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. 2017 Sep 23;8(49):86435–86446. doi: 10.18632/oncotarget.21199

Association between SNPs in microRNA machinery genes and gastric cancer susceptibility, invasion, and metastasis in Chinese Han population

Xingbo Song 1,*, Huiyu Zhong 1,*, Qian Wu 1,*, Minjin Wang 1, Juan Zhou 1, Yi Zhou 1, Xiaojun Lu 1, Binwu Ying 1
PMCID: PMC5689696  PMID: 29156806

Abstract

Objective

The present study investigates the influence of genetic variants in miRNA machinery genes (DROSHA, DICER, AGO1, and GEMIN4) on gastric cancer in Chinese Han population, further revealing the genetic mechanisms of gastric cancer occurrence and development.

Methods

Genotyping of single nucleotide polymorphisms (SNPs) was performed in 628 patients with GC and 502 frequency-matched (age and gender) controls by the high resolution melting (HRM) method.

Results

The SNPs rs3742330 (DICER) and rs7813 (GEMIN4) were associated with susceptibility to gastric cancer (P = 0.002 and 0.010, respectively). Stratified analysis showed that the G allele of rs3742330 and genotype TT as well as T allele of rs7813 were associated with a later stage of gastric cancer (P=0.027, 0.032 and 0.018, respectively). Furthermore, the genotype TT and T allele of rs7813 appeared to be associated with a higher level of lymphatic metastasis of gastric cancer (P=0.021 and 0.030, respectively), while the genotype AA and A allele of rs636832 (AGO1) were correlated with a lower level of lymphatic metastasis of gastric cancer (P=0.016 and 0.041, respectively). There was no significant association between rs10719 (DROSHA) and gastric cancer.

Conclusion

The present data demonstrated that genetic variants in miRNA machinery genes had a significant association with GC susceptibility (DICER and GEMIN4) and malignant behavior such as tumor stage (DICER and GEMIN4) and lymphatic metastasis of GC (GEMIN4 and AGO1) in Chinese Han population.

Keywords: microRNA, single nucleotide polymorphism, gastric cancer, tumor invasion and metastasis

INTRODUCTION

Gastric cancer (GC) is the most common malignant tumor of the digestive system and the third leading cause of cancer mortality worldwide [1]. The development and progression of GC are affected by the interaction between environmental factors and individual genetic factors. Factors including Helicobacter pylori infection, salted food, drinking, smoking and so on were proved by classic epidemiological studies to be the main risk factors of GC [2], but there are differences in GC susceptibility and tumor progression between individuals. These differences are associated with gene polymorphisms.

MicroRNAs (miRNAs) are small single-stranded RNA molecules of about 21-23 nucleotides (nt) in length. Recently, miRNA is widely recognized as regulators of gene expression and regulate about 30% genes in humans [3–6]. The process of miRNA synthesis begins within the nucleus where RNA polymerase II converts miRNA into pri-miRNA. The pri-miRNA is then processed into a precursor of ∼70 nt in length with a hairpin structure by a DNA endonuclease enzyme named DROSHA (RNase III) as well as its cofactor DGCR8; this precursor is called pre-miRNA. At the same time, DROSHA and DGCR8 protein constitute a microprocessor complex in the formation of pre-miRNA. Next, Exportin-5/Ran-GTP complex transfers pre-miRNA to the cytoplasm, and pre-miRNA is then cut into miRNA duplexes (about 20 bp) by the TAR RNA binding protein (TRBP)-related DICER [7–11]. One strand of the miRNA duplexes integrates into miRNA-induced silencing complex (miRISC) and becomes mature miRNA. The miRISC contains proteins including AGO1-4, GEMIN3, and GEMIN4 that participate in mRNA inhibition or shearing of target mRNA [12–15]. Therefore, genetic polymorphisms in microRNA machinery genes could lead to abnormal expression of miRNAs and in turn affect the expression level of target genes, thus becoming the risk factor of disease such as tumor. Currently, there is little research exploring the influence of single nucleotide polymorphisms (SNPs) in miRNA machinery genes on GC susceptibility, invasion, and metastasis.

