Relationship of microRNA locus with type 2 diabetes mellitus: a case–control study

in Endocrine Connections
Authors:
Qiuyu Huang Department of Cardiovascular Surgery, Union Hospital, Fujian Medical University, Fuzhou, Fujian Province, China
Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, Fujian Province, China

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Hanshen Chen Department of Anesthesiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, China

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Fan Xu Department of Cardiovascular Surgery, Union Hospital, Fujian Medical University, Fuzhou, Fujian Province, China
Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, Fujian Province, China

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Chao Liu Department of Cardiothoracic Surgery, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, Jiangsu Province, China

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Yafeng Wang Department of Cardiology, The People’s Hospital of Xishuangbanna Dai Autonomous Prefecture, Jinghong, Yunnan Province, China

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Weifeng Tang Department of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Nanjing University Medical School, Jiangsu Province, China

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Liangwan Chen Department of Cardiovascular Surgery, Union Hospital, Fujian Medical University, Fuzhou, Fujian Province, China
Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, Fujian Province, China

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Correspondence should be addressed to W Tang or L Chen: twf001001@126.com or chenliangwan@tom.com

*(Q Huang and H Chen contributed equally to this work)

DOI:
https://doi.org/10.1530/EC-21-0261
Volume/Issue:
Volume 10: Issue 11
Page Range:
1393–1402
Article Type:
Research Article
Online Publication Date:
03 Nov 2021
Copyright:
© The authors 2021
Open access

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Type 2 diabetes mellitus (T2DM) is considered as a metabolic disease with hyperglycemia. Accumulating investigations have explored the important role of hereditary factors for T2DM occurrence. Some functional microRNA (miR) polymorphisms may affect their interactions with target mRNAs and result in an aberrant expression. Thus, miR variants might be considered as a biomarker of the susceptibility of T2DM. In this study, we recruited 502 T2DM cases and 782 healthy subjects. We selected miR-146a rs2910164 C>G, miR-196a2 rs11614913 T>C and miR-499 rs3746444 A>G loci and carried out an investigation to identify whether these miR loci could influence T2DM occurrence. In this investigation, a Bonferroni correction was harnessed. After adjustment, we found that rs2910164 SNP was a protective factor for T2DM (GG vs CC/CG: adjusted P = 0.010), especially in never drinking (GG vs CC/CG: adjusted P = 0.001) and BMI ≥24 kg/m2 (GG vs CC/CG: adjusted P = 0.002) subgroups. We also identified that rs11614913 SNP was a protective factor for T2DM in smoking subjects (CC/TC vs TT: adjusted P = 0.002). When we analyzed an interaction of SNP–SNP with the susceptibility tof T2DM, rs11614913/rs3746444, rs2910164/rs3746444 and rs11614913/rs2910164 combinations were not associated with the risk of T2DM. In summary, this study highlights that rs2910164 SNP decreases the susceptibility of T2DM, especially in BMI ≥24 kg/m2 and never drinking subgroups. In addition, we also identify that rs11614913 C allele decreases the susceptibility of T2DM significantly in smoking subgroup.

Abstract

Type 2 diabetes mellitus (T2DM) is considered as a metabolic disease with hyperglycemia. Accumulating investigations have explored the important role of hereditary factors for T2DM occurrence. Some functional microRNA (miR) polymorphisms may affect their interactions with target mRNAs and result in an aberrant expression. Thus, miR variants might be considered as a biomarker of the susceptibility of T2DM. In this study, we recruited 502 T2DM cases and 782 healthy subjects. We selected miR-146a rs2910164 C>G, miR-196a2 rs11614913 T>C and miR-499 rs3746444 A>G loci and carried out an investigation to identify whether these miR loci could influence T2DM occurrence. In this investigation, a Bonferroni correction was harnessed. After adjustment, we found that rs2910164 SNP was a protective factor for T2DM (GG vs CC/CG: adjusted P = 0.010), especially in never drinking (GG vs CC/CG: adjusted P = 0.001) and BMI ≥24 kg/m2 (GG vs CC/CG: adjusted P = 0.002) subgroups. We also identified that rs11614913 SNP was a protective factor for T2DM in smoking subjects (CC/TC vs TT: adjusted P = 0.002). When we analyzed an interaction of SNP–SNP with the susceptibility tof T2DM, rs11614913/rs3746444, rs2910164/rs3746444 and rs11614913/rs2910164 combinations were not associated with the risk of T2DM. In summary, this study highlights that rs2910164 SNP decreases the susceptibility of T2DM, especially in BMI ≥24 kg/m2 and never drinking subgroups. In addition, we also identify that rs11614913 C allele decreases the susceptibility of T2DM significantly in smoking subgroup.

Keywords: polymorphism; type 2 diabetes mellitus; risk; microRNA

Introduction

Type 2 diabetes mellitus (T2DM) is considered as a metabolic disease with hyperglycemia and vascular and/or nervous complications. In China, the reported prevalence of T2DM is 11.6% (1), which appears to have increased in the past decades (2). Latest investigation suggested that healthy lifestyle in terms of reasonably restricted calorie intake and adequate physical exercise was associated with lower incidence rate and older age of onset in T2DM (3). However, the etiology of T2DM was not fully understood. The important risk factors, calorie abundant diet, inadequate physical activities, overweight and obesity, may lead to this phenomenon (4, 5). Recently, some investigations also suggested genetic components could be implicated in development of T2DM (3, 6). Accumulating case–control studies have focused on the important role of hereditary factors in the occurrence of T2DM, and individual’s genotype variants deserve a close look.

MicroRNA (miR) is a kind of non-coding RNA, which is composed of about 22 nucleotides (7, 8). On the post-translational process, miRs could regulate the expression of target genes (9). miRs are involved in a lot of complex diseases, such as T2DM, which seem to play a role in the development of inflammation (10), fat deposition (11), pancreatic β-cell apoptosis (12) and so on. In addition, more and more studies focused on functional importance of miRs in the process of proliferation of smooth muscle cells, oxidative stress and energy metabolism (13, 14, 15, 16, 17, 18), which are correlated with T2DM and its complications.

