Abstract

MicroRNA (miR) acts as a negative regulator of gene expression. Many literatures have suggested that miRs may be involved in the process of cell proliferation, inflammation, oxidative stress, energy metabolism and epithelial–mesenchymal transition. Thus, miRs may be implicated in the occurrence of non-small cell lung cancer (NSCLC). In the current investigation, we included 2249 subjects (1193 NSCLC patients and 1056 controls) and designed a study to identify the relationship of miR-146a rs2910164 C/G, -499a rs3746444 A/G and -196a-2 rs11614913 T/C with the risk of NSCLC. The risk factors (e.g., body mass index (BMI), sex, smoking, drinking and age) was used to adjust the odds ratios (ORs) and 95% confidence intervals (CIs). After conducting a power value assessment, we did not confirm that the miR-single nucleotide polymorphisms (SNPs) genotypic distributions were different in NSCLC cases and controls. However, the association of miR-196a-2 rs11614913 with a decreased risk of NSCLC was identified in the female subgroup (adjusted P=0.005, power = 0.809 for TC vs. TT, and adjusted P=0.004, power = 0.849 for CC/TC vs. TT). In addition, gene–gene interaction analysis showed that rs11614913 TC/3746444 AA and rs11614913 CC/rs3746444 AA could also reduce the susceptibility to NSCLC (rs11614913 TC/rs3746444 AA vs. rs11614913 TT/rs3746444 AA, P=0.001, power = 0.912 and rs11614913 CC/rs3746444 AA vs. rs11614913 TT/rs3746444 AA, P=0.003, power = 0.836). In conclusion, in overall comparisons, we did not confirm that the rs2910164, rs3746444, and rs11614913 SNPs genotypic distributions were different in NSCLC cases and controls. However, this case–control study demonstrates that miR-196a-2 rs11614913 may be a protective factor for the development of NSCLC among female patients.

Introduction

Lung cancer (LC) caused ∼11.6% of all new cancer cases and 18.4% of all cancer-related deaths worldwide [1]. In China, 733.3 thousand new LC patients and 610.2 thousand LC-related deaths were assessed to occur in 2015 [2]. The etiology of LC was unclear. It is reported that a number of genetic and environmental risk factors may cause the development of LC [3–5]. Non-small cell lung cancer (NSCLC) is the most common type of LC. The individual’s hereditary factor may be implicated in the occurrence of NSCLC.

MicroRNA (miR), a small non-coding RNA, acts as a negative regulator of gene expression. In the nucleus, the Drosha/DiGeorge syndrome critical region 8 complex cleaves pri-miRNAs [6]. Then, in the cytoplasm, Dicer crops these formed pre-miRNAs [7]. Finally, they are incorporated into the Argonaute-containing RNA-induced silencing complexes [8]. Mature miR is composed of ∼22 nucleic acids, which is generated from primary miRs and further changed to mature miRs in cytoplasm. The target mRNAs located in 3′-untranslated regions (3′-UTRs). Matured miRs can recognize the 3′-UTRs of mRNA and bind to them, and then result in a weakened expression of target genes. The mechanism of the process is hybridization of seed sequences of matured miRs with 3′-UTRs. An individual miR can bind to masses of targets, and regulate a number of pathways. Many investigations have suggested that miRs may be involved in the process of cell proliferation, inflammation, oxidative stress, energy metabolism and epithelial–mesenchymal transition (EMT) [9–16]. Of late, some previous investigations have indicated that miRs have been implicated in the occurrence of NSCLC [17,18]. There are single nucleotide polymorphisms (SNPs) in certain miRs. These SNPs might influence the generation process of miRs or alter target recognition/hybridization. Thus, miR polymorphisms may be implicated in the occurrence and/or progress of cancer [19–25].

Park et al. reported that miR-146a could restrain EMT progression in NSCLC by repressing the expression of insulin receptor substrate-2 [14]. It was found that miR-146a inhibited migratory capacity, downstream signaling of epidermal growth factor receptor and NSCLC cell growth; however, it could promote the apoptosis process of NSCLC cell lines [13]. Xiong et al. reported that miR-146a rs2910164 C>G locus could affect its maturation in peripheral blood mononuclear cells [26]. A recent study reported that G allele of rs2910164 mgiht increase miR-146a level [27]. A previous study suggested that rs2910164 locus might influence the toxicity in LC chemotherapy [28]. Several reports indicated that rs2910164 polymorphism in miR-146a could decrease the risk to LC [29,30]. However, other case–control studies suggested that rs2910164 might not influence the occurrence of LC [31,32]. These controversial observations may be due to the limited sample sizes. Here, we explored the role of miR-146a rs2910164 SNP with the development of NSCLC and a potential interaction of this SNP with risk factors to identify whether this locus could be used as a biomarker for susceptibility to NSCLC in Chinese populations.

Rs11614913 T>C was widely explored in malignancy as a candidate locus of miR-196a-2 [33,34]. Hu et al. reported that that the rs11614913 T→C variant in miR-196a-2 could affect the binding ability of mature hsa-mir-196a-2-3p binding with its target mRNA [35]. Recently, this polymorphism was thought to alter LC cases’ sensitivity to platinum-based chemotherapy [23]. A functional study highlighted that rs11614913 might be involved in the development of LC through altering the secondary structure and the expression of miR-196a-2 [36]. Thus, rs11614913 polymorphism might be implicated in carcinogenesis of LC and could affect an individual’s susceptibility of LC. Indeed, several case–control studies have investigated the role of rs11614913 in the occurrence of LC [23,36]. However, the observations were conflicting, even in the same ethnicity. For example, some recent studies indicated a significant relationship between miR-196a-2 rs11614913 and the development of LC [36–38], whereas others did not confirm the potential correlation [23,32].

A previous investigation reported that miR-499a rs3746444 SNP could affect the process of miR-499-5p maturation and the role of antiapoptosis [39]. The relationship between miR-499a rs3746444 A>G and the susceptibility and progress of LC has been explored. Ge et al. reported that miR-499a rs3746444 AA genotype could inhibit the expression of miR-499a gene and CD200 [40]. And then this SNP could influence the survival of NSCLC cases. Several studies have focused on the role of miR-499a rs3746444 in the development of LC [40,41]. However, recent meta-analyses have reported contradictory findings [42–44]. Thus, the correlation of miR-499a rs3746444 with the development of LC was more inconsistent.

In the current investigation, we designed a larger sample size study to identify the correlation of rs3746444, rs2910164 and rs11614913 with the occurrence of NSCLC.

Materials and methods

Study population and ethical approval

Each participant donated a peripheral blood sample. NSCLC cases in the current investigation were recruited from the Zhenjiang Medical College of Nanjing Medical University (Jiangsu Province, China) and the Union Medical College of Fujian Medical University (Fujian Province, China) between January 2014 and June 2018. All NSCLC cases were diagnosed via histopathological examination. In the present study, the selection criteria were defined as the following: (1) Chinese Han populations, (2) sporadic cases and (3) without any history of other cancer. And the exclusion criteria were summarized as: (1) a patient who had an autoimmune disease, (2) NSCLC patients who underwent chemoradiotherapy and/or targeted therapy, (3) NSCLC recurrent cases and (4) heterochronous NSCLC. In total, 1193 NSCLC cases were enrolled. At the same time, 1056 participants without a history of cancer were included as controls in the Medical Colleges mentioned above. The data of demographics and potential risk factors were collected by a pre-structured questionnaire. During the recruitment, each participant signed a written informed consent. The present study was approved by the Ethics Review Committee of Fujian Union Hospital (2018KY023).

Isolation of DNA and genotyping

Using DNA Isolation Kit (Promega, Madison, U.S.A.), we extracted genomic DNA. The obtained DNA was kept at −80°C. The quality of DNA sample was assessed by Nanodrop ND-1000 UV. A custom-SNPscan™ Kit (Genesky Biotechnologies Inc., Shanghai, China) was used to analyze the genotypes. Briefly, no less than 120 ng DNA sample was used to conduct a double ligation and multiplex fluorescence polymerase chain reaction (PCR). ABI-3730XL sequencer (PE Applied Biosystems, Foster City, CA, U.S.A.) was used to detect the PCR products. The obtained raw data were analyzed by harnessing GeneMapper 4.1 (Applied Biosystems, U.S.A.). To conduct a quality control, 90 samples were randomly chosen and repeated genotyped in the same PCR method. The results indicated that 100% concordant results were observed.

Statistical analysis

Hardy–Weinberg equilibrium (HWE) (https://ihg.gsf.de/cgi-bin/hw/hwa1.pl) [45] and SAS 9.4 (SAS Institute, Cary, North Carolina) software were harnessed to analyze HWE and genetic data. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to estimate the relationship of rs2910164, rs11614913 and rs3746444 with the risk of NSCLC. We also calculated adjusted ORs and 95% CIs using logistic regression analyses. In the current study, five risk factors [e.g., body mass index (BMI), smoking, drinking, age and gender] were included. Power Calculator (http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/PowerSampleSize) was used to calculate the power of sample size [19,46]. We also used the false-positive report probability (FPRP) to evaluate the findings [47].

Results

Characteristics of the study population

In the current study, 1193 cases with NSCLC (mean ± SD age, 58.92 ± 10.44 years) and 1056 controls (mean ± SD age, 59.36 ± 9.19 years) were collected (Table 1). In NSCLC group, 642 males and 551 females were included. While in controls, there were 586 males and 470 females. The age and gender were well-mathed (P = 0.960 and 0.425, respectively). The distribution of smoking, drinking and BMI were different between two groups (all P<0.001). Raw data of genotypes and characteristics were summarized in Supplementary Table S1.