This research chose SNP loci in classical miRNA machinery genes (rs3742330 in DICER, rs3744741 and rs7813 in GEMIN4, rs10719 in DROSHA, and rs636832 in AGO1) by using a candidate gene-based approach to genetically explore the effect of variants in miRNA machinery genes on GC susceptibility, invasion, and metastasis in Chinese Han population. In addition, the findings of this study might provide the basis for further revealing the specific mechanisms by which genetic variants of these genes participate in the occurrence and development of GC. Additional in-silico studies were used to assess the possible functional significance and miRNA-binding of the positive polymorphisms.

RESULTS

Demographic characteristics of the study participants

The demographic characteristics of 628 cases and 502 controls are presented in Table 1. The average age and sex had no significant differences between the patient group and control group (P=0.727, 0.577 respectively) and for smoking status and drinking status (P=0.297, 0.631 respectively). All the participants were from Chinese population.

Table 1. Basic demographic data of subjects and clinical characteristics of GC cases.

Parameters Case Control P
n Frequencies (%) n Frequencies (%)
Age (year, mean ± SD) 56.5±12.1 56.2±12.2 0.727
Gender
 Male 418 66.6 323 64.3 0.577
 Female 210 33.4 179 35.7
Smoking Status
 Non-smokers 365 58.1 297 59.2 0.297
 Former Smokers 139 22.1 125 24.9
 Current Smokers 124 19.7 80 15.9
Drinking status
 Non-drinker 437 69.6 348 69.3 0.631
 Light Drinkers 93 14.8 66 13.2
 Heavy Drinkers 98 15.6 88 17.5
Tumor size (diameter)
 <5 cm 207 33.0
 5-10 cm 204 32.5
 ≥10 cm 56 8.9
 N.A. 161 25.6
Tumor stages
 1a 64 10.2
 1b 36 5.7
 2a 41 6.5
 2b 72 11.5
 3a 55 8.8
 3b 71 11.3
 3c 109 17.4
 4 180 28.7
Degree of differentiation
 Low 433 68.9
 Medium 185 29.5
 High 10 1.6
Lymphatic metastasis
 0 151 24.0
 1 103 16.4
 2 109 17.4
 3a 124 19.7
 3b 61 9.7
 N.A. 80 12.7
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N.A. data not available.

The relationship between SNPs in miRNA machinery genes and GC susceptibility

Genotyping of five SNPs was successfully completed for the cases and controls. Genotype distribution of rs3744741 in patient group was not in accordance with the Hardy-Weinberg equilibrium (HWE) (χ2=10.18, P=0.001), while the other 4 SNPs in either patient or control groups met HWE (P > 0.05 for all loci). Thus, the SNP rs3744741 was excluded from further analysis (data not shown). Table 2 shows the genotype distributions and allele frequencies of the 4 SNPs in miRNA machinery genes between cases and controls.

Table 2. Comparisons of gene polymorphisms between the case and control groups.

SNP Cases Controls OR (95% C.I.)* P*
N % N %
rs 3742330
Genotype
 AA 273 43.5 177 35.3 1.00 (Reference)
 AG 284 45.2 246 49.0 0.75 (0.58-0.97) 0.026
 GG 71 11.3 79 15.7 0.58 (0.39-0.86) 0.004
Allele
 A 830 66.1 600 59.8 1.00 (Reference)
 G 426 33.9 404 40.2 0.76 (0.64-0.91) 0.002
 rs7813
Genotype
 TT 261 41.6 241 48.0 1.00 (Reference)
 CT 294 46.8 222 44.2 1.22 (0.95-1.58) 0.110
 CC 73 11.6 39 7.8 1.73 (1.11-2.71) 0.011
Allele
 T 816 65.0 704 70.1 1.00 (Reference)
 C 440 35.0 300 29.9 1.27 (1.05-1.52) 0.010
 rs10719
Genotype
 TT 314 50.0 248 49.4 1.00 (Reference)
 CT 257 40.9 205 40.8 0.99 (0.77-1.28) 0.938
 CC 57 9.1 49 9.8 0.92 (0.59-1.42) 0.690
Allele
 T 885 70.5 701 69.8 1.00 (Reference)
 C 371 29.5 303 30.2 0.97 (0.81-1.17) 0.741
 rs 636832
Genotype
 AA 321 51.1 268 53.4 1.00 (Reference)
 AG 261 41.6 198 39.4 1.10 (0.85-1.42) 0.445
 GG 46 7.3 36 7.2 1.07 (0.65-1.74) 0.785
Allele
 A 903 71.9 734 73.1 1.00 (Reference)
 G 353 28.1 270 26.9 1.06 (0.88-1.29) 0.521
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* Adjusted by sex, age, smoking status, and drinking status.