SNPs are located at pre-miR genes. Some functional miR-SNPs may affect the interaction with mRNA of their target genes and result in an aberrant expression (19). Thus, miR-SNP might be considered as a useful biomarker of the susceptibility of T2DM. A previous study suggested that miR-146a influenced the development of cancer cell by facilitating migration and invasion (20), while miR-146a rs2910164 C>G SNP could promote the expression of miR-146a and results in immune suppression (21, 22, 23). Liu et al. reported that mimics of miR-146a could decrease the peripheral neuropathy of T2DM mice (24). This miR-146a SNP might be implicated in the susceptibility and development of T2DM. There are several studies on the correlation of rs2910164 SNP with risk of T2DM (25, 26, 27, 28). Recently, a meta-analysis has suggested that rs2910164 G allele might be associated with risk of T2DM. However, only 4 case–control studies with 2069 cases and 1950 controls were included (29), and the observations might be underpowered. Additionally, the relationship of miR-196a2 rs11614913 T>C and miR-499 rs3746444 A>G SNPs with the risk of T2DM was also explored. The included participants were more limited, and the findings were more conflicting. In 2020, Gholami et al. systematically reviewed the SNPs associated with T2DM (30). In view of the potential effect of miR-SNPs in the occurrence of T2DM, we selected rs11614913, rs2910164 and rs3746444 loci and carried out an investigation to identify whether these miR-SNPs could influence the risk of T2DM.

Materials and methods

Study population and ethical approval

In this study, 502 T2DM cases who had presented to the Zhenjiang No. 1 hospital (Zhenjiang City, China) and Union Hospital of Fujian province (Fuzhou City, China) between October 2014 and May 2016 consecutively were recruited. And at the same time, 782 healthy subjects were enrolled as controls. Consent has been obtained from each patient or subject after full explanation of the purpose and nature of all procedures used. The criterion of the included T2DM patients was diagnosed according to the criterion of WHO 1999 guidelines (31). The criteria of healthy subjects were (1) normoglycemia (fasting plasma glucose (FPG) <6.1 mmol/L), (2) postprandial plasma glucose <7.8 mmol/L and (3) no history of diabetes mellitus (32). Two authors visited each subject and collected risk factors and demographic variables. We summarized the information in Table 1. Weight, height and blood pressure were measured. In Chinese adults, a BMI ≥24 kg/m2 was regarded as the criterion for overweight and obesity (33, 34). Serum HDL-C, FPG, total cholesterol, LDL-C and triglycerides were measured. The protocol was approved by Committee of Ethics at Fujian Medical University.

Table 1

Distribution of selected demographic variables and risk factors in T2DM cases and controls.
Variable Cases (n =502) Controls (n =782) Pa
n % n %
Age (years) 65.20 (±9.51) 64.67 (±9.80) 0.347
Age (years) 0.113
 <65 227 45.22 389 49.74
 ≥65 275 54.78 393 50.26
Sex 0.819
 Male 332 66.14 522 66.75
 Female 170 33.86 260 33.25
Alcohol use 0.263
 Never 453 90.24 690 88.24
 Ever 49 9.76 92 11.76
Smoking status 0.264
 Never 333 66.33 542 69.31
 Ever 169 33.67 240 30.69
Height (m) 1.68 (±0.08) 1.66 (±0.07) 0.015
Weight (kg) 67.63 (±11.42) 64.62 (±9.96) <0.001
BMI (kg/m2) 24.95 (±3.64) 23.51 (±2.94) <0.001
BMI (kg/m2) <0.001
 <24 210 436
 ≥24 292 346
FPG (mmol/L) 8.08 (±2.76) 5.13 (±0.49) <0.001
Total cholesterol (mmol/L) 4.61 (±1.24) 4.88 (±1.02) <0.001
Triglyceride (mmol/L) 1.74 (±1.14) 1.55 (±0.96) 0.001
HDL-C (mmol/L) 1.13 (±0.37) 1.30 (±0.37) <0.001
LDL-C (mmol/L) 3.00 (±1.07) 3.14 (±0.82) 0.010
Systolic pressure (mmHg) 135 (±18) 134 (±18) 0.297
Diastolic pressure (mmHg) 80 (±10) 80 (±10) 0.649

aTwo-sided χ2 test and Student's t test; bold values are statistically significant (P < 0.05).

FPG, fasting plasma glucose.

SNP selection

To determine the potential relationship between miR-SNPs and T2DM, we selected the miR-146a rs2910164 C>G, miR-196a2 rs11614913 T>C and miR-499 rs3746444 A>G loci polymorphism according to the literature, which was significantly associated with cancer (20, 21, 22, 23), coronary artery disease (35, 36) and type 2 diabetes (25, 26, 27, 28, 30), in some studies.

DNA extraction and genotyping

EDTA anticoagulant vacutainer tube (BD, Franklin Lakes NJ, USA) was used to collect the blood sample. Genomic DNA was extracted by using the DNA Purification Kit (Promega). SNPscanTM genotyping assay was carried out to identify the genotypes of rs3746444, rs11614913 and rs2910164. As summarized in Table 2, more than 99% sample was successfully genotyped. The genotypes of rs3746444, rs11614913 and rs2910164 SNP were confirmed with DNA sequence method in 4% DNA samples randomly.

Table 2

Primary information for miR-146a rs2910164 C>G, miR-196a2 rs11614913 T>C and miR-499 rs3746444 A>G polymorphisms.
Genotyped SNPs miR-146a rs2910164 C>G miR-196a2 rs11614913 T>C miR-499 rs3746444 A>G
Chromosome 5 12 20
Function nc-transcript-variant nc-transcript-variant nc-transcript-variant
Chr Pos (NCBI Build 38) 160485411 53991815 3499048
MAF for Chinese in database 0.35 0.34 0.15
MAF in our controls (n = 1109) 0.38 0.44 0.15
P value for HWE test in our controls 0.119 0.034 0.702
Genotyping method SNPscan SNPscan SNPscan
% Genotyping value 99.61% 99.61% 99.61%

HWE, Hardy–Weinberg equilibrium; MAF, minor allele frequency.