Table 1
Distribution of selected demographic variables and risk factors in NSCLC cases and controls
VariableNSCLC cases (n=1193)Controls (n=1056)Pa
n%n%
Age (years) 58.92 ± 10.44  59.36 ± 9.19  0.293 
Age (years)     0.330 
  <59 535 44.84 452 42.80  
  ≥59 658 55.16 604 57.20  
Sex     0.425 
  Male 642 53.81 586 55.65  
  Female 551 46.19 470 44.35  
Smoking status     <0.001 
  Never 757 63.45 857 81.16  
  Ever 436 36.55 199 18.84  
Alcohol use     <0.001 
  Never 946 79.30 967 91.83  
  Ever 247 20.70 89 8.17  
BMI (kg/m2    <0.001 
  <24 801 67.14 571 54.07  
  ≥24 392 32.86 485 45.93  
Type of NSCLC      
  SCC 182 15.26    
  Non-SCC 1,011 84.74    
Stage      
  I 703 58.93    
  II 87 7.29    
  III 222 18.61    
  IV 181 15.17    
Lymph node status      
  Positive 381 31.94    
  Negative 812 68.06    
VariableNSCLC cases (n=1193)Controls (n=1056)Pa
n%n%
Age (years) 58.92 ± 10.44  59.36 ± 9.19  0.293 
Age (years)     0.330 
  <59 535 44.84 452 42.80  
  ≥59 658 55.16 604 57.20  
Sex     0.425 
  Male 642 53.81 586 55.65  
  Female 551 46.19 470 44.35  
Smoking status     <0.001 
  Never 757 63.45 857 81.16  
  Ever 436 36.55 199 18.84  
Alcohol use     <0.001 
  Never 946 79.30 967 91.83  
  Ever 247 20.70 89 8.17  
BMI (kg/m2    <0.001 
  <24 801 67.14 571 54.07  
  ≥24 392 32.86 485 45.93  
Type of NSCLC      
  SCC 182 15.26    
  Non-SCC 1,011 84.74    
Stage      
  I 703 58.93    
  II 87 7.29    
  III 222 18.61    
  IV 181 15.17    
Lymph node status      
  Positive 381 31.94    
  Negative 812 68.06    

Bold values are statistically significant (P<0.05). Abbreviation: SCC, squamous cell carcinoma.

aTwo-sided χ2 test and Student’s t test.

Information of rs3746444, rs2910164 and rs11614913 SNPs

The successful ratio of genotyping was more than 99.00%. Table 2 has summarized some vital information for rs2910164, rs11614913 and rs3746444. In controls, these included miR-SNPs genotype distributions met HWE (P>0.05). Supplementary Table S1 summarized the detailed information and genotypes for each individual.

Table 2
Primary information for miR-146a rs2910164 C>G, miR-196a-2 rs11614913 T>C and miR-499a rs3746444 A>G polymorphisms
Genotyped SNPsmiR-146a rs2910164 C>GmiR-196a-2 rs11614913 T>CmiR-499a rs3746444 A>G
Chromosome 12 20 
Function nc-transcript-variant nc-transcript-variant nc-transcript-variant 
Chr Pos (NCBI Build 38) 160485411 53991815 3499048 
MAF1 for Chinese in database 0.35 0.34 0.15 
MAF in our controls (n=1056) 0.36 0.46 0.15 
P-value for HWE2 test in our controls 0.217 0.208 0.898 
Genotyping method SNPscan SNPscan SNPscan 
% Genotyping value 99.47% 99.47% 99.29% 
Genotyped SNPsmiR-146a rs2910164 C>GmiR-196a-2 rs11614913 T>CmiR-499a rs3746444 A>G
Chromosome 12 20 
Function nc-transcript-variant nc-transcript-variant nc-transcript-variant 
Chr Pos (NCBI Build 38) 160485411 53991815 3499048 
MAF1 for Chinese in database 0.35 0.34 0.15 
MAF in our controls (n=1056) 0.36 0.46 0.15 
P-value for HWE2 test in our controls 0.217 0.208 0.898 
Genotyping method SNPscan SNPscan SNPscan 
% Genotyping value 99.47% 99.47% 99.29% 
1

MAF, minor allele frequency.

2

HWE, Hardy–Weinberg equilibrium.

Rs3746444, rs2910164 and rs11614913 SNPs and NSCLC susceptibility

The number of miR-146a rs2910164 allele and genotype in NSCLC cases and controls is summarized in Table 3. In this case–control study, for overall comparisons, we identified that the miR-146a genotype frequency was not significantly different among the two groups. As well, we also found that the miR-499a rs3746444 genotypic distribution was not different in NSCLC cases and controls.

Table 3
The frequencies of miR-146a rs2910164 C>G, miR-196a-2 rs11614913 T>C and miR-499a rs3746444 A>G polymorphisms in CAD patients and controls
GenotypeOverall NSCLC cases (n=1193)SCC cases (n=182)Non-SCC cases (n=1011)Controls (n=1056)
n%n%n%n%
miR-146a rs2910164 C>G 
CC 460 38.85 68 37.57 392 39.08 440 41.79 
CG 555 46.88 91 50.28 464 46.26 467 44.35 
GG 169 14.27 22 12.15 147 14.66 146 13.87 
G allele 893 37.71 135 37.29 758 37.79 759 36.04 
miR-499a rs3746444 A>G 
AA 814 68.98 128 71.11 686 68.60 757 71.89 
AG 330 27.97 47 26.11 283 28.30 271 25.74 
GG 36 3.05 2.78 31 3.10 25 2.37 
G allele 402 17.03 57 15.83 345 17.25 321 15.24 
miR-196a-2 rs11614913 T>C 
TT 392 33.11 59 32.60 333 33.20 293 27.83 
TC 572 48.31 90 49.72 482 48.06 544 51.66 
CC 220 18.58 32 17.68 188 18.74 216 20.51 
C allele 1,012 42.74 154 42.54 858 42.77 976 46.34 
GenotypeOverall NSCLC cases (n=1193)SCC cases (n=182)Non-SCC cases (n=1011)Controls (n=1056)
n%n%n%n%
miR-146a rs2910164 C>G 
CC 460 38.85 68 37.57 392 39.08 440 41.79 
CG 555 46.88 91 50.28 464 46.26 467 44.35 
GG 169 14.27 22 12.15 147 14.66 146 13.87 
G allele 893 37.71 135 37.29 758 37.79 759 36.04 
miR-499a rs3746444 A>G 
AA 814 68.98 128 71.11 686 68.60 757 71.89 
AG 330 27.97 47 26.11 283 28.30 271 25.74 
GG 36 3.05 2.78 31 3.10 25 2.37 
G allele 402 17.03 57 15.83 345 17.25 321 15.24 
miR-196a-2 rs11614913 T>C 
TT 392 33.11 59 32.60 333 33.20 293 27.83 
TC 572 48.31 90 49.72 482 48.06 544 51.66 
CC 220 18.58 32 17.68 188 18.74 216 20.51 
C allele 1,012 42.74 154 42.54 858 42.77 976 46.34 

Abbreviation: SCC, squamous cell carcinoma.

Table 3 lists the miR-196a-2 rs11614913 genotype distribution in NSCLC cases and controls. It was notable that there was statistical significance in comparison of rs11614913 genotypes in three genetic models among NSCLC cases and controls. The decreased genotype frequencies of rs11614913 TC, CC and TC/CC were found in NSCLC patients. In relation to rs11614913 TT, individuals carrying rs11614913 TC genotypes had a decreased 21% susceptibility to the cocurrence of NSCLC (P=0.014, Table 4). Additionally, compared with rs11614913 TT, rs11614913 CC and TC/CC genotypes were also protective factors for the co-ocurrence of NSCLC (CC vs. TT: P=0.027 and TC/CC vs. TT: P=0.007, Table 4). When we adjusted for risk factors, the decreased susceptibility for the occurrence of NSCLC was not changed (Table 4).