As shown in Table 2, the minor allele (G allele) frequency of rs3742330 was 33.9% in cases and 40.2% in controls and was significantly different (OR= 0.76, 95% CI= 0.64-0.91), the p-value was 0.002; this indicated that the G allele could be a protective element for GC susceptibility. As expected, genotype GG and AG of rs3742330 had a significantly decreased risk of GC compared with AA genotype (P=0.004, OR= 0.58, 95% CI= 0.39-0.86 for GG versus AA, and P=0.026, OR= 0.75, 95% CI= 0.58-0.97 for AG versus AA).

Conversely, subjects carrying a CC genotype in rs7813 showed a significant increase in risk for GC than those carrying the TT genotype (P=0.011, OR = 1.73, 95% CI = 1.11-2.71), and it was suggested that the C allele of rs7813 may be associated with a higher risk of GC than T allele (P=0.010, OR=1.27, 95%CI=1.05-1.52). However, no significant differences in genotype distributions or allelic frequencies of rs10719 and rs636832 were demonstrated between the cases and controls. All the above data were adjusted by sex, age, smoking status, and drinking status.

Stratified analysis for the SNP genotypes and clinicopathologic characteristics of GC patients

To demonstrate the association between SNP genotypes and clinicopathologic characteristics of GC, the cases were stratified into subgroups according to tumor size, tumor stages, degree of differentiation, and lymphatic metastasis. The results for each SNP are summarized in Tables 3-1, 3-2, 3-3 and 3-4, respectively.

Table 3-1. Stratified analysis for the association between rs3742330 and GC clinical characteristics.

Clinical characteristics Genotype OR (95% C.I.)* P Allele OR (95% C.I.) P
AA AG GG A G
Tumor size
 <5 cm 100 86 21 1.00 (Reference) 286 128 1.00 (Reference)
 5-10 cm 83 94 27 0.73 (0.49-1.11) 0.120 260 148 0.79 (0.58-1.06) 0.104
 ≥10 cm 26 26 4 0.93 (0.49-1.75) 0.803 78 34 1.03 (0.64-1.66) 0.909
Tumor stages
 1a 29 30 5 1.00 (Reference) 88 40 1.00 (Reference)
 1b 14 18 4 0.77 (0.31-1.91) 0.533 46 26 0.80 (0.42-1.55) 0.483
 2a 13 18 10 0.56 (0.23-1.37) 0.165 44 38 0.53 (0.28-0.97) 0.027
 2b 38 29 5 1.35 (0.65-2.81) 0.385 74 39 0.86 (0.49-1.53) 0.590
 3a 25 24 6 1.01 (0.46-2.21) 0.988 74 36 0.93 (0.52-1.67) 0.807
 3b 29 32 10 0.83 (0.40-1.75) 0.601 90 52 0.79 (0.46-1.35) 0.353
 3c 46 51 12 0.88 (0.45-1.72) 0.690 143 75 0.87 (0.53-1.42) 0.548
 4 79 80 21 0.94 (0.51-1.74) 0.844 238 122 0.89 (0.56-1.40) 0.586
Degree of differentiation
 Low 187 198 47 1.00 (Reference) 572 292 1.00 (Reference)
 Medium 82 82 21 1.04 (0.73-1.50) 0.812 246 124 1.01 (0.78-1.32) 0.923
 High 4 4 2 12 8
Lymphatic metastasis
 0 66 72 13 1.00 (Reference) 204 98 1.00 (Reference)
 1 42 42 19 0.89 (0.52-1.52) 0.643 126 80 0.76 (0.51-1.11) 0.139
 2 49 51 9 1.05 (0.62-1.78) 0.842 149 69 1.04 (0.70-1.53) 0.847
 3a 62 50 12 1.29 (0.78-2.13) 0.298 174 74 1.13 (0.77-1.65) 0.511
 3b 19 33 9 0.58 (0.30-1.14) 0.091 71 51 0.67 (0.42-1.06) 0.068
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Table 3-2. Stratified analysis for the association between rs7813 and GC clinical characteristics.