Statistical analysis

SAS 9.4 software (SAS Institute, Cary, NC, USA) was harnessed to perform statistical analyses. Mean ± s.d. was used to express continuous variables. The difference of continuous variables between two groups was measured by Student’s t test. The differencces categorical variables (smoking status, alcohol consumption, BMI, genotypes, sex and age) were measured using chi-square test (χ2). Using the genotype number in controls of rs3746444, rs11614913 and rs2910164, Hardy–Weinberg equilibrium (HWE) was evaluated by an internet-based calculator (37, 38, 39, 40, 41). The correlations of rs3746444, rs11614913 and rs2910164 polymorphisms with occurrence of T2DM were determined by odds ratios (ORs) and CIs. A P < 0.05 (two-tailed) was regarded as the statistical significance. In this case–control study, a Bonferroni correction was harnessed to confirm our findings (42, 43).

Results

Baseline characteristics

Table 1 summarizes the demographics and risk factors, anthropometric data, biochemistry characteristics. Gender, age, smoking status and alcohol consumption were well matched. Compared with controls, the mean level of BMI was higher in the T2DM group (P < 0.05). In this study, the mean level of diastolic pressure and systolic pressure was similar. Other information, HDL-C, FPG, total cholesterol, LDL-C and triglycerides, were also summarized in Table 1. Table 2 shows the primary SNPs information of rs3746444, rs11614913 and rs2910164. Minor allele frequency is summarized in Table 2.

Association of rs3746444, rs11614913 and rs2910164 loci with T2DM

The rs3746444, rs11614913 and rs2910164 genotype distributions are shown in Table 3. The distributions of the rs3746444 and rs2910164 genotypes were suggested to be in HWE. In an analysis of miR-146a rs2910164 SNP, compared with miR-146a rs2910164 CC or CC/CG genotype, the frequency distribution of miR-146a rs2910164 GG genotype was different between healthy controls and T2DM cases (GG vs CC: crude P = 0.046 and GG vs CG/CC: crude P = 0.006). After a logistic regression analysis, it is also suggested that miR-146a rs2910164 SNP decreased the susceptibility of T2DM (GG vs CC: adjusted P = 0.036 and GG vs CG/CC: adjusted P = 0.004). However, miR-196a2 rs11614913 and miR-499 rs3746444 SNPs were not correlated with the occurrence of T2DM.

Table 3

The relationship of miRNA polymorphisms with T2DM.
Genotype Cases (n =502) Controls (n =782) Crude OR (95% CI) P Adjusted ORa (95% CI) P
n % n %
miR-499 rs3746444
 AA 363 73.04 565 72.25 1.00 1.00
 AG 125 25.15 201 25.70 0.97 (0.75–1.25) 0.806 0.95 (0.73–1.23) 0.689
 GG 9 1.81 16 2.05 0.88 (0.38–2.00) 0.753 0.89 (0.39–2.06) 0.788
 AG+GG 134 26.96 217 27.75 0.96 (0.75–1.24) 0.759 0.94 (0.73–1.22) 0.657
 AA+AG 488 98.19 766 97.95 1.00 1.00
 GG 9 1.81 16 2.05 0.88 (0.39–2.01) 0.767 0.90 (0.39–2.08) 0.813
 G allele 143 14.39 233 14.90
miR-146a rs2910164
 CC 187 37.63 308 39.39 1.00 1.00
 CG 258 51.91 349 44.63 1.22 (0.96–1.55) 0.112 1.22 (0.96–1.56) 0.110
 GG 52 10.46 125 15.98 0.69 (0.47–0.99) 0.046 0.67 (0.46–0.97) 0.036
 CG+GG 310 62.37 474 60.61 1.07 (0.86–1.36) 0.530 1.07 (0.85–1.36) 0.548
 CC+CG 445 89.54 657 84.01 1.00 1.00
 GG 52 10.46 125 15.98 0.61 (0.44–0.87) 0.006 0.60 (0.42–0.85) 0.004
 G allele 362 36.42 599 38.30
miR-196a2 rs11614913
 TT 165 33.20 229 29.28 1.00 1.00
 TC 251 50.50 415 53.07 0.84 (0.65–1.08) 0.177 0.83 (0.64–1.07) 0.151
 CC 81 16.30 138 17.65 0.82 (0.58–1.14) 0.237 0.83 (0.59–1.18) 0.301
 TC+CC 332 66.80 553 70.72 0.83 (0.65–1.06) 0.140 0.83 (0.65–1.06) 0.135
 TT+TC 416 83.70 644 82.35 1.00 1.00
 CC 81 16.30 138 17.65 0.91 (0.67–1.23) 0.533 0.94 (0.69–1.27) 0.682
 C allele 413 41.55 691 44.18

Bold values are statistically significant (P < 0.05).

aAdjusted for age, sex, smoking, status of BMI and drinking.

Subgroup analysis of the association of miR-SNPs with T2DM

miR-196a2 rs11614913 genotypes in the stratified analysis are shown in Table 4. The subgroup analysis was conducted according to the included risk factors. In ≥65 years subgroup, after adjustment for smoking status, gender, alcohol use and BMI, rs11614913 TC and CC/TC genotypes might decrease T2DM susceptibility compared with rs11614913 TT genotype (TC vs TT: adjusted P = 0.013 and CC/TC vs TT: adjusted P = 0.011). Compared with rs11614913 TT genotype, rs11614913 TC, CC and CC/TC genotypes decreased the susceptibility of T2DM significantly in smoking subgroup (TC vs TT: adjusted P = 0.005, CC vs TT: adjusted P = 0.018 and CC/TC vs TT: adjusted P = 0.002). In drinking subgroup, compared with rs11614913 TT genotype, rs11614913 TC and CC/TC genotypes decreased the susceptibility of T2DM significantly (TC vs TT: adjusted P = 0.007 and CC/TC vs TT: adjusted P = 0.020). In BMI <24 kg/m2 subgroup, compared with rs11614913 TT genotype, rs11614913 CC and CC/TC genotypes decreased the susceptibility of T2DM significantly (CC vs TT: adjusted P = 0.013 and CC/TC vs TT: adjusted P = 0.030).

