Abstract
Background
MicroRNAs (miRNAs) have been demonstrated to play important roles in cancer progression. Recently, studies have revealed that polymorphisms in miRNAs might be associated with cancer susceptibility.
Materials and methods
In the current study, we investigated the associations of single nucleotide polymorphisms (SNPs) in miRNAs (rs11134527 in pri-miR-218-2, rs74693964 in pri-miR-145, rs6062251 in pri-miR-133a-2, and rs4705343 in pri-miR-143) with nonsmall cell lung cancer (NSCLC) in a Han population from Yunnan Province, Southwest China using a binary logistic regression analysis. A total of 452 patients with NSCLC and 452 healthy individuals were recruited for polymorphism genotyping using the TaqMan assay.
Results
Our results showed that the allelic frequencies of rs11134527 and rs4705343 were significantly different between the NSCLC and control groups (P=0.025 and 0.029). Additionally, the genotypic frequencies of rs11134527 were significantly different between the NSCLC and control groups (P=0.045). The mode of inheritance analysis showed that genotypes A/G+G/G of rs11134527 were associated with a lower risk of NSCLC under the dominant model (OR=0.69; 95% CI: 0.51–0.94). In addition, genotypes 2C/C+C/T of rs4705343 were associated with an increased risk of NSCLC under the log-additive model (OR=1.25; 95% CI: 1.01–1.53). However, there was no significant difference in the other SNPs between the NSCLC and control groups (P>0.05). Moreover, the association analysis of these SNPs between adenocarcinoma and squamous cell carcinoma (SCC) showed that allele A of rs11134527 was associated with SCC (OR=0.65; 95% CI: 0.48–0.88).
Conclusion
Our results indicated that the A allele of rs11134527 might be a risk factor (OR=1.24; 95% CI: 1.03–1.50) and that the T allele of rs4705343 might be a protective factor (OR=0.80; 95% CI: 0.66–0.98) for NSCLC in a Han Chinese population.
Keywords: microRNA, single nucleotide polymorphism, lung cancer, Chinese population, genetic variation
Introduction
Lung cancer is one of the most common cancers and is the leading cause of cancer-related deaths worldwide. There were 1.8 million new diagnoses of lung cancer in 2012, accounting for 13% of the global cancer burden.1 The incidence and mortality rates of lung cancer in China were estimated to be 0.7 and 0.6 million cases in 2015.2 Although smoking is the key risk factor for lung cancer, there are lung cancer patients without a smoking history.3 An increasing number of studies have proven that genetic factors play a key role in the development of nonsmall cell lung cancer (NSCLC), which accounts for approximately 80% of lung cancer cases.
MicroRNAs (miRNAs) are a group of short RNAs with 20–23 noncoding nucleotides that play important modulatory roles in various biological processes through posttranscriptional regulation. It is estimated that miRNAs regulate more than 30% of all human genes4,5 and are involved in crucial physiological processes, such as proliferation, apoptosis, differentiation, tumorigenesis, and cancer metastasis.5–8 Recently, studies have indicated the aberrant expression of miRNAs in human cancers,9–11 including lung cancer.12 Moreover, Sheervalilou et al13 showed that aberrant expression of miRNAs in lung cancer might induce lung tumorigenesis. On the other hand, functional studies have shown that single nucleotide polymorphisms (SNPs) in miRNA genes may be associated with miRNA expression14,15 and target gene expression.16 Moreover, several studies have shown that SNPs in miRNA are associated with human cancers,17–21 for example, in 2014, Wang et al reported that rs3746444 in miR-499a and rs11614913 in miR-196a2 were associated with lung cancer.22
In the current study, we evaluated the association of SNPs (rs11134527 in pri-miR-218-2; rs74693964 in pri-miR-145; rs6062251 in pri-miR-133a-2; rs4705343 in pri-miR-143) in miRNA genes with NSCLC in a Han Chinese population. Our results revealed the role of genetic variations in the miRNA genes in the development of NSCLC in a Han Chinese population.