Table 4
Overall and stratified analyses of miR-146a rs2910164 C>G, miR-196a-2 rs11614913 T>C and miR-499a rs3746444 A>G polymorphisms with NSCLC
GenotypeOverall NSCLC cases (n=1193) vs. Controls (1056)Non-SCC cases cases (n=1011) vs. Controls (1056)SCC cases cases (n=182) vs. Controls (1056)
Crude OR (95% CI)PAdjusted OR1 (95% CI)PCrude OR (95% CI)PAdjusted OR1 (95% CI)PCrude OR (95% CI)PAdjusted OR1 (95% CI)P
miR-146a rs2910164 C>G 
  CG vs. CC 1.14 (0.95–1.36) 0.162 1.11 (0.92–1.34) 0.268 1.12 (0.93–1.35) 0.254 1.07 (0.88–1.30) 0.498 1.26 (0.90–1.77) 0.182 1.22 (0.82–1.81) 0.323 
  GG vs. CC 1.11 (0.86–1.43) 0.437 1.17 (0.90–1.54) 0.243 1.13 (0.87–1.48) 0.368 1.15 (0.87–1.51) 0.329 0.98 (0.58–1.63) 0.924 1.24 (0.68–2.27) 0.477 
  GG/CG vs. CC 1.13 (0.95–1.34) 0.158 1.13 (0.94–1.34) 0.188 1.12 (0.94–1.33) 0.212 1.09 (0.91–1.31) 0.367 1.19 (0.86–1.65) 0.287 1.23 (0.84–1.79) 0.291 
  GG vs. CC/CG 1.03 (0.82–1.31) 0.782 1.11 (0.87–1.42) 0.415 1.07 (0.83–1.37) 0.608 1.11 (0.86–1.43) 0.436 0.86 (0.53–1.39) 0.536 1.12 (0.64–1.96) 0.700 
miR-499a rs3746444 A>G 
AG vs. AA 1.13 (0.94–1.37) 0.196 1.14 (0.93–1.39) 0.201 1.15 (0.95–1.40) 0.156 1.16 (0.94–1.42) 0.164 1.03 (0.71–1.47) 0.891 0.92 (0.61–1.41) 0.707 
GG vs. AA 1.34 (0.80–2.25) 0.271 1.63 (0.94–2.81) 0.081 1.37 (0.80–2.34) 0.253 1.64 (0.94–2.88) 0.083 1.18 (0.45–3.15) 0.737 1.18 (0.37–3.70) 0.780 
GG/AG vs. AA 1.15 (0.96–1.38) 0.133 1.18 (0.97–1.42) 0.098 1.17 (0.97–1.42) 0.103 1.19 (0.98–1.45) 0.080 1.04 (0.73–1.47) 0.829 0.94 (0.63–1.42) 0.778 
GG vs. AA/AG 1.29 (0.77–2.17) 0.329 1.57 (0.91–2.71) 0.104 1.32 (0.77–2.24) 0.315 1.58 (0.90–2.76) 0.109 1.18 (0.44–3.11) 0.746 1.20 (0.38–3.76) 0.752 
miR-196a-2 rs11614913 T>C 
  TC vs. TT 0.79 (0.65–0.95) 0.014 0.79 (0.65–0.97) 0.024 0.78 (0.64–0.95) 0.014 0.79 (0.64–0.97) 0.026 0.82 (0.58–1.18) 0.282 0.82 (0.54–1.24) 0.336 
  CC vs. TT 0.76 (0.60–0.97) 0.027 0.77 (0.60–0.99) 0.042 0.77 (0.60–0.98) 0.037 0.77 (0.60–1.00) 0.052 0.74 (0.46–1.17) 0.196 0.83 (0.48–1.42) 0.490 
  CC/ TC vs. TT 0.78 (0.65–0.93) 0.007 0.79 (0.65–0.95) 0.014 0.78 (0.64–0.94) 0.008 0.79 (0.65–0.96) 0.015 0.80 (0.57–1.12) 0.190 0.82 (0.55–1.21) 0.319 
  CC vs. TT/TC 0.88 (0.72–1.09) 0.249 0.89 (0.71–1.11) 0.286 0.89 (0.72–1.11) 0.314 0.90 (0.71–1.12) 0.333 0.83 (0.55–1.25) 0.380 0.94 (0.59–1.51) 0.795 
GenotypeOverall NSCLC cases (n=1193) vs. Controls (1056)Non-SCC cases cases (n=1011) vs. Controls (1056)SCC cases cases (n=182) vs. Controls (1056)
Crude OR (95% CI)PAdjusted OR1 (95% CI)PCrude OR (95% CI)PAdjusted OR1 (95% CI)PCrude OR (95% CI)PAdjusted OR1 (95% CI)P
miR-146a rs2910164 C>G 
  CG vs. CC 1.14 (0.95–1.36) 0.162 1.11 (0.92–1.34) 0.268 1.12 (0.93–1.35) 0.254 1.07 (0.88–1.30) 0.498 1.26 (0.90–1.77) 0.182 1.22 (0.82–1.81) 0.323 
  GG vs. CC 1.11 (0.86–1.43) 0.437 1.17 (0.90–1.54) 0.243 1.13 (0.87–1.48) 0.368 1.15 (0.87–1.51) 0.329 0.98 (0.58–1.63) 0.924 1.24 (0.68–2.27) 0.477 
  GG/CG vs. CC 1.13 (0.95–1.34) 0.158 1.13 (0.94–1.34) 0.188 1.12 (0.94–1.33) 0.212 1.09 (0.91–1.31) 0.367 1.19 (0.86–1.65) 0.287 1.23 (0.84–1.79) 0.291 
  GG vs. CC/CG 1.03 (0.82–1.31) 0.782 1.11 (0.87–1.42) 0.415 1.07 (0.83–1.37) 0.608 1.11 (0.86–1.43) 0.436 0.86 (0.53–1.39) 0.536 1.12 (0.64–1.96) 0.700 
miR-499a rs3746444 A>G 
AG vs. AA 1.13 (0.94–1.37) 0.196 1.14 (0.93–1.39) 0.201 1.15 (0.95–1.40) 0.156 1.16 (0.94–1.42) 0.164 1.03 (0.71–1.47) 0.891 0.92 (0.61–1.41) 0.707 
GG vs. AA 1.34 (0.80–2.25) 0.271 1.63 (0.94–2.81) 0.081 1.37 (0.80–2.34) 0.253 1.64 (0.94–2.88) 0.083 1.18 (0.45–3.15) 0.737 1.18 (0.37–3.70) 0.780 
GG/AG vs. AA 1.15 (0.96–1.38) 0.133 1.18 (0.97–1.42) 0.098 1.17 (0.97–1.42) 0.103 1.19 (0.98–1.45) 0.080 1.04 (0.73–1.47) 0.829 0.94 (0.63–1.42) 0.778 
GG vs. AA/AG 1.29 (0.77–2.17) 0.329 1.57 (0.91–2.71) 0.104 1.32 (0.77–2.24) 0.315 1.58 (0.90–2.76) 0.109 1.18 (0.44–3.11) 0.746 1.20 (0.38–3.76) 0.752 
miR-196a-2 rs11614913 T>C 
  TC vs. TT 0.79 (0.65–0.95) 0.014 0.79 (0.65–0.97) 0.024 0.78 (0.64–0.95) 0.014 0.79 (0.64–0.97) 0.026 0.82 (0.58–1.18) 0.282 0.82 (0.54–1.24) 0.336 
  CC vs. TT 0.76 (0.60–0.97) 0.027 0.77 (0.60–0.99) 0.042 0.77 (0.60–0.98) 0.037 0.77 (0.60–1.00) 0.052 0.74 (0.46–1.17) 0.196 0.83 (0.48–1.42) 0.490 
  CC/ TC vs. TT 0.78 (0.65–0.93) 0.007 0.79 (0.65–0.95) 0.014 0.78 (0.64–0.94) 0.008 0.79 (0.65–0.96) 0.015 0.80 (0.57–1.12) 0.190 0.82 (0.55–1.21) 0.319 
  CC vs. TT/TC 0.88 (0.72–1.09) 0.249 0.89 (0.71–1.11) 0.286 0.89 (0.72–1.11) 0.314 0.90 (0.71–1.12) 0.333 0.83 (0.55–1.25) 0.380 0.94 (0.59–1.51) 0.795 

Bold values are statistically significant (P<0.05). Abbreviation: SCC, squamous cell carcinoma.

1

Adjusted for age, sex, smoking, drinking and BMI.

MiR-SNPs and NSCLC susceptibility in different types of pathology

Supplementary Tables S2 and S3 summarized the detailed information and genotypes for squamous cell carcinoma (SCC) and non-SCC cases, respectively. When we conducted a subgroup analysis by type of pathology, for rs11614913 SNP, the decreased susceptibility for the occurrence of NSCLC was also found in non-SCC subgroup (TC vs. TT: adjusted P=0.026 and TC/CC vs. TT: adjusted P=0.015, Table 4). For rs2910164 and rs3746444 polymorphisms, no significant association between these SNPs and NSCLC risk was found (Table 4).

Stratification analysis of miR-SNPs and NSCLC susceptibility

MiR-146a rs2910164 C>G locus

When we conducted stratification analyses by risk factors, an increased risk for the occurrence of NSCLC was identified in never drinking subgroup (CG vs. CC: adjusted P=0.043 and GG/CG vs. CC: adjusted P=0.028, Table 5).

Table 5
Stratified analyses between miR-146a rs2910164 C>G polymorphism and NSCLC risk by age, sex, smoking, drinking and BMI
VariablemiRNA-146a rs2910164 C>G (case/control)1Adjusted OR2 (95% CI); P
CCCGGGCG vs. CCGG vs. CCGG/CG vs. CCGG vs. CC/CG
Sex 
  Male 260/249 289/255 89/80 1.06 (0.81–1.37); P: 0.685 1.19 (0.82–1.73); P: 0.361 1.09 (0.85–-1.39); P: 0.508 1.16 (0.82–1.64); P: 0.411 
  Female 200/191 266/212 80/66 1.15 (0.88–1.52); P: 0.309 1.21 (0.82–1.78); P: 0.347 1.17 (0.90–1.51); P: 0.247 1.12 (0.78–1.60); P: 0.550 
Age 
  <59 203/192 258/198 69/60 1.16 (0.87–1.54); P: 0.313 1.17 (0.76–1.78); P: 0.478 1.16 (0.89–1.52); P: 0.282 1.08 (0.73–1.60); P: 0.709 
  ≥59 257/248 297/269 100/86 1.06 (0.83–1.37); P: 0.627 1.22 (0.86–1.73); P: 0.272 1.10 (0.87–1.39); P: 0.426 1.18 (0.85–1.63); P: 0.323 
Smoking status 
  Never 280/358 360/371 111/125 1.22 (0.98–1.52); P: 0.080 1.15 (0.85–1.57); P: 0.274 1.20 (0.98–1.48); P: 0.084 1.04 (0.78–1.38); P: 0.809 
  Ever 180/82 195/96 58/21 0.88 (0.61–1.27); P: 0.507 1.32 (0.74–2.33); P: 0.352 0.96 (0.68–1.36); P: 0.814 1.40 (0.82–2.40); P: 0.221 
Alcohol consumption 
  Never 354/410 447/420 139/135 1.23 (1.01–1.21); P: 0.043 1.26 (0.94–1.67); P: 0.120 1.24 (1.02–1.50); P: 0.028 1.12 (0.86–1.47); P: 0.390 
  Ever 106/30 108/47 30/11 0.59 (0.34–1.02); P: 0.061 0.77 (0.34–1.73); P: 0.527 0.63 (0.37–1.06); P: 0.079 1.02 (0.48–2.16); P: 0.956 
BMI (kg/m2       
  <24 303/236 381/260 110/73 1.12 (0.88–1.42); P: 0.373 1.27 (0.89–1.80); P: 0.191 1.15 (0.91–1.44); P: 0.236 1.19 (0.86–1.66); P: 0.292 
  ≥24 157/204 174/207 59/73 1.11 (0.82–1.50); P: 0.493 1.06 (0.70–1.61); P: 0.790 1.10 (0.83–1.46); P: 0.518 1.00 (0.68–1.48); P: 0.988 
VariablemiRNA-146a rs2910164 C>G (case/control)1Adjusted OR2 (95% CI); P
CCCGGGCG vs. CCGG vs. CCGG/CG vs. CCGG vs. CC/CG
Sex 
  Male 260/249 289/255 89/80 1.06 (0.81–1.37); P: 0.685 1.19 (0.82–1.73); P: 0.361 1.09 (0.85–-1.39); P: 0.508 1.16 (0.82–1.64); P: 0.411 
  Female 200/191 266/212 80/66 1.15 (0.88–1.52); P: 0.309 1.21 (0.82–1.78); P: 0.347 1.17 (0.90–1.51); P: 0.247 1.12 (0.78–1.60); P: 0.550 
Age 
  <59 203/192 258/198 69/60 1.16 (0.87–1.54); P: 0.313 1.17 (0.76–1.78); P: 0.478 1.16 (0.89–1.52); P: 0.282 1.08 (0.73–1.60); P: 0.709 
  ≥59 257/248 297/269 100/86 1.06 (0.83–1.37); P: 0.627 1.22 (0.86–1.73); P: 0.272 1.10 (0.87–1.39); P: 0.426 1.18 (0.85–1.63); P: 0.323 
Smoking status 
  Never 280/358 360/371 111/125 1.22 (0.98–1.52); P: 0.080 1.15 (0.85–1.57); P: 0.274 1.20 (0.98–1.48); P: 0.084 1.04 (0.78–1.38); P: 0.809 
  Ever 180/82 195/96 58/21 0.88 (0.61–1.27); P: 0.507 1.32 (0.74–2.33); P: 0.352 0.96 (0.68–1.36); P: 0.814 1.40 (0.82–2.40); P: 0.221 
Alcohol consumption 
  Never 354/410 447/420 139/135 1.23 (1.01–1.21); P: 0.043 1.26 (0.94–1.67); P: 0.120 1.24 (1.02–1.50); P: 0.028 1.12 (0.86–1.47); P: 0.390 
  Ever 106/30 108/47 30/11 0.59 (0.34–1.02); P: 0.061 0.77 (0.34–1.73); P: 0.527 0.63 (0.37–1.06); P: 0.079 1.02 (0.48–2.16); P: 0.956 
BMI (kg/m2       
  <24 303/236 381/260 110/73 1.12 (0.88–1.42); P: 0.373 1.27 (0.89–1.80); P: 0.191 1.15 (0.91–1.44); P: 0.236 1.19 (0.86–1.66); P: 0.292 
  ≥24 157/204 174/207 59/73 1.11 (0.82–1.50); P: 0.493 1.06 (0.70–1.61); P: 0.790 1.10 (0.83–1.46); P: 0.518 1.00 (0.68–1.48); P: 0.988 
1