Table 4

Stratified analyses between miR-196a2 rs11614913 T>C polymorphism and T2DM risk by sex, age, BMI, smoking status and alcohol consumption.
Variable miR-196a2 rs11614913 T>C (case/control)a Adjusted ORb (95% CI); P
TT TC CC TC vs TT CC vs TT CC/TC vs TT CC vs TT/TC
Sex
 Male 115/161 158/275 56/86 0.78 (0.57–1.07); P = 0.117 0.92 (0.60–1.39); P = 0.678 0.81 (0.60–1.09); P = 0.165 1.07 (0.73–1.55); P =  0.742
 Female 50/68 93/140 25/52 0.90 (0.57–1.42); P =  0.650 0.70 (0.38–1.29); P = 0.254 0.85 (0.55–1.32); P = 0.461 0.75 (0.44–1.28); P = 0.292
Age
 <65 66/121 118/197 40/71 1.12 (0.76–1.64); P = 0.571 1.12 (0.68–1.84); P = 0.661 1.12 (0.78–1.61); P = 0.551 1.04 (0.67–1.61); P = 0.851
 ≥65 99/108 133/218 41/67 0.64 (0.45–0.91); P = 0.013 0.66 (0.41–1.07); P = 0.091 0.65 (0.46–0.90); P = 0.011 0.87 (0.57–1.34); P = 0.528
Smoking status
 Never 99/166 173/280 59/96 1.03 (0.75–1.41); P = 0.855 1.07 (0.71–1.62); P = 0.743 1.04 (0.77–1.41); P = 0.797 1.05 (0.73–1.51); P = 0.784
 Ever 66/63 78/135 22/42 0.52 (0.33–0.81); P =  0.005 0.46 (0.24–0.88); P =  0.018 0.50 (0.32–0.78); P =  0.002 0.70 (0.39–1.24); P =  0.216
Alcohol consumption
 Never 145/207 234/361 70/122 0.91 (0.69–1.19); P =  0.489 0.83 (0.57–1.19); P =  0.310 0.89 (0.68–1.15); P =  0.371 0.88 (0.63–1.22); P = 0.434
 Ever 20/22 17/54 11/16 0.32 (0.14–0.73); P = 0.007 0.70 (0.26–1.90); P = 0.481 0.40 (0.19–0.87); P =  0.020 1.39 (0.58–3.35); P =  0.465
BMI (kg/m2)
 <24 75/123 104/225 29/88 0.73 (0.50–1.06); P = 0.101 0.52 (0.31–0.87); P= 0.013 0.67 (0.47–0.96); P = 0.030 0.63 (0.40–1.00); P = 0.052
 ≥24 90/106 147/190 52/50 0.89 (0.63–1.28); P = 0.534 1.20 (0.74–1.94); P =  0.467 0.96 (0.68–1.35); P =  0.797 1.29 (0.84–1.97); P =  0.250

aFor miR-196a2 rs11614913 T>C, the genotyping was successful in 497 (99.00%) T2DM cases and 782 (100.00%) controls; bAdjusted for age, sex, smoking, drinking, BMI, hypertension, T2DM and hyperlipidemia (besides stratified factors accordingly) in a multiple logistic regression model.

Bold values are statistically significant (P < 0.05).

In the stratified analysis, miR-146a rs2910164 genotypes are shown in Table 5. In female subgroup, compared with rs2910164 CC and CC/CG genotypes, rs2910164 GG genotypes might decrease T2DM susceptibility (GG vs CC: adjusted P = 0.019 and GG vs CC/CG: adjusted P = 0.010). In ≥65 years subgroup, rs2910164 GG genotypes might decrease T2DM susceptibility compared with rs2910164 CC and CC/CG (GG vs CC: adjusted P = 0.049 and GG vs CC/CG: adjusted P = 0.020). Compared with rs2910164 CC and CC/CG genotypes, rs2910164 GG genotypes decreased the susceptibility of T2DM significantly in individuals 7,without tobacco consumption (GG vs CC: adjusted P = 0.024 and GG vs CC/CG: adjusted P = 0.004). In non-drinking subgroup, compared with rs2910164 CC and CC/CG genotypes, rs2910164 GG genotype decreased the susceptibility of T2DM significantly (GG vs CC: adjusted P = 0.014 and GG vs CC/CG: adjusted P = 0.001). In obesity/overweight subgroup, compared with rs2910164 CC and CC/CG genotypes, rs2910164 GG genotype decreased the susceptibility of T2DM significantly (GG vs CC: adjusted P = 0.045 and GG vs CC/CG: adjusted P = 0.002). However, compared with rs2910164 CC genotype, rs2910164 CG genotype might increase the susceptibility of T2DM significantly in obesity/overweight subgroup (CG vs CC: adjusted P = 0.028).

Table 5

Stratified analyses between miR-146a rs2910164 C>G polymorphism and T2DM risk by sex, age, BMI, smoking status and alcohol consumption.
Variable miR-146a rs2910164 C>G (case/control)a Adjusted ORb (95% CI); P
CC CG GG CG vs CC GG vs CC GG/CG vs CC GG vs CC/CG
Sex
 Male 118/208 174/235 37/79 1.34 (0.99–1.82); P =  0.057 0.84 (0.53–1.33); P =  0.466 1.22 (0.91–1.63); P =  0.185 0.71 (0.47–1.09); P =  0.119
 Female 69/100 84/114 15/46 1.04 (0.68–1.59); P =  0.874 0.45 (0.23–0.88); P =  0.019 0.87 (0.58–1.30); P =  0.484 0.44 (0.23–0.82); P =  0.010
Age
 <65 83/164 118/166 23/59 1.35 (0.94–1.94); P =  0.104 0.76 (0.44–1.33); P =  0.339 1.20 (0.85–1.69); P =  0.307 0.65 (0.38–1.09); P =  0.099
 ≥65 104/144 140/183 29/66 1.09 (0.77–1.53); P =  0.631 0.60 (0.36–1.00); P =  0.049 0.95 (0.69–1.32); P =  0.775 0.57 (0.36–0.92);P =  0.020
Smoking status
 Never 127/213 171/238 33/91 1.18 (0.88–1.59); P =  0.273 0.59 (0.37–0.93); P =  0.024 1.02 (0.77–1.35); P =  0.909 0.54 (0.35–0.82); P =  0.004
 Ever 60/95 87/111 19/34 1.38 (0.88–2.14); P =  0.159 0.94 (0.48–1.83); P =  0.856 1.27 (0.83–1.94); P =  0.267 0.79 (0.43–1.45); P =  0.441
Alcohol consumption
 Never 172/277 233/300 44/113 1.23 (0.95–1.60); P =  0.114 0.60 (0.40–0.90); P =  0.014 1.06 (0.83–1.36); P =  0.650 0.54 (0.37–0.78); P =  0.001
 Ever 15/31 25/49 8/12 1.01 (0.45–2.24); P =  0.985 1.32 (0.44–3.95); P =  0.619 1.07 (0.50–2.29); P =  0.859 1.31 (0.49–3.50); P =  0.585
BMI (kg/m2)
 <24 82/170 102/205 24/61 1.01 (0.70–1.44); P =  0.969 0.81 (0.47–1.40); P =  0.452 0.96 (0.68–1.35); P =  0.825 0.81 (0.49–1.34); P =  0.411
 ≥24 105/138 156/144 28/64 1.47 (1.04–2.08); P =  0.028 0.59 (0.35–0.99); P =  0.045 1.20 (0.87–1.66); P =  0.273 0.48 (0.30–0.77); P =  0.002