Materials and methods
Ethics statement
This study was approved by the Institutional Review Board of the No. 1 and No. 3 Affiliated Hospitals of Kunming Medical University. The methods used in this investigation were in accordance with the approved guidelines and the principles expressed in the Helsinki Declaration of 1975, which was revised in 2008. All participants provided written informed consent.
Subjects
The NSCLC group included 452 patients (308 males and 144 females) who were diagnosed with NSCLC at the No. 1 and No. 3 Affiliated Hospitals of Kunming Medical University from July 2014 to May 2016. The histological type of NSCLC was identified according to the World Health Organization (WHO 2004) classifications.23 The pathologic stage was determined according to the International System for Staging Lung Cancer.24 Based on the pathomorphological reports, the NSCLC cases included adenocarcinoma (AC), squamous cell carcinoma (SCC), and both AC and SCC. NSCLC patients with a prior history of primary cancer other than lung cancer were excluded from the current study. Clinical characteristics and data, such as gender, age, family history of cancer, and histological type of cancer, were obtained. The healthy control group included 452 subjects (318 males and 134 females) who had no family history of NSCLC and were recruited from a population undergoing routine health checkups at the No. 1 and No. 3 Affiliated Hospitals of Kunming Medical University. All participants self-reported as ethnic Hans and lived within approximately the same geographic region (Yunnan Province, Southwest China).
SNP genotyping
Genomic DNA was extracted from peripheral lymphocytes using the QIAamp Blood Mini Kit (Qiagen, Hilden, Germany). Four SNPs in miRNA genes, namely, rs11134527, rs74693964, rs6062251, and rs4705343, were genotyped using PCR amplification with a TaqMan assay. Primers and probes were purchased from Applied Biosystems (Foster City, CA, USA). To identify the accuracy of SNP genotyping by the TaqMan assay, three positive controls and one negative control were genotyped via TaqMan assay at the same time.
Statistical analysis
SPSS 19.0 (IBM Corporation, Armonk, NY, USA) and Microsoft Excel were used to conduct the statistical analyses. The Student’s t-test or one-way analysis of variance was used to test the quantitative variables, and the categorical data were compared by chi-square test. The allelic and genotypic frequencies of the four SNPs were calculated by the direct counting method. Hardy–Weinberg equilibrium (HWE) was tested for the SNPs in both the NSCLC and control groups. The genetic association between the SNPs in miRNA genes and NSCLC susceptibility was calculated using a binary logistic regression with gender and age as the covariates, and the ORs with the associated 95% CIs of allele-specific risks were also calculated. In addition, the Hosmer–Lemeshow test and the Nagelkerke R2 were employed to establish the optimal model. The association between genotypes of the SNPs and NSCLC was analyzed using the inheritance model analysis of SNPStats software.25 Five models including the codominant, dominant, recessive, overdominant, and log-additive were analyzed. The best-fit inheritance model was determined using the Akaike information criterion and Bayesian information criterion, which possesses the minimal Akaike information criterion and Bayesian information criterion values. A P value less than 0.05 was considered statistically significant.
Results
Subject characteristics
Table 1 lists the characteristics of the enrolled subjects. There were no age or gender differences between the NSCLC and control groups (P>0.05). In the NSCLC individuals, 271 had AC, 169 had SCC, and 12 had both AC and SCC. There were 70 patients in pathological stage I, 77 in stage II, 161 in stage III, and 144 in stage IV.
Table 1.
Clinical characteristics of the subjects enrolled in the current study
| Characteristics | NSCLC | Control | P-value |
|---|---|---|---|
| N | 452 | 452 | |
| Ages, mean ± SD (years) | 55.71±10.01 | 55.96±10.72 | 0.112 |
| Gender (male/female) | 308/144 | 318/134 | 0.471 |
| Adenocarcinoma | 271 (59.9%) | ||
| Squamous cell carcinoma | 169 (37.4%) | ||
| Adenocarcinoma and squamous cell carcinoma | 12 (2.7%) | ||
| Clinical stage | |||
| I | 70 (15.5%) | ||
| II | 77 (17.0%) | ||
| III | 161 (35.6%) | ||
| IV | 144 (31.9%) |
Abbreviation: NSCLC, nonsmall cell lung cancer.