For miRNA-146a rs2910164 C>G, the genotyping was successful in 1184 (99.25%) NSCLC cases and 1053 (99.72%) controls.

2

Adjusted for age, sex, smoking, drinking and BMI (besides stratified factors accordingly) in a multiple logistic regression model.

MiR-499a rs3746444 A>G locus

Table 6 listed the findings of stratification analyses for rs3746444 polymorphism. We identified that rs3746444 polymorphism elevated the susceptibility of NSCLC (never smoking subgroup: adjusted P=0.035 for GG vs. AA genetic model and adjusted P=0.049 for GG vs. AA/AG genetic model; never drinking subgroup: adjusted P=0.032 for GG vs. AA genetic model, adjusted P=0.035 for GG/AG vs. AA genetic model and adjusted P=0.047 for GG vs. AA/AG genetic model; BMI < 24 (kg/m2) subgroup: adjusted P=0.042 for AG vs. AA genetic model and adjusted P=0.034 for GG vs. AA/AG genetic model and never BMI ≥ 24 (kg/m2) subgroup: adjusted P=0.046 for GG vs. AA/AG genetic model).

Table 6
Stratified analyses between miR-499a rs3746444 A>G polymorphism and NSCLC risk by age, sex, smoking, drinking and BMI
VariablemiRNA-499a rs3746444 A>G (case/control)1Adjusted OR2 (95% CI); P
AAAGGGAG vs. AAGG vs. AAGG/AG vs. AAGG vs. AA/AG
Sex 
  Male 444/415 172/152 20/17 1.05 (0.79–1.38); P: 0.744 1.59 (0.79–3.21); P: 0.199 1.09 (0.84–1.43); P: 0.509 1.57 (0.78–3.16); P: 0.209 
  Female 370/342 158/119 16/8 1.21 (0.91–1.61); P: 0.194 1.84 (0.77–4.41); P: 0.172 1.25 (0.95–1.65); P: 0.118 1.74 (0.73–4.17); P: 0.211 
Age 
  <59 367/338 144/101 15/11 1.30 (0.95–1.78); P: 0.096 1.68 (0.72–3.92); P: 0.233 1.33 (0.99–1.80); P: 0.060 1.57 (0.67–3.65); P: 0.297 
  ≥59 447/419 186/170 21/14 1.03 (0.79-1.33); P: 0.854 1.71 (0.84-3.51); P: 0.141 1.07 (0.84–1.38); P: 0.583 1.70 (0.83–3.47); P: 0.144 
Smoking status 
Never 511/618 209/215 28/21 1.17 (0.93–1.48); P: 0.176 1.91 (1.08–3.48); P: 0.035 1.23 (0.99–1.54); P: 0.066 1.82 (1.00–3.32); P: 0.049 
Ever 303/139 121/56 8/4 1.04 (0.71–1.52); P: 0.856 0.90 (0.26–3.13); P: 0.873 1.03 (0.71–1.49); P: 0.889 0.90 (0.26–3.09); P: 0.861 
Alcohol consumption 
  Never 629/695 274/247 33/23 1.19 (0.97–1.47); P: 0.101 1.86 (1.06–3.29); P: 0.032 1.25 (1.02–1.53); P: 0.035 1.77 (1.01–3.12); P: 0.047 
  Ever 185/62 56/24 3/2 0.75 (0.42–1.32); P: 0.314 0.43 (0.07–2.65); P: 0.360 0.72 (0.41–1.25); P: 0.245 0.46 (0.07–2.82); P: 0.398 
BMI (kg/m2) 
  <24 535/413 230/139 25/17 1.30 (1.01–1.68); P: 0.042 1.31 (0.68–2.52); P: 0.419 1.30 (1.02–1.67); P: 0.034 1.22 (0.64–2.33); P: 0.555 
  ≥24 279/344 100/132 11/8 0.90 (0.65–1.23); P: 0.495 2.54 (0.98–6.55); P: 0.054 0.97 (0.71–1.32); P: 0.854 2.61 (1.02–6.73); P: 0.046 
VariablemiRNA-499a rs3746444 A>G (case/control)1Adjusted OR2 (95% CI); P
AAAGGGAG vs. AAGG vs. AAGG/AG vs. AAGG vs. AA/AG
Sex 
  Male 444/415 172/152 20/17 1.05 (0.79–1.38); P: 0.744 1.59 (0.79–3.21); P: 0.199 1.09 (0.84–1.43); P: 0.509 1.57 (0.78–3.16); P: 0.209 
  Female 370/342 158/119 16/8 1.21 (0.91–1.61); P: 0.194 1.84 (0.77–4.41); P: 0.172 1.25 (0.95–1.65); P: 0.118 1.74 (0.73–4.17); P: 0.211 
Age 
  <59 367/338 144/101 15/11 1.30 (0.95–1.78); P: 0.096 1.68 (0.72–3.92); P: 0.233 1.33 (0.99–1.80); P: 0.060 1.57 (0.67–3.65); P: 0.297 
  ≥59 447/419 186/170 21/14 1.03 (0.79-1.33); P: 0.854 1.71 (0.84-3.51); P: 0.141 1.07 (0.84–1.38); P: 0.583 1.70 (0.83–3.47); P: 0.144 
Smoking status 
Never 511/618 209/215 28/21 1.17 (0.93–1.48); P: 0.176 1.91 (1.08–3.48); P: 0.035 1.23 (0.99–1.54); P: 0.066 1.82 (1.00–3.32); P: 0.049 
Ever 303/139 121/56 8/4 1.04 (0.71–1.52); P: 0.856 0.90 (0.26–3.13); P: 0.873 1.03 (0.71–1.49); P: 0.889 0.90 (0.26–3.09); P: 0.861 
Alcohol consumption 
  Never 629/695 274/247 33/23 1.19 (0.97–1.47); P: 0.101 1.86 (1.06–3.29); P: 0.032 1.25 (1.02–1.53); P: 0.035 1.77 (1.01–3.12); P: 0.047 
  Ever 185/62 56/24 3/2 0.75 (0.42–1.32); P: 0.314 0.43 (0.07–2.65); P: 0.360 0.72 (0.41–1.25); P: 0.245 0.46 (0.07–2.82); P: 0.398 
BMI (kg/m2) 
  <24 535/413 230/139 25/17 1.30 (1.01–1.68); P: 0.042 1.31 (0.68–2.52); P: 0.419 1.30 (1.02–1.67); P: 0.034 1.22 (0.64–2.33); P: 0.555 
  ≥24 279/344 100/132 11/8 0.90 (0.65–1.23); P: 0.495 2.54 (0.98–6.55); P: 0.054 0.97 (0.71–1.32); P: 0.854 2.61 (1.02–6.73); P: 0.046 
1

For miR-499a rs3746444 A>G, the genotyping was successful in 1180 (98.91%) NSCLC cases and 1053 (99.72%) controls.

2

Adjusted for age, sex, smoking, drinking and BMI (besides stratified factors accordingly) in a multiple logistic regression model.

MiR-196a-2 rs11614913 T>C locus

For miR-196a-2 rs11614913, significant difference in frequency of its genotype was found between NSCLC cases and controls. We identified that rs11614913 polymorphism may be a protective factor for the occurrence of NSCLC (female subgroup: adjusted P=0.005 for TC vs. TT genetic model, adjusted P=0.038 for CC vs. TT genetic model and adjusted P=0.004 for CC/TC vs. TT genetic model; never smoking subgroup: adjusted P=0.038 for CC vs. TT genetic model and adjusted P=0.049 for CC/TC vs. TT genetic model; never drinking subgroup: adjusted P=0.024 for TC vs. TT genetic model, adjusted P=0.018 for CC vs. TT genetic model and adjusted P=0.009 for CC/TC vs. TT genetic model, Table 7).