Clinical characteristics Genotype OR (95% C.I.)* P Allele OR (95% C.I.) P
TT CT CC T C
Tumor size
 <5 cm 81 105 21 1.00 (Reference) 267 147 1.00 (Reference)
 5-10 cm 88 94 22 1.18 (0.78-1.78) 0.409 270 138 1.08 (0.80-1.45) 0.612
 ≥10 cm 26 26 4 1.35 (0.71-2.55) 0.324 78 34 1.26 (0.79-2.03) 0.309
Tumor stages
 1a 20 37 7 1.00 (Reference) 77 51 1.00 (Reference)
 1b 15 13 8 1.57 (0.62-4.00) 0.295 43 29 0.98 (0.52-1.85) 0.952
 2a 13 24 4 1.02 (0.40-2.58) 0.961 50 32 1.03 (0.56-1.90) 0.906
 2b 33 30 9 1.86 (0.87-4.00) 0.082 96 48 1.32 (0.78-2.24) 0.265
 3a 20 28 7 1.26 (0.55-2.89) 0.556 68 42 1.07 (0.61-1.87) 0.793
 3b 24 38 9 1.12 (0.51-2.46) 0.601 86 56 1.02 (0.61-1.71) 0.946
 3c 54 50 5 2.16 (1.08-4.36) 0.019 158 60 1.74 (1.07-2.84) 0.018
 4 84 74 22 1.92 (1.01-3.69) 0.032 242 118 1.36 (0.88-2.10) 0.149
Degree of differentiation
 Low 178 203 52 1.00 (Reference) 559 307 1.00 (Reference)
 Medium 79 88 18 1.07 (0.74-1.54) 0.713 246 124 1.09 (0.84-1.42) 0.513
 High 4 3 3 11 9
Lymphatic metastasis
 0 50 80 21 1.00 (Reference) 180 122 1.00 (Reference)
 1 48 45 10 1.76 (1.02-3.05) 0.030 141 65 1.47 (1.00-2.17) 0.042
 2 43 52 14 1.36 (0.76-2.27) 0.293 138 80 1.17 (0.80-1.70) 0.393
 3a 58 54 12 1.78 (1.06-2.98) 0.021 170 78 1.48 (1.02-2.14) 0.030
 3b 28 28 5 1.71 (0.89-3.29) 0.080 84 38 1.50 (0.94-2.40) 0.075
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Table 3-3. Stratified analysis for the association between rs10719 and GC clinical characteristics.

Clinical characteristics Genotype OR (95% C.I.)* P Allele OR (95% C.I.) P
TT CT CC T C
Tumor size
 <5 cm 106 83 18 1.00 (Reference) 295 119 1.00 (Reference)
 5-10 cm 104 79 21 0.99 (0.66-1.49) 0.963 287 121 0.96 (0.70-1.31) 0.773
 ≥10 cm 30 21 5 1.10 (0.58-2.07) 0.753 81 31 1.05 (0.65-1.73) 0.825
Tumor stages
 1a 26 32 6 1.00 (Reference) 84 44 1.00 (Reference)
 1b 20 14 2 1.83 (0.74-4.54) 0.150 54 18 1.57 (0.79-3.16) 0.169
 2a 23 14 4 1.87 (0.78-4.47) 0.121 60 22 1.43 (0.74-2.75) 0.251
 2b 34 33 5 1.31 (0.63-2.74) 0.439 101 43 1.23 (0.72-2.12) 0.426
 3a 30 19 6 1.75 (0.79-3.88) 0.129 79 31 1.33 (0.74-2.41) 0.305
 3b 37 24 10 1.59 (0.76-3.34) 0.182 98 44 1.17 (0.68-2.00) 0.553
 3c 59 41 9 1.72 (0.88-3.38) 0.086 159 59 1.41 (0.86-2.32) 0.151
 4 85 80 15 1.31 (0.73-2.43) 0.363 250 110 1.19 (0.76-1.87) 0.425
Degree of differentiation
 Low 215 179 39 1.00 (Reference) 609 257 1.00 (Reference)
 Medium 94 74 17 1.05 (0.73-1.50) 0.792 262 108 1.02 (0.73-1.35) 0.863
 High 5 4 1 14 6
Lymphatic metastasis
 0 69 69 13 1.00 (Reference) 207 95 1.00 (Reference)
 1 55 39 9 1.36 (0.80-2.32) 0.228 149 57 1.20 (0.80-1.81) 0.360
 2 60 39 10 1.46 (0.86-2.46) 0.137 159 59 1.24 (0.83-1.85) 0.279
 3a 67 46 11 1.40 (0.80-2.42) 0.169 180 68 1.21 (0.83-1.79) 0.302
 3b 26 26 9 0.88 (0.46-1.68) 0.684 78 44 0.81 (0.51-1.44) 0.360
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Table 3-4. Stratified analysis for the association between rs636832 and GC clinical characteristics.