Association of the four SNPs with NSCLC
The allelic and genotypic frequencies of the four SNPs in the miRNAs are displayed in Table 2. Genotypic frequencies of the four SNPs were in HWE for the case and control groups (P>0.05). The allelic and genotypic frequencies of rs11134527 were significantly different in the NSCLC and control groups (P=0.025 and 0.045, respectively). The A allele of rs11134527 occurred more frequently in the NSCLC group compared with the control group (OR=1.24; 95% CI: 1.03–1.50). The allelic frequencies for rs4705343 were significantly different between the NSCLC and control groups (P=0.029). The frequency of the A allele for rs4705343 was significantly higher in the control group than that in the NSCLC group (OR=0.80; 95% CI: 0.66–0.98). The allelic and genotypic frequencies of rs74693964 and rs6062251 showed no significant difference between the NSCLC and control groups (P>0.05).
Table 2.
Comparison of genotypic and allelic distribution of the four SNPs between NSCLC and control groups (after adjusting for gender and age)
| SNP | Genotype, n (%) | P-value | Allele, n (%) | P-value | OR (95% CI) | |||
|---|---|---|---|---|---|---|---|---|
| rs11134527 | A/A | A/G | G/G | A | G | |||
| NSCLC | 181 (40.0) | 195 (43.1) | 76 (16.8) | 0.045 | 557 (61.6) | 347 (38.4) | 0.025 | 1.24 (1.03–1.50) |
| Control | 145 (32.1) | 220 (48.7) | 87 (19.2) | 510 (56.4) | 394 (43.6) | |||
| rs74693964 | C/C | C/T | T/T | C | T | |||
| NSCLC | 422 (93.4) | 30 (6.6) | 0 (0.0) | 0.264 | 874 (96.7) | 30 (3.3) | 0.319 | 0.76 (0.44–1.31) |
| Control | 430 (95.1) | 21 (4.6) | 1 (0.2) | 881 (97.5) | 23 (2.5) | |||
| rs6062251 | C/C | C/T | T/T | C | T | |||
| NSCLC | 43 (9.5) | 217 (48.0) | 192 (42.5) | 0.338 | 303 (33.5) | 601 (66.5) | 0.395 | 1.90 (0.89–1.33) |
| Control | 46 (10.2) | 195 (43.1) | 211 (46.7) | 287 (31.7) | 617 (68.3) | |||
| rs4705343 | T/T | T/C | C/C | T | C | |||
| NSCLC | 204 (45.1) | 196 (43.4) | 52 (11.5) | 0.092 | 604 (66.8) | 300 (33.2) | 0.029 | 0.80 (0.66–0.98) |
| Control | 232 (51.3) | 183 (40.5) | 37 (8.2) | 647 (71.6) | 257 (28.4) | |||
Abbreviations: NSCLC, nonsmall cell lung cancer; SNP, single nucleotide polymorphism.
Model of inheritance analysis of the four SNPs with NSCLC
Tables 3–6 present the results of the analyses of the different inheritance model for the four SNPs. The genotypic frequency of rs11134527 A/G+G/G was significantly different from A/A (P=0.011) under the dominant model between the NSCLC and control groups. The A/G+GG genotype was associated with a decreased risk of NSCLC (OR=0.69; 95% CI: 0.52–0.92). The genotypic frequencies of rs4705343 were significantly different under the log-additive model between the NSCLC and control groups (P=0.036). The 2CC+T/C genotype was associated with a higher risk of NSCLC (OR=1.25; 95% CI: 1.01–1.53).
Table 3.