Table 7
Stratified analyses between miR-196a-2 rs11614913 T>C polymorphism and NSCLC risk by age, sex, smoking, drinking and BMI
VariablemiR-196a-2 rs11614913 T>C (case/control)1Adjusted OR2 (95% CI); P
TTTCCCTC vs. TTCC vs. TTCC/TC vs. TTCC vs. TT/TC
Sex 
  Male 204/176 315/287 119/121 0.96 (0.73–1.26); P: 0.761 0.87 (0.61–1.23); P: 0.428 0.93 (0.72–1.21); P: 0.594 0.89 (0.66–1.21); P: 0.461 
  Female 188/117 257/257 101/95 0.66 (0.49–0.88); P: 0.005 0.68 (0.47–0.98); P: 0.038 0.66 (0.50–0.87); P: 0.004 0.88 (0.64–1.22); P: 0.445 
Age 
  <59 184/141 246/218 100/91 0.81 (0.60–1.09); P: 0.165 0.81 (0.56–1.19); P: 0.279 0.81 (0.61–1.07); P: 0.142 0.92 (0.66–1.29); P: 0.625 
  ≥59 208/152 326/326 120/125 0.79 (0.60–1.03); P: 0.083 0.74 (0.53–1.04); P: 0.081 0.77 (0.60–1.00); P: 0.050 0.86 (0.64–1.15); P: 0.317 
Smoking status 
  Never 246/237 365/436 140/181 0.83 (0.66–1.05); P: 0.121 0.73 (0.55–0.98); P: 0.038 0.80 (0.64–1.00); P: 0.049 0.82 (0.64–1.06); P: 0.131 
  Ever 146/56 207/108 80/35 0.73 (0.49–1.08); P: 0.116 0.88 (0.53–1.47); P: 0.624 0.77 (0.531.11); P: 0.163 1.07 (0.69–1.67); P: 0.765 
Alcohol consumption 
  Never 312/264 456/501 172/200 0.78 (0.63–0.97); P: 0.024 0.72 (0.55–0.95); P: 0.018 0.76 (0.62–0.94); P: 0.009 0.84 (0.671.07); P: 0.151 
  Ever 80/29 116/43 48/16 0.97 (0.55–1.70); P: 0.908 1.19 (0.582.45); P: 0.640 1.03 (0.611.74); P: 0.923 1.21 (0.642.30); P: 0.558 
BMI (kg/m2
  <24 258/165 382/282 154/122 0.83 (0.64–1.08); P: 0.167 0.82 (0.591.12); P: 0.207 0.83 (0.651.06); P: 0.128 0.91 (0.691.20); P: 0.505 
  ≥24 134/128 190/262 66/94 0.75 (0.551.03); P: 0.079 0.70 (0.471.07); P: 0.097 0.74 (0.551.00); P: 0.051 0.84 (0.591.21); P: 0.358 
VariablemiR-196a-2 rs11614913 T>C (case/control)1Adjusted OR2 (95% CI); P
TTTCCCTC vs. TTCC vs. TTCC/TC vs. TTCC vs. TT/TC
Sex 
  Male 204/176 315/287 119/121 0.96 (0.73–1.26); P: 0.761 0.87 (0.61–1.23); P: 0.428 0.93 (0.72–1.21); P: 0.594 0.89 (0.66–1.21); P: 0.461 
  Female 188/117 257/257 101/95 0.66 (0.49–0.88); P: 0.005 0.68 (0.47–0.98); P: 0.038 0.66 (0.50–0.87); P: 0.004 0.88 (0.64–1.22); P: 0.445 
Age 
  <59 184/141 246/218 100/91 0.81 (0.60–1.09); P: 0.165 0.81 (0.56–1.19); P: 0.279 0.81 (0.61–1.07); P: 0.142 0.92 (0.66–1.29); P: 0.625 
  ≥59 208/152 326/326 120/125 0.79 (0.60–1.03); P: 0.083 0.74 (0.53–1.04); P: 0.081 0.77 (0.60–1.00); P: 0.050 0.86 (0.64–1.15); P: 0.317 
Smoking status 
  Never 246/237 365/436 140/181 0.83 (0.66–1.05); P: 0.121 0.73 (0.55–0.98); P: 0.038 0.80 (0.64–1.00); P: 0.049 0.82 (0.64–1.06); P: 0.131 
  Ever 146/56 207/108 80/35 0.73 (0.49–1.08); P: 0.116 0.88 (0.53–1.47); P: 0.624 0.77 (0.531.11); P: 0.163 1.07 (0.69–1.67); P: 0.765 
Alcohol consumption 
  Never 312/264 456/501 172/200 0.78 (0.63–0.97); P: 0.024 0.72 (0.55–0.95); P: 0.018 0.76 (0.62–0.94); P: 0.009 0.84 (0.671.07); P: 0.151 
  Ever 80/29 116/43 48/16 0.97 (0.55–1.70); P: 0.908 1.19 (0.582.45); P: 0.640 1.03 (0.611.74); P: 0.923 1.21 (0.642.30); P: 0.558 
BMI (kg/m2
  <24 258/165 382/282 154/122 0.83 (0.64–1.08); P: 0.167 0.82 (0.591.12); P: 0.207 0.83 (0.651.06); P: 0.128 0.91 (0.691.20); P: 0.505 
  ≥24 134/128 190/262 66/94 0.75 (0.551.03); P: 0.079 0.70 (0.471.07); P: 0.097 0.74 (0.551.00); P: 0.051 0.84 (0.591.21); P: 0.358 
1

For miR-196a-2 rs11614913 T>C, the genotyping was successful in 1184 (99.25%) NSCLC cases and 1053 (99.72%) controls.

2

Adjusted for age, sex, smoking, drinking and BMI (besides stratified factors accordingly) in a multiple logistic regression model.

Gene–gene interaction analysis

We also conducted miR-SNPs combined analysis for three included SNPs. Three potential types (rs11614913/rs2910164, rs11614913/rs3746444, rs2910164/rs3746444 and rs11614913/rs2910164/rs3746444) were combined to explore the gene–gene interaction and their roles on the occurrence of NSCLC.

In analysis of rs11614913/rs2910164 loci combination, we used rs11614913 TT/rs2910164 CC as reference. It was notable that the rs11614913 CC/rs2910164 CC combination was a protective factor for the development of NSCLC (P=0.010, Table 8). In another analysis of rs11614913/rs3746444 loci combination, compared with rs11614913 TT/rs3746444 AA, frequency of rs11614913 TC/rs3746444 AA was lower in NSCLC patients 32.54% (384/1080) than in controls 37.70% (397/1053). When rs11614913 TT/rs3746444 AA was used as a reference, frequency of rs11614913 CC/rs3746444 AA was also lower in NSCLC patients 12.46% (147/1080) than in controls 15.19% (160/1053). When rs11614913 TT/rs2910164 CC/rs3746444 AA was used as a reference, TC/CC/AA, TC/GG/AA and CC/CC/AA genotype combinations might decrease the risk of NSCLC (Table 8).

