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.
Keywords: microRNA, non-small cell lung cancer, polymorphism, susceptibility
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.
| Variable | NSCLC 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 SNPs | miR-146a rs2910164 C>G | miR-196a-2 rs11614913 T>C | miR-499a 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 |
| 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% |
MAF, minor allele frequency.
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.
| Genotype | Overall 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 | 5 | 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.
| Genotype | Overall 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) | P | Adjusted OR1 (95% CI) | P | Crude OR (95% CI) | P | Adjusted OR1 (95% CI) | P | Crude OR (95% CI) | P | Adjusted 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.
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.
| Variable | miRNA-146a rs2910164 C>G (case/control)1 | Adjusted OR2 (95% CI); P | |||||
|---|---|---|---|---|---|---|---|
| CC | CG | GG | CG vs. CC | GG vs. CC | GG/CG vs. CC | GG 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 |
For miRNA-146a rs2910164 C>G, the genotyping was successful in 1184 (99.25%) NSCLC cases and 1053 (99.72%) controls.
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.
| Variable | miRNA-499a rs3746444 A>G (case/control)1 | Adjusted OR2 (95% CI); P | |||||
|---|---|---|---|---|---|---|---|
| AA | AG | GG | AG vs. AA | GG vs. AA | GG/AG vs. AA | GG 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 |
For miR-499a rs3746444 A>G, the genotyping was successful in 1180 (98.91%) NSCLC cases and 1053 (99.72%) controls.
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.
| Variable | miR-196a-2 rs11614913 T>C (case/control)1 | Adjusted OR2 (95% CI); P | |||||
|---|---|---|---|---|---|---|---|
| TT | TC | CC | TC vs. TT | CC vs. TT | CC/TC vs. TT | CC 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 (0.53–1.11); 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 (0.67–1.07); P: 0.151 |
| Ever | 80/29 | 116/43 | 48/16 | 0.97 (0.55–1.70); P: 0.908 | 1.19 (0.58–2.45); P: 0.640 | 1.03 (0.61–1.74); P: 0.923 | 1.21 (0.64–2.30); P: 0.558 |
| BMI (kg/m2) | |||||||
| <24 | 258/165 | 382/282 | 154/122 | 0.83 (0.64–1.08); P: 0.167 | 0.82 (0.59–1.12); P: 0.207 | 0.83 (0.65–1.06); P: 0.128 | 0.91 (0.69–1.20); P: 0.505 |
| ≥24 | 134/128 | 190/262 | 66/94 | 0.75 (0.55–1.03); P: 0.079 | 0.70 (0.47–1.07); P: 0.097 | 0.74 (0.55–1.00); P: 0.051 | 0.84 (0.59–1.21); P: 0.358 |
For miR-196a-2 rs11614913 T>C, the genotyping was successful in 1184 (99.25%) NSCLC cases and 1053 (99.72%) controls.
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.
| Genotype | Case | Control | OR (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 | 7 | 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 | 6 | 0.51 | 8 | 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 | 8 | 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 | 5 | 0.42 | 5 | 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 | 4 | 0.34 | 1 | 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 | 5 | 0.42 | 4 | 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 | 2 | 0.17 | 2 | 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 | 7 | 0.59 | 3 | 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 | 9 | 0.76 | 5 | 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 | 3 | 0.25 | 2 | 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 | 2 | 0.17 | 4 | 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 | 4 | 0.34 | 3 | 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 | 5 | 0.47 | 2.26 (0.79–6.47) | 0.119 |
| CC/GG/GG | 0 | 0.0 | 1 | 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.
Supplementary Material
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
Contributor Information
Haiyong Gu, Email: haiyong_gu@hotmail.com.
Qingfeng Zheng, Email: qingfeng_zheng@163.com.
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.