Inheritance analysis of rs11134527 in pri-miR-218-2 between NSCLC and control groups (after adjusting for gender and age)
| Model | Genotype | Control, n (%) | NSCLC, n (%) | OR (95% CI) | P-value | AIC | BIC |
|---|---|---|---|---|---|---|---|
| Codominant | A/A | 145 (32.1) | 181 (40) | 1.00 | 0.039 | 1334.6 | 1603.7 |
| A/G | 220 (48.7) | 195 (43.1) | 0.69 (0.51–0.94) | ||||
| G/G | 87 (19.2) | 76 (16.8) | 0.69 (0.46–1.02) | ||||
| Dominant | A/A | 145 (32.1) | 181 (40) | 1.00 | 0.011 | 1332.6 | 1596.9 |
| A/G-G/G | 307 (67.9) | 271 (60) | 0.69 (0.52–0.92) | ||||
| Recessive | A/A-A/G | 365 (80.8) | 376 (83.2) | 1.00 | 0.340 | 1338.1 | 1602.5 |
| G/G | 87 (19.2) | 76 (16.8) | 0.84 (0.59–1.20) | ||||
| Overdominant | A/A-G/G | 232 (51.3) | 257 (56.9) | 1.00 | 0.086 | 1336.1 | 1600.5 |
| A/G | 220 (48.7) | 195 (43.1) | 0.79 (0.60–1.04) | ||||
| Log-additive | – | – | – | 0.81 (0.66–0.98) | 0.026 | 1334.1 | 1598.5 |
Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; NSCLC, nonsmall cell lung cancer.
Table 4.
Inheritance analysis of 74693964 in pri-miR-145 between NSCLC and control groups (after adjusting for gender and age)
| Model | Genotype | Control, n (%) | NSCLC, n (%) | OR (95% CI) | P-value | AIC | BIC |
|---|---|---|---|---|---|---|---|
| Codominant | C/C | 430 (95.1) | 422 (93.4) | 1.00 | 0.110 | 1336.6 | 1605.8 |
| C/T | 21 (4.7) | 30 (6.6) | 1.66 (0.91–3.05) | ||||
| T/T | 1 (0.2) | 0 (0.0) | 0.00 (0.00–NA) | ||||
| Dominant | C/C | 430 (95.1) | 422 (93.4) | 1.00 | 0.130 | 1336.8 | 1601.2 |
| C/T-T/T | 22 (4.9) | 30 (6.6) | 1.57 (0.87–2.86) | ||||
| Recessive | C/C-C/T | 451 (99.8) | 452 (100) | 1.00 | 0.200 | 1337.4 | 1601.8 |
| T/T | 1 (0.2) | 0 (0.0) | 0.00 (0.00–NA) | ||||
| Overdominant | C/C-T/T | 431 (95.3) | 422 (93.4) | 1.00 | 0.094 | 1336.2 | 1600.6 |
| C/T | 21 (4.7) | 30 (6.6) | 1.67 (0.91–3.06) | ||||
| Log-additive | – | – | – | 1.47 (0.82–2.62) | 0.190 | 1337.4 | 1601.7 |
Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; NSCLC, nonsmall cell lung cancer.
Table 5.
Inheritance analysis of rs6062251 in pri-miR-133a-2 between NSCLC and control groups (after adjusting for gender and age)
| Model | Genotype | Control, n (%) | NSCLC, n (%) | OR (95% CI) | P-value | AIC | BIC |
|---|---|---|---|---|---|---|---|
| Codominant | T/T | 211 (46.7) | 192 (42.5) | 1.00 | 0.340 | 1338.9 | 1608.1 |
| C/T | 195 (43.1) | 217 (48) | 1.23 (0.92–1.64) | ||||
| C/C | 46 (10.2) | 43 (9.5) | 1.01 (0.63–1.63) | ||||
| Dominant | T/T | 211 (46.7) | 192 (42.5) | 1.00 | 0.220 | 1337.5 | 1601.9 |
| C/T-C/C | 241 (53.3) | 260 (57.5) | 1.19 (0.90–1.56) | ||||
| Recessive | T/T-C/T | 406 (89.8) | 409 (90.5) | 1.00 | 0.690 | 1338.9 | 1603.3 |
| C/C | 46 (10.2) | 43 (9.5) | 0.91 (0.58–1.44) | ||||
| Overdominant | T/T-C/C | 257 (56.9) | 235 (52) | 1.00 | 0.140 | 1336.9 | 1601.3 |
| C/T | 195 (43.1) | 217 (48) | 1.23 (0.93–1.61) | ||||
| Log-additive | – | – | – | 1.08 (0.88–1.34) | 0.450 | 1260.4 | 1279.6 |
Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; NSCLC, nonsmall cell lung cancer.