Table 8
Combination analysis of miR polymorphisms (rs2910164, rs11614913 and rs3746444) in NSCLC patients and controls
GenotypeCaseControlOR (95% CI)P-value
n%n%
rs11614913/rs2910164 
TT/CC 159 13.43 122 11.59 1.00  
TT/CG 177 14.95 133 12.63 1.02 (0.74–1.41) 0.900 
TT/GG 56 4.73 38 3.61 1.13 (0.70–1.82) 0.612 
TC/CC 227 19.17 224 21.27 0.78 (0.58–1.02) 0.110 
TC/CG 268 22.64 239 22.70 0.86 (0.64–1.15) 0.315 
TC/GG 77 6.50 81 7.69 0.73 (0.49–1.08) 0.113 
CC/CC 74 6.25 94 8.93 0.60 (0.41–0.89) 0.010 
CC/CG 110 9.29 95 9.02 0.89 (0.62–1.28) 0.522 
CC/GG 36 3.04 27 2.56 1.02(0.59–1.78) 0.936 
rs11614913/rs3746444 
TT/AA 283 23.98 200 18.99 1.00  
TT/AG 97 8.22 86 8.17 0.80 (0.57–1.12) 0.194 
TT/GG 11 0.93 0.66 1.11 (0.42–2.91) 0.831 
TC/AA 384 32.54 397 37.70 0.68 (0.54–0.86) 0.001 
TC/AG 166 14.07 137 13.01 0.86 (0.64–1.14) 0.294 
TC/GG 19 1.61 10 0.95 1.34 (0.61–2.95) 0.462 
CC/AA 147 12.46 160 15.19 0.65 (0.49–0.87) 0.003 
CC/AG 67 5.68 48 4.56 0.99 (0.65–1.49) 0.948 
CC/GG 0.51 0.76 0.53(0.18–1.55) 0.239 
rs2910164/rs3746444 
CC/AA 322 27.29 324 30.77 1.00  
CC/AG 124 10.51 108 10.26 1.16 (0.86–1.56) 0.346 
CC/GG 13 1.10 0.76 1.64 (0.67–4.00) 0.277 
CG/AA 374 31.69 320 30.89 1.18 (0.95–1.46) 0.139 
CG/AG 161 13.64 135 12.82 1.20 (0.91–1.58) 0.195 
CG/GG 18 1.53 12 1.14 1.51 (0.72–3.18) 0.277 
GG/AA 118 10.00 113 10.73 1.05 (0.78–1.42) 0.747 
GG/AG 45 3.81 28 2.66 1.62 (0.98–2.66) 0.056 
GG/GG 0.42 0.47 1.01 (0.29–3.51) 0.992 
rs11614913/rs2910164/rs3746444 
TT/CC/AA 114 9.66 86 8.17 1.00  
TT/CC/AG 41 3.47 35 3.32 0.88 (0.52–1.50) 0.648 
TT/CC/GG 0.34 0.09 3.02 (0.33–27.55) 0.304 
TT/CG/AA 128 10.85 89 8.45 1.08 (0.74–1.60) 0.681 
TT/CG/AG 44 3.73 40 3.80 0.83 (0.50–1.38) 0.475 
TT/CG/GG 0.42 0.38 0.94 (0.25–3.62) 0.932 
TT/GG/AA 41 3.47 25 2.37 1.24 (0.70–2.19) 0.464 
TT/GG/AG 12 1.02 11 1.04 0.82 (0.35–1.95) 0.658 
TT/GG/GG 0.17 0.19 0.75 (0.10–5.47) 0.780 
TC/CC/AA 155 13.14 167 15.86 0.70 (0.49–1.00) 0.049 
TC/CC/AG 64 5.42 54 5.13 0.89 (0.57–1.41) 0.632 
TC/CC/GG 0.59 0.28 1.76 (0.44–7.01) 0.417 
TC/CG/AA 174 14.75 163 15.48 0.81 (0.57–1.15) 0.228 
TC/CG/AG 83 7.03 71 6.74 0.88 (0.58–1.35) 0.560 
TC/CG/GG 0.76 0.47 1.36 (0.44–4.20) 0.594 
TC/GG/AA 55 4.66 67 6.36 0.62 (0.39–0.97) 0.038 
TC/GG/AG 19 1.61 12 1.14 1.19 (0.55–2.59) 0.653 
TC/GG/GG 0.25 0.19 1.13 (0.18–6.92) 0.894 
CC/CC/AA 53 4.49 71 6.74 0.56 (0.36–0.89) 0.013 
CC/CC/AG 19 1.61 19 1.80 0.75 (0.38–1.51) 0.426 
CC/CC/GG 0.17 0.38 0.38 (0.07–2.11) 0.250 
CC/CG/AA 72 6.10 68 6.46 0.80 (0.52–1.23) 0.310 
CC/CG/AG 34 2.88 24 2.28 1.07 (0.59–1.93) 0.826 
CC/CG/GG 0.34 0.28 1.01 (0.22–4.61) 0.994 
CC/GG/AA 22 1.86 21 1.99 0.79 (0.41–1.53) 0.484 
CC/GG/AG 14 1.19 0.47 2.26 (0.79–6.47) 0.119 
CC/GG/GG 0.0 0.09 0.25 (0.01–6.26) 0.251 
GenotypeCaseControlOR (95% CI)P-value
n%n%
rs11614913/rs2910164 
TT/CC 159 13.43 122 11.59 1.00  
TT/CG 177 14.95 133 12.63 1.02 (0.74–1.41) 0.900 
TT/GG 56 4.73 38 3.61 1.13 (0.70–1.82) 0.612 
TC/CC 227 19.17 224 21.27 0.78 (0.58–1.02) 0.110 
TC/CG 268 22.64 239 22.70 0.86 (0.64–1.15) 0.315 
TC/GG 77 6.50 81 7.69 0.73 (0.49–1.08) 0.113 
CC/CC 74 6.25 94 8.93 0.60 (0.41–0.89) 0.010 
CC/CG 110 9.29 95 9.02 0.89 (0.62–1.28) 0.522 
CC/GG 36 3.04 27 2.56 1.02(0.59–1.78) 0.936 
rs11614913/rs3746444 
TT/AA 283 23.98 200 18.99 1.00  
TT/AG 97 8.22 86 8.17 0.80 (0.57–1.12) 0.194 
TT/GG 11 0.93 0.66 1.11 (0.42–2.91) 0.831 
TC/AA 384 32.54 397 37.70 0.68 (0.54–0.86) 0.001 
TC/AG 166 14.07 137 13.01 0.86 (0.64–1.14) 0.294 
TC/GG 19 1.61 10 0.95 1.34 (0.61–2.95) 0.462 
CC/AA 147 12.46 160 15.19 0.65 (0.49–0.87) 0.003 
CC/AG 67 5.68 48 4.56 0.99 (0.65–1.49) 0.948 
CC/GG 0.51 0.76 0.53(0.18–1.55) 0.239 
rs2910164/rs3746444 
CC/AA 322 27.29 324 30.77 1.00  
CC/AG 124 10.51 108 10.26 1.16 (0.86–1.56) 0.346 
CC/GG 13 1.10 0.76 1.64 (0.67–4.00) 0.277 
CG/AA 374 31.69 320 30.89 1.18 (0.95–1.46) 0.139 
CG/AG 161 13.64 135 12.82 1.20 (0.91–1.58) 0.195 
CG/GG 18 1.53 12 1.14 1.51 (0.72–3.18) 0.277 
GG/AA 118 10.00 113 10.73 1.05 (0.78–1.42) 0.747 
GG/AG 45 3.81 28 2.66 1.62 (0.98–2.66) 0.056 
GG/GG 0.42 0.47 1.01 (0.29–3.51) 0.992 
rs11614913/rs2910164/rs3746444 
TT/CC/AA 114 9.66 86 8.17 1.00  
TT/CC/AG 41 3.47 35 3.32 0.88 (0.52–1.50) 0.648 
TT/CC/GG 0.34 0.09 3.02 (0.33–27.55) 0.304 
TT/CG/AA 128 10.85 89 8.45 1.08 (0.74–1.60) 0.681 
TT/CG/AG 44 3.73 40 3.80 0.83 (0.50–1.38) 0.475 
TT/CG/GG 0.42 0.38 0.94 (0.25–3.62) 0.932 
TT/GG/AA 41 3.47 25 2.37 1.24 (0.70–2.19) 0.464 
TT/GG/AG 12 1.02 11 1.04 0.82 (0.35–1.95) 0.658 
TT/GG/GG 0.17 0.19 0.75 (0.10–5.47) 0.780 
TC/CC/AA 155 13.14 167 15.86 0.70 (0.49–1.00) 0.049 
TC/CC/AG 64 5.42 54 5.13 0.89 (0.57–1.41) 0.632 
TC/CC/GG 0.59 0.28 1.76 (0.44–7.01) 0.417 
TC/CG/AA 174 14.75 163 15.48 0.81 (0.57–1.15) 0.228 
TC/CG/AG 83 7.03 71 6.74 0.88 (0.58–1.35) 0.560 
TC/CG/GG 0.76 0.47 1.36 (0.44–4.20) 0.594 
TC/GG/AA 55 4.66 67 6.36 0.62 (0.39–0.97) 0.038 
TC/GG/AG 19 1.61 12 1.14 1.19 (0.55–2.59) 0.653 
TC/GG/GG 0.25 0.19 1.13 (0.18–6.92) 0.894 
CC/CC/AA 53 4.49 71 6.74 0.56 (0.36–0.89) 0.013 
CC/CC/AG 19 1.61 19 1.80 0.75 (0.38–1.51) 0.426 
CC/CC/GG 0.17 0.38 0.38 (0.07–2.11) 0.250 
CC/CG/AA 72 6.10 68 6.46 0.80 (0.52–1.23) 0.310 
CC/CG/AG 34 2.88 24 2.28 1.07 (0.59–1.93) 0.826 
CC/CG/GG 0.34 0.28 1.01 (0.22–4.61) 0.994 
CC/GG/AA 22 1.86 21 1.99 0.79 (0.41–1.53) 0.484 
CC/GG/AG 14 1.19 0.47 2.26 (0.79–6.47) 0.119 
CC/GG/GG 0.0 0.09 0.25 (0.01–6.26) 0.251 

Values in bold are statistically significant (P<0.05).

Study power (α = 0.05) and FPRP mothed

For overall comparisons, these miR-SNPs did not confer a risk to NSCLC. Each power value for overall positive report was less than 0.8 (data not shown). For the comparison of miR-SNPs and NSCLC susceptibility in different types of pathology, we also could not confirm the positive report (data not shown). In stratification analysis of miR-SNPs with NSCLC susceptibility, we only confirmed that rs11614913 polymorphism could be a protective factor for the occurrence of NSCLC in the female subgroup (the power values were 0.809 in TC vs. TT and 0.848 in CC/TC vs. TT). In these miR-SNPs combination analysis, compared with rs11614913 TT/3746444 AA, rs11614913 TC/3746444 AA and rs11614913 CC/rs3746444 AA could decrease the susceptibility of NSCLC (power value: 0.912 and 0.836, respectively). Other power values less than 0.8 were not shown. After assessing power value and FPRP, we highlighted that miR-196a-2 rs11614913 decreased the risk to NSCLC in the female subgroup. As well, gene–gene interaction analysis showed that rs11614913 TC/3746444 AA and rs11614913 CC/rs3746444 AA could also reduce the susceptibility to NSCLC.

Discussion

LC is a common malignancy with 18.4% of overall cancer-related deaths worldwide [1]. The etiology of LC is not well-known. NSCLC is the most common subtype of LC. MiR is a negative regulator of gene expression. It may involve in the development of cancer. Some investigations have focused on the role of miRs on the occurrence and survival of NSCLC [40,48,49]. The individual’s hereditary factor may be implicated in the occurrence of NSCLC. In this investigation, we designed a study to identify the correlation of miR-SNPs (rs3746444, rs2910164 and rs11614913) with the risk of NSCLC in Chinese populations. We highlighted that rs11614913, in the female subgroup, could decrease the risk to NSCLC. As well, gene–gene interaction analysis showed that rs11614913 TC/3746444 AA and rs11614913 CC/rs3746444 AA could also reduce the susceptibility to NSCLC.