aFor miR-146a rs2910164 C>G, the genotyping was successful in 497 (99.00%) T2DM cases, and 782 (100.00%) controls; bAdjusted for age, sex, smoking, drinking and BMI (besides stratified factors accordingly) in a multiple logistic regression model.

Bold values are statistically significant (P < 0.05).

When we focused on the association of the miR-499 rs3746444 SNP with the risk of T2DM, null association was found (Table 6).

Table 6

Stratified analyses between miR-499 rs3746444 A>G polymorphism and T2DM risk by sex, age, BMI, smoking status and alcohol consumption.
Variable miR-499 rs3746444 A>G (case/control)a Adjusted ORb (95% CI); P
AA AG GG AG vs AA GG vs AA GG/AG vs AA GG vs AA/AG
Sex
 Male 245/376 78/134 6/12 0.88 (0.63–1.22); P =  0.427 0.77 (0.28–2.10); P =  0.605 0.87 (0.63–1.19); P =  0.378 0.79 (0.29–2.16); P =  0.650
 Female 118/189 47/67 3/4 1.03 (0.66–1.62); P =  0.891 1.19 (0.26–5.50); P =  0.825 1.04 (0.67–1.61); P =  0.860 1.18 (0.26–5.43); P =  0.833
Age
 <65 167/290 53/93 4/6 0.92 (0.62–1.37); P =  0.682 1.10 (0.30–4.05); P =  0.888 0.93 (0.63–1.37); P =  0.718 1.12 (0.31–4.12); P =  0.864
 ≥65 196/275 72/108 5/10 0.94 (0.66–1.34); P =  0.726 0.79 (0.26–2.36); P =  0.669 0.93 (0.66–1.31); P =  0.664 0.80 (0.27–2.39); P =  0.691
Smoking status
 Never 240/398 85/132 6/12 1.03 (0.75–1.41); P =  0.875 0.83 (0.31–2.26); P =  0.716 1.01 (0.74–1.38); P =  0.949 0.83 (0.30–2.24); P =  0.706
 Ever 123/167 40/69 3/4 0.75 (0.47–1.20); P =  0.233 0.88 (0.19–4.15); P =  0.866 0.76 (0.48–1.20); P =  0.238 0.94 (0.20–4.44); P =  0.940
Alcohol consumption
 Never 325/504 116/171 8/15 1.03 (0.78–1.36); P = 0.857 0.87 (0.36–2.09); P = 0.752 1.01 (0.77–1.33); P = 0.922 0.86 (0.36–2.07); P = 0.740
 Ever 38/61 9/30 1/1 0.47 (0.20–1.11); P = 0.084 2.17 (0.12–40.78); P = 0.606 0.51 (0.22–1.17); P = 0.113 2.60 (0.14–48.65); P = 0.523
BMI (kg/m2)
 <24 153/317 53/107 2/12 0.99 (0.67–1.45); P = 0.948 0.33 (0.07–1.50); P = 0.152 0.92 (0.63–1.34); P = 0.669 0.33 (0.07–1.50); P = 0.152
 ≥24 210/248 72/94 7/4 0.91 (0.63–1.30); P = 0.592 2.18 (0.62–7.73); P = 0.227 0.96 (0.67–1.36); P = 0.801 2.24 (0.63–7.91); P = 0.211

aFor miR-499 rs3746444 A>G, the genotyping was successful in 497 (99.00%) T2DM cases and 782 (100.00%) controls; bAdjusted for age, sex, smoking, drinking and BMI (besides stratified factors accordingly) in a multiple logistic regression model.

Bonferroni correction

A Bonferroni correction was harnessed to confirm our findings in this study. After adjustment, we found that miR-146a rs2910164 SNP was a protective factor for T2DM, especially in non-drinking and BMI ≥24 kg/m2 subgroups. We also identified that miR-196a2 rs11614913 SNP was a protective factor for T2DM in smoking subjects.

Combination analysis of miRNA polymorphisms

miR-SNPs combined analyses were summarized in Table 7. When we analyzed the interactions of SNP–SNP with the susceptibility of T2DM, rs11614913/rs3746444, rs2910164/rs3746444, rs11614913/rs2910164 and rs11614913/rs2910164/rs3746444 combinations were used. However, null association was found.