Table 6.
Inheritance analysis of rs4705343 in pri-miR-143 between NSCLC and control groups (after adjusting for gender and age)
| Model | Genotype | Control, n (%) | NSCLC, n (%) | OR (95% CI) | P-value | AIC | BIC |
|---|---|---|---|---|---|---|---|
| Codominant | T/T | 232 (51.3) | 204 (45.1) | 1.00 | 0.110 | 1336.7 | 1605.8 |
| T/C | 183 (40.5) | 196 (43.4) | 1.25 (0.94–1.67) | ||||
| C/C | 37 (8.2) | 52 (11.5) | 1.54 (0.96–2.48) | ||||
| Dominant | T/T | 232 (51.3) | 204 (45.1) | 1.00 | 0.056 | 1335.4 | 1599.8 |
| T/C-C/C | 220 (48.7) | 248 (54.9) | 1.30 (0.99–1.71) | ||||
| Recessive | T/T-T/C | 415 (91.8) | 400 (88.5) | 1.00 | 0.160 | 1337.1 | 1601.4 |
| C/C | 37 (8.2) | 52 (11.5) | 1.39 (0.88–2.19) | ||||
| Overdominant | T/T-C/C | 269 (59.5) | 256 (56.6) | 1.00 | 0.280 | 1337.9 | 1602.2 |
| T/C | 183 (40.5) | 196 (43.4) | 1.16 (0.88–1.53) | ||||
| Log-additive | – | – | – | 1.25 (1.01–1.53) | 0.036 | 1334.7 | 1599.0 |
Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; NSCLC, nonsmall cell lung cancer.
Association analysis of the four SNPs with different pathologic stages
Table 7 shows the results of the association analysis of the four SNPs between pathologic stages I+II and III+IV. There were no significant differences in the allelic and genotypic frequencies for the four SNPs between pathologic stages I+II and III+IV (P>0.05).
Table 7.
Comparison of genotypic and allelic distribution of the four SNPs between pathologic stages I+II and III+IV (after adjusting for gender and age)
| SNP | Genotype, n (%) | P-value | Allele, n (%) | P-value | OR (95% CI) | |||
|---|---|---|---|---|---|---|---|---|
| rs11134527 | A/A | A/G | G/G | A | G | |||
| I+II | 63 (42.9) | 60 (40.8) | 24 (16.3) | 0.693 | 186 (63.3) | 108 (36.7) | 0.462 | 0.90 (0.67–1.20) |
| III+IV | 118 (63.3) | 135 (44.3) | 52 (17.0) | 371 (60.8) | 239 (39.2) | |||
| rs74693964 | C/C | C/T | T/T | C | T | |||
| I+II | 138 (93.9) | 9 (6.9) | 0 (0.0) | 0.760 | 285 (96.9) | 9 (3.1) | 0.749 | 0.88 (0.40–1.94) |
| III+IV | 284 (93.1) | 21 (6.9) | 0 (0.0) | 589 (96.6) | 21 (6.9) | |||
| rs6062251 | C/C | C/T | T/T | C | T | |||
| I+II | 17 (11.6) | 74 (50.3) | 56 (38.1) | 0.334 | 108 (36.7) | 186 (63.3) | 0.156 | 0.81 (0.60–1.08) |
| III+IV | 26 (8.5) | 143 (46.9) | 136 (44.6) | 195 (32.0) | 415 (68.0) | |||
| rs4705343 | T/T | T/C | C/C | T | C | |||
| I+II | 63 (42.9) | 63 (42.9) | 21 (14.3) | 0.422 | 189 (64.3) | 105 (35.7) | 0.248 | 1.19 (0.89–1.59) |
| III+IV | 141 (46.2) | 133 (43.6) | 31 (10.2) | 415 (68.0) | 195 (32.0) | |||
Abbreviation: SNP, single nucleotide polymorphism.