Rs11614913 locates on the 3p strand region of mature miR-196a-2 [50]. Thus, this locus could participate in the process of pre-miR maturation and affect the combination of miR-196a-2 with target genes [51]. Hu et al. reported that that the T>C variant in rs11614913 locus could alter the ability of mature hsa-mir-196a-2-3p binding to its target mRNA [35]. Therefore, this SNP could be used as an important biomarker for NSCLC prognosis [35]. A previous study has suggested that annexin A1 (ANXA1), a regulator of inflammation, could be regulated by miR-196a-2 [52]. A bioinformatics analysis suggested that the expression of ANXA1 could influence the survival of NSCLC cases [53]. Additionally, knockdown of ANXA1 could inhibit the invasion, migration and proliferation of NSCLC cells. Thus, miR-196a-2 could be implicated in the occurrence of cancer. Fang et al. reported that variants of rs11614913 could alter the response of LC case to platinum-based chemotherapy [23]. Toraih et al. found that individuals carrying the rs11614913 C allele might be a protective factor of LC, which was associated with miR-196a-2 low-expression in tissue [54]. A recent investigation indicated that the polymorphism of rs11614913, through influencing the level of miR-196a-2 and secondary structure, conferred risk to LC in females [36]. In the current invstigation, we found that the miR-196a-2 rs11614913 could reduce the susceptibility to NSCLC in female. In view of these investigations mentioned above, we might conclude that rs11614913 C allele could be a protective factor to the occurrence of NSCLC though altering the level of miR-196a-2 and secondary structure. It is well known that smoking is a major risk for LC. However, in the present study, we did not find the interaction of tobacco using and rs11614913 SNP with the development of NSCLC. In the future, these conclusions should be confirmed by further studies.

Several literatures have focused on the relationship between gene–gene interaction and the occurrence of human diseases [55–57]. In this study, we analyzed the combined effect of these miR-SNPs. Gene–gene interaction analyses showed that rs11614913 TC/3746444 AA and rs11614913 CC/rs3746444 AA could also decrease the susceptibility of NSCLC, which suggested that rs11614913 C allele could inhibit the development of NSCLC. We first confirmed that rs11614913 TC/3746444 AA and rs11614913 CC/rs3746444 AA combinations could decrease the risk of NSCLC. However, this combination did not influence the risk of cervical cancer [56]. Therefore, the effect of rs11614913 TC/3746444 AA combination could be different in different cancer. In the future, the possible correlation is needed to verify in other studies.

Several limitations, in this investigation, should be pointed out. Firstly, some vital data were unknown; thus, a more extensively stratified analysis for other risk factors (e.g., vegetable and fruit intake, air pollution, lifestyle and occupational exposure) could not be done. Second, due to the hospital-based study, bias might have happened in our analysis. Third, the number of participants in the present study was moderate. Last, we only included three miR-SNPs in the present study, and other important miR-SNPs should not be ignored.

In conclusion, the present study highlights that miR-196a-2 rs11614913 decreases the risk to NSCLC among female subgroup. Additionally, combined gene–gene analyses suggest that rs11614913 TC/3746444 AA and rs11614913 CC/rs3746444 AA are protective factors for the development of NSCLC. More investigations are needed to validate the potential effect of these miR-SNPs in NSCLC. And more functional studies should also be done.

Data Availability

Full data are available via an online supplementary material. Raw data of genotypes and characteristics were summarized in Supplementary Table S1. Supplementary Tables S2 and S3 summarized the detailed information and genotypes for SCC and non-SCC cases, respectively.

Competing Interests

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

Funding

This work was supported in part by the Fujian Provincial Health Technology Project [grant number 2018-CXB-4]; the Research Foundation for Senior Talents of Jiangsu University [grant number 16JDG066]; the National Natural Science Foundation of China [grant number 81472332]; and the Interdisciplinary Program of Shanghai Jiao Tong University [grant number YG2016MS79].

Author Contribution

Haiyong Gu and Qingfeng Zheng designed the study. Hao Qiu, Zhiqiang Xie, Weifeng Tang, Chao Liu and Yafeng Wang performed the experiments. Hao Qiu and Zhiqiang Xie analyzed the data. Hao Qiu drafted the manuscript and Haiyong Gu revised the manuscript.

Acknowledgements

We appreciate the help/participation of all people who participated in the present study.