Table 7

Combination analysis of miRNA loci (rs3746444, rs2910164 and rs11614913) in T2DM cases and controls.
Genotype Case Control OR (95% CI) P value
n % n %
rs11614913/rs2910164
 TT/CC 51 10.26 87 11.13 1.00
 TT/CG 93 18.71 106 13.55 1.50 (0.96–2.33) 0.074
 TT/GG 21 4.23 36 4.60 1.00 (0.52–1.89) 0.988
 TC/CC 105 21.13 165 21.10 1.09 (0.71–1.66) 0.704
 TC/CG 125 25.15 185 23.66 1.15 (0.76–1.74) 0.501
 TC/GG 21 4.23 65 8.31 0.55 (0.30–1.01) 0.051
 CC/CC 31 6.24 56 7.16 0.94 (0.54–1.65) 0.841
 CC/CG 40 8.05 58 7.42 1.18 (0.69–2.00) 0.548
 CC/GG 10 2.01 24 3.07 0.71 (0.31–1.61) 0.410
rs11614913/rs3746444
 TT/AA 119 23.94 159 20.33 1.00
 TT/AG 43 8.65 67 8.57 0.86 (0.55–1.35) 0.504
 TT/GG 3 0.60 3 0.38 1.34 (0.26–6.74) 1.000
 TC/AA 189 38.03 307 39.26 0.82 (0.61–1.11) 0.200
 TC/AG 57 11.47 98 12.53 0.78 (0.52–1.16) 0.221
 TC/GG 5 1.01 10 1.28 0.67 (0.22–2.01) 0.470
 CC/AA 55 11.07 99 12.66 0.74 (0.49–1.11) 0.150
 CC/AG 25 5.03 36 4.60 0.93 (0.53–1.63) 0.794
 CC/GG 1 0.20 3 0.38 0.45 (0.05–4.34) 0.639
rs2910164/rs3746444
 CC/AA 133 26.76 220 28.13 1.00
 CC/AG 48 9.66 82 10.49 0.97 (0.64–1.47) 0.879
 CC/GG 6 1.21 6 0.77 1.65 (0.52–5.24) 0.387
 CG/AA 190 38.23 247 31.59 1.27 (0.96–1.69) 0.099
 CG/AG 65 13.08 98 12.53 1.10 (0.75–1.60) 0.633
 CG/GG 3 0.60 4 0.51 1.24 (0.27–5.63) 1.000
 GG/AA 40 8.05 98 12.53 0.68 (0.44–1.03) 0.070
 GG/AG 12 2.41 21 2.69 0.95 (0.45–1.98) 0.882
 GG/GG 0 0.00 6 0.77 0.13 (0.01–2.28) 0.089
rs11614913/rs2910164/rs3746444
 TT/CC/AA 35 7.04 58 7.42 1.00
 TT/CC/AG 14 2.82 29 3.71 0.80 (0.37–1.72) 0.566
 TT/CC/GG 2 0.40 0 0.00 8.24 (0.38–176.72) 0.149
 TT/CG/AA 70 14.08 76 9.72 1.53 (0.90–2.59) 0.117
 TT/CG/AG 22 4.43 29 3.71 1.26 (0.63–2.52) 0.518
 TT/CG/GG 1 0.20 1 0.13 1.65 (0.10–27.36) 1.000
 TT/GG/AA 14 2.82 25 3.20 0.93 (0.43–2.02) 0.851
 TT/GG/AG 7 1.41 9 1.15 1.29 (0.44–3.77) 0.642
 TT/GG/GG 0 0.00 2 0.26 0.33 (0.02–7.07) 0.530
 TC/CC/AA 78 15.69 125 15.98 1.03 (0.62–1.72) 0.897
 TC/CC/AG 24 4.83 36 4.60 1.10 (0.57–2.15) 0.769
 TC/CC/GG 3 0.60 4 0.51 1.24 (0.26–5.89) 1.000
 TC/CG/AA 93 18.71 128 16.37 1.20 (0.73–1.98) 0.464
 TC/CG/AG 30 6.04 55 7.03 0.90 (0.49–1.67) 0.746
 TC/CG/GG 2 0.40 2 0.26 1.66 (0.22–12.30) 0.635
 TC/GG/AA 18 3.62 54 6.91 0.55 (0.28–1.09) 0.085
 TC/GG/AG 3 0.60 7 0.90 0.71 (0.17–2.93) 0.742
 TC/GG/GG 0 0.00 4 0.51 0.18 (0.01–3.51) 0.293
 CC/CC/AA 20 4.02 37 4.73 0.90 (0.45–1.78) 0.753
 CC/CC/AG 10 2.01 17 2.17 0.97 (0.40–2.37) 0.955
 CC/CC/GG 1 0.20 2 0.26 0.83 (0.07–9.48) 1.000
 CC/CG/AA 27 5.43 43 5.50 1.04 (0.55–1.97) 0.903
 CC/CG/AG 13 2.62 14 1.79 1.54 (0.65–3.65) 0.326
 CC/CG/GG 0 0.00 1 0.13 0.55 (0.02–13.86) 1.000
 CC/GG/AA 8 1.61 19 2.43 0.70 (0.28–1.76) 0.445
 CC/GG/AG 2 0.40 5 0.64 0.66 (0.12–3.60) 1.000
 CC/GG/GG 0 0.00 0 0.00 - -

Power calculation

Using α = 0.05 as the criterion of the Type I error probability (two-sided), we calculated the power value of this study. For miR-146a rs2910164 SNP, the power value was 0.838 in overall comparison, 0.916 in non-drinker subgroup and 0.877 in BMI ≥24 kg/m2 subgroup among the GG vs CG/CC genetic model. For miR-196a2 rs11614913 SNP, the power value was 0.898 in drinker subgroup among the dominant genetic model. These findings suggested that miR-146a rs2910164 and miR-196a2 rs11614913 SNPs could be protective factors of T2DM occurrence.

Discussion

Some investigations have found rs11614913, rs2910164 and rs3746444 loci of miR-SNPs may be potential biomarker for increased incidence of T2DM. However, relationship of the mentioned miR-SNPs with development of T2DM was unclear. This case–control study showed that rs2910164 SNP is associated with decreased susceptibility of T2DM, especially in non-drinking and BMI ≥24 kg/m2 subgroups. In addition, we also found that rs11614913 C allele may be associated with decreased susceptibility of T2DM significantly in smoking subgroup.

Annexin A1 (ANXA1) is found to be an important anti-inflammatory factor. Purvis et al. have reported that ANXA1 level significantly increased in people with hyperglycemia (44). In addition, ANXA1 might be a protective factor for T2DM (45) and imply a new treatment strategy for T2DM (44). Huang et al. have reported that peptide Ac2-26, an ANXA1 N-terminal peptide, promotes wound healing in diabetes (46). Thus, it is suggested that ANXA1 may be implicated in the development of T2DM. A recent study has suggested that miR-196a2 regulates the role of ANXA1(47). rs11614913 T→C variant in miR-196a2 gene locates at the 3p regions in mature miR (48). Several investigations found that rs11614913 C allele increased the expression of miR-196a2 (49, 50). Ghanbari et al. have found that rs11614913 C allele decreases waist to hip ratio (51). A previous pooled analysis suggested that rs11614913 C allele did not influence the susceptibility of T2DM in China (30). However, in this study, we found that rs11614913 C allele decreased the susceptibility of T2DM in smoking subgroup, which might indicate that the role of rs11614913 C allele could be affected by environmental factor. Yin et al. also suggested that an interaction of the rs11614913 genotypes in miR-196a2 gene with environmental factor is associated with an increased susceptibility of lung cancer (52). It might be explained that the correlation of rs11614913 locus with an occurrence of T2DM could be affected by risk factors of environment. Considering a single locus of miR could only make low penetrance effects on T2DM, the interaction of gene–environment factors may dilute the role of rs11614913, and more investigations are needed to support our findings in the future.