Association analysis of the four SNPs with AC and SCC
Our results showed that the allelic and genotypic frequencies of rs11134527 were significantly different between AC and SCC (P=0.005 and 0.015, respectively). The A allele was associated with SCC (OR=0.65; 95% CI: 0.48–0.88) (Table 8).
Table 8.
Comparison of genotypic and allelic distribution of the four SNPs between AC and SCC (after adjusting for gender and age)
| SNP | Genotype, n (%) | P-value | Allele, n (%) | P-value | OR (95% CI) | |||
|---|---|---|---|---|---|---|---|---|
| rs11134527 | A/A | A/G | G/G | A | G | |||
| AC | 96 (35.4) | 124 (45.8) | 51 (18.8) | 0.015 | 316 (58.3) | 226 (41.7) | 0.005 | 0.65 (0.48–0.88) |
| SCC | 82 (48.5) | 67 (39.6) | 20 (11.8) | 231 (68.3) | 107 (31.7) | |||
| rs74693964 | C/C | C/T | T/T | C | T | |||
| AC | 253 (93.4) | 18 (6.6) | 0 (0.0) | 0.853 | 524 (96.7) | 18 (3.3) | 0.870 | 1.07 (0.49–2.32) |
| SCC | 157 (92.9) | 12 (7.1) | 0 (0.0) | 326 (96.4) | 12 (3.6) | |||
| rs6062251 | C/C | C/T | T/T | C | T | |||
| AC | 25 (9.2) | 121 (44.6) | 125 (46.1) | 0.344 | 171 (31.5) | 371 (68.5) | 0.364 | 0.87 (0.64–1.18) |
| SCC | 17 (10.1) | 86 (50.9) | 66 (39.1) | 120 (35.5) | 218 (64.5) | |||
| rs4705343 | T/T | T/C | C/C | T | C | |||
| AC | 124 (45.8) | 116 (42.8) | 31 (11.4) | 0.984 | 364 (67.2) | 178 (32.8) | 0.961 | 0.99 (0.73–1.34) |
| SCC | 76 (45.0) | 73 (43.2) | 20 (11.8) | 225 (66.6) | 113 (33.4) | |||
Abbreviations: AC, adenocarcinoma; SCC, squamous cell carcinoma; SNP, single nucleotide polymorphism.
Discussion
Recently, many studies have reported the association of miRNA genes and NSCLC. In addition, several studies have been carried out to evaluate the association of SNPs located in miRNA genes with the susceptibility of cancers, including NSCLC. In the current study, we analyzed the association of four SNPs (rs11134527 in pri-miR-218-2; rs74693964 in pri-miR-145; rs6062251 in pri-miR-133a-2; rs4705343 in pri-miR-143) in miRNAs with NSCLC susceptibility in a Han Chinese population. Our results showed that the A allele of rs11134527 might be a risk factor and that the T allele of rs4705343 might be a protective factor for NSCLC in this Han Chinese population.
miR-218 has been reported to be downregulated in various cancers.26–28 In 2010, Wu et al29 reported that reduced expression of miR-218 was associated with worse survival in lung cancer. Then, Kumamoto et al30 found that ectopic expression of miR-218 significantly inhibited cancer cell migration and invasion in lung cancer. In addition, some studies have found associations between rs11134527, located at pri-miR-218-2, and the risk of different human cancers, such as esophageal squamous cell carcinoma (ESCC),31 cervical cancer,32,33 and hepatocellular carcinoma. Our results also showed that the genotypic and allelic frequencies of rs11134527 were significantly different between the NSCLC and control groups. Allele A of rs11134527 was associated with a higher risk of NSCLC. Our results are consistent with previous studies performed on cervical cancer that found that the rs11134527 GG genotype was associated with a decreased risk of cervical carcinoma32,33 and ESCC,31 which indicated that rs11134527 might alter the expression of miR-218 and be associated with cancer. However, Zhang et al34 and Zhang et al35 found that there was no association of this SNP with ESCC and hepatocellular carcinoma, respectively. The reason for these different results might be the different cancer types.