Abbreviations

     
  • ANXA1

    annexin A1

  •  
  • BMI

    body mass index

  •  
  • CI

    confidence interval

  •  
  • EMT

    epithelial–mesenchymal transition

  •  
  • FPRP

    false-positive report probability

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • LC

    lung cancer

  •  
  • miR

    microRNA

  •  
  • NSCLC

    non-small cell lung cancer

  •  
  • OR

    odds ratio

  •  
  • PCR

    polymerase chain reaction

  •  
  • SCC

    squamous cell carcinoma

  •  
  • SNP

    single nucleotide polymorphism

  •  
  • 3′-UTR

    3′-untranslated region

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.
de Groot
P.
and
Munden
R.F.
(
2012
)
Lung cancer epidemiology, risk factors, and prevention
.
Radiol. Clin. North Am.
50
,
863
876
[PubMed]
4.
Yang
J.J.
,
Yu
D.
,
Xiang
Y.B.
et al.
(
2020
)
Association of dietary fiber and yogurt consumption with lung cancer risk: a pooled analysis
.
JAMA Oncol.
6
,
e194107
PMCID: PMC6813596
5.
Akhtar
N.
and
Bansal
J.G.
(
2017
)
Risk factors of lung cancer in nonsmoker
.
Curr. Prob. Cancer.
41
,
328
339
[PubMed]
6.
Han
J.
,
Lee
Y.
,
Yeom
K.H.
et al.
(
2006
)
Molecular basis for the recognition of primary microRNAs by the Drosha-DGCR8 complex
.
Cell
125
,
887
901
[PubMed]
7.
Urbanek-Trzeciak
M.O.
,
Jaworska
E.
and
Krzyzosiak
W.J.
(
2018
)
miRNAmotif-A tool for the prediction of Pre-miRNA(-) protein interactions
.
Int. J. Mol. Sci.
19
,
4075
PMCID: PMC6321451
8.
Gebert
L.F.R.
and
MacRae
I.J.
(
2019
)
Regulation of microRNA function in animals
.
Nat. Rev. Mol. Cell Biol.
20
,
21
37
,
PMCID: PMC6546304
[PubMed]
9.
McDonald
R.A.
,
Halliday
C.A.
,
Miller
A.M.
et al.
(
2015
)
Reducing in-stent restenosis: therapeutic manipulation of miRNA in vascular remodeling and inflammation
.
J. Am. Coll. Cardiol.
65
,
2314
2327
,
PMCID: PMC4444526
[PubMed]
10.
Afzal
T.A.
,
Luong
L.A.
,
Chen
D.
et al.
(
2016
)
NCK associated protein 1 modulated by miRNA-214 determines vascular smooth muscle cell migration, proliferation, and neointima hyperplasia
.
J. Am. Heart Assoc.
5
,
e00429
PMCID: PMC5210428
[PubMed]
11.
Maves
C.K.
,
Johnson
J.F.
,
Bove
K.
et al.
(
1989
)
Gastric inflammatory pseudotumor in children
.
Radiology
173
,
381
383
[PubMed]
12.
Jin
X.
,
Chen
D.
,
Zheng
R.H.
et al.
(
2017
)
miRNA-133a-UCP2 pathway regulates inflammatory bowel disease progress by influencing inflammation, oxidative stress and energy metabolism
.
World J. Gastroenterol.
23
,
76
86
PMCID: PMC5221288
[PubMed]
13.
Chen
G.
,
Umelo
I.A.
,
Lv
S.
et al.
(
2013
)
miR-146a inhibits cell growth, cell migration and induces apoptosis in non-small cell lung cancer cells
.
PLoS ONE
8
,
e60317
PMCID: PMC3608584
[PubMed]
14.
Park
D.H.
,
Jeon
H.S.
,
Lee
S.Y.
et al.
(
2015
)
MicroRNA-146a inhibits epithelial mesenchymal transition in non-small cell lung cancer by targeting insulin receptor substrate 2
.
Int. J. Oncol.
47
,
1545
1553
[PubMed]
15.
Li
Y.Y.
,
Zheng
X.H.
,
Deng
A.P.
et al.
(
2019
)
MiR-92b inhibited cells EMT by targeting Gabra3 and predicted prognosis of triple negative breast cancer patients
.
Eur. Rev. Med. Pharmacol. Sci.
23
,
10433
10442
[PubMed]
16.
Han
S.
,
Shi
Y.
,
Sun
L.
et al.
(
2019
)
MiR-4319 induced an inhibition of epithelial-mesenchymal transition and prevented cancer stemness of HCC through targeting FOXQ1
.
Int. J. Biol. Sci.
15
,
2936
2947
PMCID: PMC6909970
17.
Petrek
H.
and
Yu
A.M.
(
2019
)
MicroRNAs in non-small cell lung cancer: gene regulation, impact on cancer cellular processes, and therapeutic potential
.
Pharmacol. Res. Perspect.
7
,
e00528
18.
Zou
Y.
,
Jing
C.
,
Liu
L.
et al.
(
2019
)
Serum microRNA-135a as a diagnostic biomarker in non-small cell lung cancer
.
Medicine (Baltimore)
98
,
e17814
[PubMed]
19.
Tang
W.
,
Wang
Y.
,
Pan
H.
et al.
(
2019
)
Association of miRNA-499 rs3746444 A>G variants with adenocarcinoma of esophagogastric junction (AEG) risk and lymph node status
.
Onco Targets Ther.
12
,
6245
6252
PMCID: PMC6690596
[PubMed]
20.
Chen
Y.
,
Tang
W.
,
Liu
C.
et al.
(
2018
)
miRNA-146a rs2910164 C>G polymorphism increased the risk of esophagogastric junction adenocarcinoma: a case-control study involving 2,740 participants
.
Cancer Manag. Res.
10
,
1657
1664
PMCID: PMC6025765
21.
Ahmad
M.
,
Ahmad
S.
,
Rahman
B.
et al.
(
2019
)
Association of MIR146A rs2910164 variation with a predisposition to sporadic breast cancer in a Pakistani cohort
.
Ann. Hum. Genet.
83
,
325
330
[PubMed]
22.
Wang
S.
,
Zhu
H.
,
Ding
B.
et al.
(
2019
)
Genetic variants in microRNAs are associated with cervical cancer risk
.
Mutagenesis
34
,
127
133
[PubMed]
23.
Fang
C.
,
Li
X.P.
,
Chen
Y.X.
et al.
(
2018
)
Functional miRNA variants affect lung cancer susceptibility and platinum-based chemotherapy response
.
J. Thorac.Dis.
10
,
3329
3340
PMCID: PMC6051820
24.
Zhang
W.
,
Xiao
J.
,
Lu
X.
et al.
(
2019
)
PVT1 (rs13281615) and miR-146a (rs2910164) polymorphisms affect the prognosis of colon cancer by regulating COX2 expression and cell apoptosis
.
J. Cell. Physiol.
234
,
17538
17548
[PubMed]
25.
Dai
Z.M.
,
Lv
J.R.
,
Liu
K.
et al.
(
2018
)
The role of microRNA-608 polymorphism on the susceptibility and survival of cancer: a meta-analysis
.
Aging
10
,
1402
1414
PMCID: PMC6046227
[PubMed]
26.
Xiong
X.D.
,
Cho
M.
,
Cai
X.P.
et al.
(
2014
)
A common variant in pre-miR-146 is associated with coronary artery disease risk and its mature miRNA expression
.
Mutat. Res.
761
,
15
20
[PubMed]
27.
Alipoor
B.
,
Ghaedi
H.
,
Meshkani
R.
et al.
(
2018
)
The rs2910164 variant is associated with reduced miR-146a expression but not cytokine levels in patients with type 2 diabetes
.
J. Endocrinol. Invest.
41
,
557
566
[PubMed]
28.
Fang
C.
,
Li
X.P.
,
Gong
W.J.
et al.
(
2017
)
Age-related common miRNA polymorphism associated with severe toxicity in lung cancer patients treated with platinum-based chemotherapy
.
Clin. Exp. Pharmacol. Physiol.
44
,
21
29
[PubMed]
29.
Yin
Z.
,
Cui
Z.
,
Ren
Y.
et al.
(
2017
)
MiR-146a polymorphism correlates with lung cancer risk in Chinese nonsmoking females
.
Oncotarget
8
,
2275
2283
PMCID: PMC5356798
[PubMed]
30.
Yin
Z.
,
Cui
Z.
,
Ren
Y.
et al.
(
2016
)
Association between polymorphisms in pre-miRNA genes and risk of lung cancer in a Chinese non-smoking female population
.
Lung Cancer
94
,
15
21
[PubMed]
31.
Yin
Z.
,
Cui
Z.
,
Guan
P.
et al.
(
2015
)
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
10
,
e0128572
PMCID: PMC4471348
[PubMed]
32.
Parlayan
C.
,
Ikeda
S.
,
Sato
N.
et al.
(
2014
)
Association analysis of single nucleotide polymorphisms in miR-146a and miR-196a2 on the prevalence of cancer in elderly Japanese: a case-control study
.
Asian Pac. J. Cancer Prev.
15
,
2101
2107
[PubMed]
33.
Rahim
A.
,
Afzal
M.
and
Naveed
A.K.
(
2019
)
Genetic polymorphism of miRNA-196a and its target gene annexin-A1 expression based on ethnicity in Pakistani female breast cancer patients
.
Pakistan J. Med. Sci.
35
,
1598
1604
PMCID: PMC6861506
34.
Farokhizadeh
Z.
,
Dehbidi
S.
,
Geramizadeh
B.
et al.
(
2019
)
Association of microRNA polymorphisms with hepatocellular carcinoma in an Iranian Population
.
Ann. Lab. Med.
39
,
58
66
PMCID: PMC6143471
35.
Hu
Z.
,
Chen
J.
,
Tian
T.
et al.
(
2008
)
Genetic variants of miRNA sequences and non-small cell lung cancer survival
.
J. Clin. Invest.
118
,
2600
2608
PMCID: PMC2402113
[PubMed]
36.
Yin
Z.
,
Cui
Z.
,
Ren
Y.
et al.
(
2017
)
MiR-196a2 and lung cancer in Chinese non-smoking females: a genetic association study and expression analysis
.
Oncotarget
8
,
70890
70898
PMCID: PMC5642605
[PubMed]
37.
He
F.
,
Lin
J.
,
Yu
T.
et al.
(
2016
)
Interaction research on smoking and microRNA genes SNP related to lung cancer in Fujian Han population
.
Zhonghua Yu Fang Yi Xue Za Zhi
50
,
168
174
[PubMed]
38.
Yuan
Z.
,
Zeng
X.
,
Yang
D.
et al.
(
2013
)
Effects of common polymorphism rs11614913 in Hsa-miR-196a2 on lung cancer risk
.
PLoS ONE
8
,
e61047
PMCID: PMC3625214
[PubMed]
39.
Ding
W.
,
Li
M.
,
Sun
T.
et al.
(
2018
)
A polymorphism rs3746444 within the pre-miR-499 alters the maturation of miR-499-5p and its antiapoptotic function
.
J. Cell. Mol. Med.
22
,
5418
5428
PMCID: PMC6201352
[PubMed]
40.
Ge
N.
,
Mao
C.
,
Yang
Q.
et al.
(
2019
)
Single nucleotide polymorphism rs3746444 in miR499a affects susceptibility to nonsmall cell lung carcinoma by regulating the expression of CD200
.
Int. J. Mol. Med.
43
,
2221
2229
[PubMed]
41.
Li
D.
,
Zhu
G.
,
Di
H.
et al.
(
2016
)
Associations between genetic variants located in mature microRNAs and risk of lung cancer
.
Oncotarget
7
,
41715
41724
PMCID: PMC5173090
[PubMed]
42.
Fan
X.
and
Wu
Z.
(
2014
)
Effects of four single nucleotide polymorphisms in microRNA-coding genes on lung cancer risk
.
Tumour Biol.
35
,
10815
10824
[PubMed]
43.
Chen
Z.
,
Xu
L.
,
Ye
X.
et al.
(
2013
)
Polymorphisms of microRNA sequences or binding sites and lung cancer: a meta-analysis and systematic review
.
PLoS ONE
8
,
e61008
PMCID: PMC3628762
[PubMed]
44.
Yang
X.
,
Li
X.
and
Zhou
B.
(
2018
)
A meta-analysis of miR-499 rs3746444 polymorphism for cancer risk of different systems: evidence from 65 case-control studies
.
Front. Physiol.
9
,
737
PMCID: PMC6005882
[PubMed]
45.
Tang
W.
,
Wang
Y.
,
Chen
S.
et al.
(
2016
)
Investigation of cytotoxic T-lymphocyte antigen 4 polymorphisms in gastric cardia adenocarcinoma
.
Scand. J. Immunol.
83
,
212
218
[PubMed]
46.
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
PMCID: PMC3862673
[PubMed]
47.
He
J.
,
Wang
M.Y.
,
Qiu
L.X.
et al.
(
2013
)
Genetic variations of mTORC1 genes and risk of gastric cancer in an Eastern Chinese population
.
Mol. Carcinog.
52
,
E70
E79
[PubMed]
48.
Li
C.
,
Zhang
Y.
,
Li
Y.
et al.
(
2018
)
The association of polymorphisms in miRNAs with nonsmall cell lung cancer in a Han Chinese population
.
Cancer Manag. Res.
10
,
697
704
PMCID: PMC5901134
49.
Wu
S.
,
Shen
W.
,
Pan
Y.
et al.
(
2015
)
Genetic variations in key microRNAs are associated with the survival of nonsmall cell lung cancer
.
Medicine (Baltimore)
94
,
e2084
PMCID: PMC5058987
[PubMed]
50.
Hu
Z.
,
Liang
J.
,
Wang
Z.
et al.
(
2009
)
Common genetic variants in pre-microRNAs were associated with increased risk of breast cancer in Chinese women
.
Hum. Mutat.
30
,
79
84
[PubMed]
51.
Landgraf
P.
,
Rusu
M.
,
Sheridan
R.
et al.
(
2007
)
A mammalian microRNA expression atlas based on small RNA library sequencing
.
Cell
129
,
1401
1414
PMCID: PMC2681231
[PubMed]
52.
Luthra
R.
,
Singh
R.R.
,
Luthra
M.G.
et al.
(
2008
)
MicroRNA-196a targets annexin A1: a microRNA-mediated mechanism of annexin A1 downregulation in cancers
.
Oncogene
27
,
6667
6678
[PubMed]
53.
Fang
Y.
,
Guan
X.
,
Cai
T.
et al.
(
2016
)
Knockdown of ANXA1 suppresses the biological behavior of human NSCLC cells in vitro
.
Mol. Med. Rep.
13
,
3858
3866
PMCID: PMC4838122
[PubMed]
54.
Toraih
E.A.
,
Fawzy
M.S.
,
Mohammed
E.A.
et al.
(
2016
)
MicroRNA-196a2 biomarker and targetome network analysis in solid tumors
.
Mol. Diagn. Ther.
20
,
559
577
55.
Rah
H.
,
Jeon
Y.J.
,
Shim
S.H.
et al.
(
2013
)
Association of miR-146aC>G, miR-196a2T>C, and miR-499A>G polymorphisms with risk of premature ovarian failure in Korean women
.
Reprod. Sci.
20
,
60
68
56.
Thakur
N.
,
Singhal
P.
,
Mehrotra
R.
et al.
(
2019
)
Impacts of single nucleotide polymorphisms in three microRNAs (miR-146a, miR-196a2 and miR-499) on the susceptibility to cervical cancer among Indian women
.
Biosci. Rep.
39
,
BSR20180723
PMCID: PMC6465206
57.
Qiu
H.
,
Chen
Z.
,
Lv
L.
et al.
(
2020
)
Associations between microRNA polymorphisms and development of coronary artery disease: a case-control study
.
DNA Cell Biol.
39
,
25
36

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