Considering that an individual SNP contributes little to the susceptibility of T2DM, the current investigation urges a need of sufficient power to get an assessment of rs2910164 with T2DM risk. Recently, the correlation of rs2910164 with T2DM risk was explored by several case–control studies. A meta-analysis suggested an association of rs2910164 in miR-146a gene with T2DM risk (29). However, in that meta-analysis, only 2069 cases and 1950 controls were included. The observations may be underpowered. Wang et al. suggested that G allele of rs2910164 SNP did not influence the occurrence of T2DM (25), while other investigations reported that G allele of this SNP decreased the risk of T2DM (26, 27, 28). Considering only four publications with moderate sample sizes exploring the correlation of rs2910164 with the occurrence of T2DM in Asians, the observations might be underpowered. In this study, 1284 participants were included to assess a correlation of rs2910164 in miR-146a gene with T2DM risk. We identified that rs2910164 in miR-146a gene decreased the risk of T2DM. In our study, a Bonferroni correction was harnessed to confirm our findings. After adjustment, the correlation of rs2910164 with the occurrence of T2DM also existed in overall comparison and in BMI ≥24 kg/m2 and never drinking subgroups, which was similar to the findings of a more recent meta-analysis (30). In the future, more well-matched investigations are needed to confirm or refute our observations.

There are some limitations in our study. First, selective bias might exist. T2DM patients and normal controls came from two different hospitals, and these subjects could not well represent the Chinese populations. Secondly, only three miR-SNPs were included and studied in our study, more polymorphisms widely investigated should be included by artificial neural networks database. Thirdly, for temporary limitation of T2DM sample size and type, we couldn’t perform genotype-based mRNA expression analysis. However, further investigations with detailed gene–environmental factors and functional exploring are certainly needed (53). Fourthly, in some subgroups, the sample size might be insufficient. Fifth, variant distribution of rs11614913 was suggested to be out of HWE in overall and some subgroups comparisons. The findings should be explained with caution. Finally, this study did not focus on the detailed information on T2DM complications, which might restrict a further evaluation of the mentioned SNPs on prognosis of T2DM.

In summary, this study highlights that miR-146a rs2910164 SNP is associated with decreases in susceptibility of T2DM, especially in BMI ≥24 kg/m2 and non-smoking group. In addition, we also found that miR-196a2 rs11614913 C allele decreases the susceptibility of T2DM significantly in smoking subgroup.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

The project was supported by the Joint Funds for the innovation of Science and Technology, Fujian province, China (grant numbers 2017Y9015); the Educational Commission of Yunnan Province, China (grant number 2017ZDX116).

Acknowledgements

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

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    • Export Citation
  • 40↑

    Tang W, Liu J, Zhong Z, Qiu H & Kang M Association of metabolism-related genes polymorphisms with adenocarcinoma of the oesophagogastric junction: evidence from 2261 subjects. Journal of Cellular Biochemistry 2019 120 18689–18701. (https://doi.org/10.1002/jcb.29167)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41↑

    Tang W, Chen S, Liu J, Liu C, Wang Y & Kang M Investigation of IGF1, IGF2BP2, and IGFBP3 variants with lymph node status and esophagogastric junction adenocarcinoma risk. Journal of Cellular Biochemistry 2019 120 5510–5518. (https://doi.org/10.1002/jcb.27834)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42↑

    Bland JM& Altman DG Multiple significance tests: the Bonferroni method. BMJ 1995 310 170. (https://doi.org/10.1136/bmj.310.6973.170)

  • 43↑

    Lesack K& Naugler C An open-source software program for performing Bonferroni and related corrections for multiple comparisons. Journal of Pathology Informatics 2011 2 52. (https://doi.org/10.4103/2153-3539.91130)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44↑

    Purvis GSD, Collino M, Loiola RA, Baragetti A, Chiazza F, Brovelli M, Sheikh MH, Collotta D, Cento A & Mastrocola R et al.Identification of annexinA1 as an endogenous regulator of RhoA, and its role in the pathophysiology and experimental therapy of Type-2 diabetes. Frontiers in Immunology 2019 10 571. (https://doi.org/10.3389/fimmu.2019.00571)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45↑

    Marshall SA, Qin CX, Jelinic M, O’Sullivan K, Deo M, Walsh J, Li M, Parry LJ, Ritchie RH, Leo CH. The novel small-molecule annexin-A1 mimetic, compound 17b, elicits vasoprotective actions in streptozotocin-induced diabetic mice. International Journal of Molecular Sciences 2020 21 1384. (https://doi.org/10.3390/ijms21041384)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46↑

    Huang JJ, Xia CJ, Wei Y, Yao Y, Dong MW, Lin KZ, Yu LS, Gao Y & Fan YY Annexin A1-derived peptide Ac2-26 facilitates wound healing in diabetic mice. Wound Repair and Regeneration 2020 28 772–779. (https://doi.org/10.1111/wrr.12860)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 47↑

    Luthra R, Singh RR, Luthra MG, Li YX, Hannah C, Romans AM, Barkoh BA, Chen SS, Ensor J & Maru DM et al.MicroRNA-196a targets annexin A1: a microRNA-mediated mechanism of annexin A1 downregulation in cancers. Oncogene 2008 27 6667–6678. (https://doi.org/10.1038/onc.2008.256)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 48↑

    Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A, Pfeffer S, Rice A, Kamphorst AO & Landthaler M et al.A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 2007 129 1401–1414. (https://doi.org/10.1016/j.cell.2007.04.040)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 49↑

    Yin Z, Cui Z, Ren Y, Xia L, Li H & Zhou B MiR-196a2 and lung cancer in Chinese non-smoking females: a genetic association study and expression analysis. Oncotarget 2017 8 70890–70898. (https://doi.org/10.18632/oncotarget.20174)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 50↑