miR-143, a tumor suppressor of various types of human cancer, has been demonstrated to play crucial roles in tumor growth, migration, and invasion.36–38 In lung cancer, miR-143 can inhibit the genesis and development of tumors through directly suppressing the expression of its target genes.39,40 Recently, an integrated bioinformatics analysis showed that miR-143 was downregulated in NSCLC.41 These data indicate that miR-143 is important for the initiation and development of human cancers. Based on this, other studies have investigated the association of SNPs in miR-143 with human cancers and found that SNPs (such as rs4705343 and rs353292) in miR-143 are associated with several cancers.42–44 In the current study, we found that allele T of rs4705343 was associated with a decreased risk of NSCLC, which is in agreement with the results of a case–control study on cervical SCC.45 These results also indicate that rs4705343 in miR-143 plays an important role in NSCLC and cervical SCC.
Downregulation of miR-145 and miR-133a has been reported in cancers. Several SNPs located in the flanking region of the miR-145 gene were demonstrated to be associated with the expression of miR-145 and disease susceptibility.43,45–47 In 2016, Xiao et al48 identified miR-133 as a biomarker for lung cancer. In addition, several studies have found an association between SNPs in the miR-133 gene (rs8089787, rs9948906, rs13040413, and rs200375711) with different diseases.49,50 In the current study, we analyzed the association of rs74693964 in miR-145 and rs6062251 in miR-133a2 with NSCLC. However, we did not find an association of rs74693964 in pri-miR-145 or rs6062251 in pri-miR-133a-2 with NSCLC in the current population.
A limitation of the present study is that we did not ascertain the smoking status of the control individuals, making it difficult to perform future analyses of such exposure variables or a gene–smoking interaction analysis. This limitation may neutralize the effect of smoking and expose the effects of genetic variants in our study.
Conclusion
In the current study, we investigated the association of four SNPs located in four tumor suppressor miRNA genes (pri-miR-218-2, pri-miR-143, pri-miR-145, and pri-miR-133a-2) with NSCLC in a Chinese Han population. Our results showed that the A allele of rs11134527 in pri-miR-218-2 might be a risk factor for lung cancer. In addition, the T allele of rs4705343 in pri-miR-143 might be associated with a decreased risk of NSCLC. Our findings provide evidence that rs11134527 in pri-miR-218-2 and rs4705343 in pri-miR-143 may be new biomarkers for NSCLC. However, we analyzed the distribution of the SNPs between Asian and European populations (http://asia.ensembl.org/Homo_sapiens/Info/Index) and found that the allelic frequencies selected in the current study were significantly different between these two populations (Table 9). This result might suggest that it is necessary to evaluate the association of these SNPs with NSCLC in different populations before these SNPs are used as new biomarkers for NSCLC.
Table 9.
Comparison of the SNP distribution between European and Asian populations
| SNP in current study | Allelic frequencies in European population | Allelic frequencies in Eastern Asian population | P value |
|---|---|---|---|
| rs11134527 | G: 76%; A: 24% | G: 43%; A: 57% | <0.001 |
| rs74693964 | C: 100% | C: 96%; T: 4% | 0.043 |
| rs6062251 | T: 38%; C: 62% | T: 67%; C: 33% | <0.001 |
| rs4705343 | T: 83%; C: 17% | T: 68%; C: 32% | 0.014 |
Abbreviation: SNP, single nucleotide polymorphism.
Acknowledgments
We are grateful for the participation of the patients and control subjects in this study. Appreciation is also owed to Xiaona Wang and Xinwen Zhang who participated in the experiments and data checking. This work was supported by a grant from the National Natural Science Foundation of China (81573206 and 31270030), Yunnan Applied Basic Research Projects (2016FA034), Fundamental Research Funds for the Central Universities and the PUMC Youth Fund (3332015149), The Association Foundation Program of Yunnan Provincial Science and Technology Department and Kunming Medical University (2017FE468-193, 2013FB169), and Special Funds for high-level health talents of Yunnan Province (D-201669 and L-201615). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Footnotes
Disclosure
The authors report no conflicts of interest in this work.
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