
Ma
X.L.
,
Zhao
C.
et al.
(
2014
)
Associations of single nucleotide polymorphisms in miR-146a, miR-196a, miR-149 and miR-499 with colorectal cancer susceptibility
.
Asian Pacific J. Cancer Prevent.: APJCP
15
,
1047
1055
[PubMed]
23.
Wan
D.
,
Gu
W.
,
Xu
G.
et al.
(
2014
)
Effects of common polymorphisms rs2910164 in miR-146a and rs11614913 in miR-196a2 on susceptibility to colorectal cancer: a systematic review meta-analysis
.
Clin. Transl. Oncol.: Off. Publication Feder. Spanish Oncol. Societies Natl. Cancer Institute Mexico
16
,
792
800
24.
Zou
C.
,
Qiu
H.
,
Tang
W.
et al.
(
2018
)
CTLA4 tagging polymorphisms and risk of colorectal cancer: a case-control study involving 2,306 subjects
.
OncoTargets Ther.
11
,
4609
4619
PMC6086103
[PubMed]
25.
Lin
J.
,
Chen
Y.
,
Tang
W.F.
et al.
(
2019
)
PPARG rs3856806 C>T Polymorphism Increased the Risk of Colorectal Cancer: A Case-Control Study in Eastern Chinese Han Population
.
Front. Oncol.
9
,
63
PMC6389672
[PubMed]
26.
Jiang
J.
,
Tang
W.
,
Liu
C.
et al.
(
2018
)
Association between ICOS polymorphisms and risk of colorectal cancer: a case-control study involving 2,606 subjects
.
Int. J. Clin. Exp. Pathol.
11
,
2822
2830
PMC6958277
[PubMed]
27.
Lin
J.
,
Xie
Z.
,
Lan
B.
et al.
(
2020
)
Investigation of Leptin and its receptor (LEPR) for single nucleotide polymorphisms in colorectal cancer: a case-control study involving 2,306 subjects
.
Am. J. Transl. Res.
12
,
3613
3628
PMC7407677
[PubMed]
28.
Higgins
J.P.
,
Thompson
S.G.
,
Deeks
J.J.
et al.
(
2003
)
Measuring inconsistency in meta-analyses
.
BMJ
327
,
557
560
PMC192859
[PubMed]
29.
DerSimonian
R.
and
Laird
N.
(
1986
)
Meta-analysis in clinical trials
.
Control. Clin. Trials
7
,
177
188
[PubMed]
30.
Mantel
N.
and
Haenszel
W.
(
1959
)
Statistical aspects of the analysis of data from retrospective studies of disease
.
J. Natl. Cancer Inst.
22
,
719
748
[PubMed]
31.
Tang
W.
,
Qiu
H.
,
Ding
H.
et al.
(
2013
)
Association between the STK15 F31I polymorphism and cancer susceptibility: a meta-analysis involving 43,626 subjects
.
PLoS ONE
8
,
e82790
PMC3862673
[PubMed]
32.
Min
K.T.
,
Kim
J.W.
,
Jeon
Y.J.
et al.
(
2012
)
Association of the miR-146aC>G, 149C>T, 196a2C>T, and 499A>G polymorphisms with colorectal cancer in the Korean population
.
Mol. Carcinog.
51
,
E65
E73
[PubMed]
33.
Ma
L.
,
Zhu
L.
,
Gu
D.
et al.
(
2013
)
A genetic variant in miR-146a modifies colorectal cancer susceptibility in a Chinese population
.
Arch. Toxicol.
87
,
825
833
[PubMed]
34.
Chayeb
V.
,
Mahjoub
S.
,
Zitouni
H.
et al.
(
2018
)
Contribution of microRNA-149, microRNA-146a, and microRNA-196a2 SNPs in colorectal cancer risk and clinicopathological features in Tunisia
.
Gene
666
,
100
107
[PubMed]
35.
Hezova
R.
,
Kovarikova
A.
,
Bienertova-Vasku
J.
et al.
(
2012
)
Evaluation of SNPs in miR-196-a2, miR-27a and miR-146a as risk factors of colorectal cancer
.
World J. Gastroenterol.
18
,
2827
2831
PMC3374987
[PubMed]
36.
Vinci
S.
,
Gelmini
S.
,
Mancini
I.
et al.
(
2013
)
Genetic and epigenetic factors in regulation of microRNA in colorectal cancers
.
Methods
59
,
138
146
[PubMed]
37.
Kupcinskas
J.
,
Bruzaite
I.
,
Juzenas
S.
et al.
(
2014
)
Lack of association between miR-27a, miR-146a, miR-196a-2, miR-492 and miR-608 gene polymorphisms and colorectal cancer
.
Sci. Rep.
4
,
5993
PMC4125984
[PubMed]
38.
Mao
Y.
,
Li
Y.
,
Jing
F.
et al.
(
2014
)
Association of a genetic variant in microRNA-146a with risk of colorectal cancer: a population-based case-control study
.
Tumour Biol.: J. Int. Soc. Oncodevelopmental Biol. Med.
35
,
6961
6967
[PubMed]
39.
Hu
X.
,
Li
L.
,
Shang
M.
et al.
(
2014
)
Association between microRNA genetic variants and susceptibility to colorectal cancer in Chinese population
.
Tumour Biol.: J. Int. Soc. Oncodevelopment. Biol. Med.
35
,
2151
2156
[PubMed]
40.
Ying
H.Q.
,
Peng
H.X.
,
He
B.S.
et al.
(
2016
)
MiR-608, pre-miR-124-1 and pre-miR26a-1 polymorphisms modify susceptibility and recurrence-free survival in surgically resected CRC individuals
.
Oncotarget
7
,
75865
75873
,
PubMed Central PMCID: PMC5342784
[PubMed]
41.
Gao
X.
,
Zhu
Z.
and
Zhang
S.
(
2018
)
miR-146a rs2910164 polymorphism and the risk of colorectal cancer in Chinese population
.
J. Cancer Res. Therapeutics
14
,
S97
S99
[PubMed]
42.
Zhang
S.
,
Jiang
J.
,
Tang
W.
et al.
(
2020
)
Methylenetetrahydrofolate reductase C677T (Ala>Val, rs1801133 C>T) polymorphism decreases the susceptibility of hepatocellular carcinoma: a meta-analysis involving 12,628 subjects
.
Biosci. Rep.
40
,
BSR20194229
PMC7033308
43.
Yang
J.
,
Zhong
Z.
,
Tang
W.
et al.
(
2019
)
Leptin rs2167270 G > A (G19A) polymorphism may decrease the risk of cancer: A case-control study and meta-analysis involving 19 989 subjects
.
J. Cell. Biochem.
120
,
10998
11007
PMC6590124
44.
Lindor
N.M.
,
Larson
M.C.
,
DeRycke
M.S.
et al.
(
2017
)
Germline miRNA DNA variants and the risk of colorectal cancer by subtype
.
Genes Chromosomes Cancer
56
,
177
184
PMC5245119
45.
Park
R.
,
Lopes
L.
and
Saeed
A.
(
2020
)
Association Between the Functional miR-146a SNP rs2910164 and Risk of Digestive System Cancer: Updated Meta-analysis
.
Anticancer Res.
40
,
1495
1502
[PubMed]

Author notes

*

These authors contributed equally to this work.

This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

Supplementary data