    Zhao H, Xu J, Zhao D, Geng M, Ge H, Fu L & Zhu Z Somatic mutation of the SNP rs11614913 and its association with increased MIR 196A2 expression in breast cancer. DNA and Cell Biology 2016 35 81–87. (https://doi.org/10.1089/dna.2014.2785)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 51↑

    Ghanbari M, Sedaghat S, de Looper HW, Hofman A, Erkeland SJ, Franco OH & Dehghan A The association of common polymorphisms in miR-196a2 with waist to hip ratio and miR-1908 with serum lipid and glucose. Obesity 2015 23 495–503. (https://doi.org/10.1002/oby.20975)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 52↑

    Yin Z, Cui Z, Guan P, Li X, Wu W, Ren Y, He Q & Zhou B Interaction between polymorphisms in pre-MiRNA genes and cooking oil fume exposure on the risk of lung cancer in Chinese non-smoking female population. PLoS ONE 2015 10 e0128572. (https://doi.org/10.1371/journal.pone.0128572)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 53↑

    Zhuo ZJ, Liu W, Zhang J, Zhu J, Zhang R, Tang J, Yang T, Zou Y, He J & Xia H Functional polymorphisms at ERCC1/XPF genes confer neuroblastoma risk in Chinese children. EBiomedicine 2018 30 113–119. (https://doi.org/10.1016/j.ebiom.2018.03.003)

    • PubMed
    • Search Google Scholar
    • Export Citation

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Online ISSN:
2049-3614
Copyright:
© The authors 2021
Page(s):
10
Article Type:
Research Article
Received Date:
30 Aug 2021
Accepted Date:
30 Sep 2021
Online Publication Date:
03 Nov 2021
Keywords:
polymorphism; type 2 diabetes mellitus; risk; microRNA
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    Tang W, Liu J, Zhong Z, Qiu H & Kang M Association of metabolism-related genes polymorphisms with adenocarcinoma of the oesophagogastric junction: evidence from 2261 subjects. Journal of Cellular Biochemistry 2019 120 18689–18701. (https://doi.org/10.1002/jcb.29167)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41

    Tang W, Chen S, Liu J, Liu C, Wang Y & Kang M Investigation of IGF1, IGF2BP2, and IGFBP3 variants with lymph node status and esophagogastric junction adenocarcinoma risk. Journal of Cellular Biochemistry 2019 120 5510–5518. (https://doi.org/10.1002/jcb.27834)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42

    Bland JM& Altman DG Multiple significance tests: the Bonferroni method. BMJ 1995 310 170. (https://doi.org/10.1136/bmj.310.6973.170)

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    Lesack K& Naugler C An open-source software program for performing Bonferroni and related corrections for multiple comparisons. Journal of Pathology Informatics 2011 2 52. (https://doi.org/10.4103/2153-3539.91130)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44

    Purvis GSD, Collino M, Loiola RA, Baragetti A, Chiazza F, Brovelli M, Sheikh MH, Collotta D, Cento A & Mastrocola R et al.Identification of annexinA1 as an endogenous regulator of RhoA, and its role in the pathophysiology and experimental therapy of Type-2 diabetes. Frontiers in Immunology 2019 10 571. (https://doi.org/10.3389/fimmu.2019.00571)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45

    Marshall SA, Qin CX, Jelinic M, O’Sullivan K, Deo M, Walsh J, Li M, Parry LJ, Ritchie RH, Leo CH. The novel small-molecule annexin-A1 mimetic, compound 17b, elicits vasoprotective actions in streptozotocin-induced diabetic mice. International Journal of Molecular Sciences 2020 21 1384. (https://doi.org/10.3390/ijms21041384)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 46

    Huang JJ, Xia CJ, Wei Y, Yao Y, Dong MW, Lin KZ, Yu LS, Gao Y & Fan YY Annexin A1-derived peptide Ac2-26 facilitates wound healing in diabetic mice. Wound Repair and Regeneration 2020 28 772–779. (https://doi.org/10.1111/wrr.12860)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 47

    Luthra R, Singh RR, Luthra MG, Li YX, Hannah C, Romans AM, Barkoh BA, Chen SS, Ensor J & Maru DM et al.MicroRNA-196a targets annexin A1: a microRNA-mediated mechanism of annexin A1 downregulation in cancers. Oncogene 2008 27 6667–6678. (https://doi.org/10.1038/onc.2008.256)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 48

    Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A, Pfeffer S, Rice A, Kamphorst AO & Landthaler M et al.A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 2007 129 1401–1414. (https://doi.org/10.1016/j.cell.2007.04.040)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 49

    Yin Z, Cui Z, Ren Y, Xia L, Li H & Zhou B MiR-196a2 and lung cancer in Chinese non-smoking females: a genetic association study and expression analysis. Oncotarget 2017 8 70890–70898. (https://doi.org/10.18632/oncotarget.20174)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 50

    Zhao H, Xu J, Zhao D, Geng M, Ge H, Fu L & Zhu Z Somatic mutation of the SNP rs11614913 and its association with increased MIR 196A2 expression in breast cancer. DNA and Cell Biology 2016 35 81–87. (https://doi.org/10.1089/dna.2014.2785)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 51

    Ghanbari M, Sedaghat S, de Looper HW, Hofman A, Erkeland SJ, Franco OH & Dehghan A The association of common polymorphisms in miR-196a2 with waist to hip ratio and miR-1908 with serum lipid and glucose. Obesity 2015 23 495–503. (https://doi.org/10.1002/oby.20975)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 52

    Yin Z, Cui Z, Guan P, Li X, Wu W, Ren Y, He Q & Zhou B Interaction between polymorphisms in pre-MiRNA genes and cooking oil fume exposure on the risk of lung cancer in Chinese non-smoking female population. PLoS ONE 2015 10 e0128572. (https://doi.org/10.1371/journal.pone.0128572)

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 53

    Zhuo ZJ, Liu W, Zhang J, Zhu J, Zhang R, Tang J, Yang T, Zou Y, He J & Xia H Functional polymorphisms at ERCC1/XPF genes confer neuroblastoma risk in Chinese children. EBiomedicine 2018 30 113–119. (https://doi.org/10.1016/j.ebiom.2018.03.003)

    • PubMed
    • Search Google Scholar
    • Export Citation