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 10.3322/caac.21492 [DOI] [PubMed] [Google Scholar]
- 2.Chen W., Zheng R., Baade P.D.et al. (2016) Cancer statistics in China, 2015. CA Cancer J. Clin. 66, 115–132 10.3322/caac.21338 [DOI] [PubMed] [Google Scholar]
- 3.de Groot P. and Munden R.F. (2012) Lung cancer epidemiology, risk factors, and prevention. Radiol. Clin. North Am. 50, 863–876 10.1016/j.rcl.2012.06.006 [DOI] [PubMed] [Google Scholar]
- 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 10.1001/jamaoncol.2019.4107 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Akhtar N. and Bansal J.G. (2017) Risk factors of lung cancer in nonsmoker. Curr. Prob. Cancer. 41, 328–339 10.1016/j.currproblcancer.2017.07.002 [DOI] [PubMed] [Google Scholar]
- 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 10.1016/j.cell.2006.03.043 [DOI] [PubMed] [Google Scholar]
- 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 10.3390/ijms19124075 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1038/s41580-018-0045-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1016/j.jacc.2015.03.549 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1161/JAHA.116.004629 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Maves C.K., Johnson J.F., Bove K.et al. (1989) Gastric inflammatory pseudotumor in children. Radiology 173, 381–383 10.1148/radiology.173.2.2678252 [DOI] [PubMed] [Google Scholar]
- 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, 76–86PMCID: PMC5221288 10.3748/wjg.v23.i1.76 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1371/journal.pone.0060317 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.3892/ijo.2015.3111 [DOI] [PubMed] [Google Scholar]
- 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 [DOI] [PubMed] [Google Scholar]
- 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, 2936–2947PMCID: PMC6909970 10.7150/ijbs.38000 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1097/MD.0000000000017814 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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, 6245–6252PMCID: PMC6690596 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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, 1657–1664PMCID: PMC6025765 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1111/ahg.12316 [DOI] [PubMed] [Google Scholar]
- 22.Wang S., Zhu H., Ding B.et al. (2019) Genetic variants in microRNAs are associated with cervical cancer risk. Mutagenesis 34, 127–133 10.1093/mutage/gez005 [DOI] [PubMed] [Google Scholar]
- 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, 3329–3340PMCID: PMC6051820 10.21037/jtd.2018.05.145 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1002/jcp.28377 [DOI] [PubMed] [Google Scholar]
- 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, 1402–1414PMCID: PMC6046227 10.18632/aging.101476 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1016/j.mrfmmm.2014.01.001 [DOI] [PubMed] [Google Scholar]
- 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 10.1007/s40618-017-0766-z [DOI] [PubMed] [Google Scholar]
- 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 10.1111/1440-1681.12704 [DOI] [PubMed] [Google Scholar]
- 29.Yin Z., Cui Z., Ren Y.et al. (2017) MiR-146a polymorphism correlates with lung cancer risk in Chinese nonsmoking females. Oncotarget 8, 2275–2283PMCID: PMC5356798 10.18632/oncotarget.13722 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1016/j.lungcan.2016.01.013 [DOI] [PubMed] [Google Scholar]
- 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 10.1371/journal.pone.0128572 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.7314/APJCP.2014.15.5.2101 [DOI] [PubMed] [Google Scholar]
- 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, 1598–1604PMCID: PMC6861506 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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, 58–66PMCID: PMC6143471 10.3343/alm.2019.39.1.58 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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, 2600–2608PMCID: PMC2402113 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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, 70890–70898PMCID: PMC5642605 10.18632/oncotarget.20174 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 [DOI] [PubMed] [Google Scholar]
- 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 10.1371/journal.pone.0061047 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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, 5418–5428PMCID: PMC6201352 10.1111/jcmm.13813 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 [DOI] [PubMed] [Google Scholar]
- 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, 41715–41724PMCID: PMC5173090 10.18632/oncotarget.9566 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1007/s13277-014-2371-5 [DOI] [PubMed] [Google Scholar]
- 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 10.1371/journal.pone.0061008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.3389/fphys.2018.00737 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1111/sji.12409 [DOI] [PubMed] [Google Scholar]
- 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 10.1371/journal.pone.0082790 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1002/mc.22013 [DOI] [PubMed] [Google Scholar]
- 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, 697–704PMCID: PMC5901134 10.2147/CMAR.S154040 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1097/MD.0000000000002084 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1002/humu.20837 [DOI] [PubMed] [Google Scholar]
- 51.Landgraf P., Rusu M., Sheridan R.et al. (2007) A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129, 1401–1414PMCID: PMC2681231 10.1016/j.cell.2007.04.040 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1038/onc.2008.256 [DOI] [PubMed] [Google Scholar]
- 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, 3858–3866PMCID: PMC4838122 10.3892/mmr.2016.5022 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1007/s40291-016-0223-2 [DOI] [PubMed] [Google Scholar]
- 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 [DOI] [PubMed] [Google Scholar]
- 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 10.1042/BSR20180723 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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 10.1089/dna.2019.4963 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
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.