Clinical characteristics Genotype OR (95% C.I.)* P Allele OR (95% C.I.) P
AA AG GG A G
Tumor size
 <5 cm 96 94 17 1.00 (Reference) 286 128 1.00 (Reference)
 5-10 cm 106 86 12 1.25 (0.83-1.88) 0.258 298 110 1.21 (0.89-1.66) 0.211
 ≥10 cm 27 24 5 1.08 (0.57-2.03) 0.807 78 34 1.03 (0.64-1.66) 0.909
Tumor stages
 1a 35 26 3 1.00 (Reference) 96 32 1.00 (Reference)
 1b 23 12 1 1.47 (0.58-3.70) 0.371 58 14 1.38 (0.65-2.98) 0.370
 2a 25 12 4 1.29 (0.54-3.11) 0.525 62 20 1.03 (0.52-2.07) 0.920
 2b 36 30 6 1.83 (0.40-1.72) 0.585 102 42 0.81 (0.46-1.43) 0.441
 3a 34 20 1 1.34 (0.60-2.99) 0.432 88 22 1.33 (0.69-2.58) 0.358
 3b 31 36 4 0.64 (0.31-1.34) 0.201 98 44 0.74 (0.42-1.31) 0.275
 3c 46 51 12 0.60 (0.31-1.18) 0.112 143 75 0.64 (0.38-1.06) 0.068
 4 91 74 15 0.85 (0.46-1.56) 0.570 256 104 0.82 (0.50-1.33) 0.399
Degree of differentiation
 Low 211 191 31 1.00 (Reference) 613 253 1.00 (Reference)
 Medium 103 67 15 1.32 (0.92-1.90) 0.114 273 97 1.16 (0.88-1.54) 0.284
 High 7 3 0 17 3
Lymphatic metastasis
 0 88 56 7 1.00 (Reference) 232 70 1.00 (Reference)
 1 59 36 8 0.96 (0.56-1.65) 0.875 154 52 0.89 (0.58-1.38) 0.593
 2 47 56 6 0.54 (0.32-0.92) 0.016 150 68 0.67 (0.44-1.00) 0.041
 3a 61 47 16 0.69 (0.42-1.15) 0.132 169 79 0.65 (0.43-0.96) 0.023
 3b 27 31 3 0.57 (0.30-1.08) 0.064 85 37 0.69 (0.42-1.14) 0.125
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As shown in Table 3-1, the A allele of rs3742330 may decrease the risk of GC in stage 1b rather than 1a (P=0.027, OR=0.53, 95%CI=0.28-0.97). However, there was no significant difference found in the frequency of AA genotype. According to Table 3-2, individuals carrying TT genotype and T allele of rs7813 had an increased risk of GC in tumor stage 3c than stage 1a (P=0.019, OR=2.16, 95%CI=1.08-4.36; P=0.018, OR=1.74, 95%CI=1.07-2.84, respectively). In terms of the data, the TT genotype of rs7813 also increased the risk of GC in stage 4 than stage 1a (P=0.032, OR=1.92, 95%CI=1.01-3.69). For GC invasion and metastasis, the data in Table 3-2 indicated that the TT genotype and the T allele of rs7813 had a higher risk of lymphatic metastasis stage 1 or 3a than stage 0 (P=0.030, OR=1.76, 95%CI=1.02-3.05; P=0.042, OR=1.47, 95%CI=1.00-2.17 and P=0.021, OR=1.78, 95%CI=1.06-2.98; P=0.030, OR=1.48, 95%CI=1.02-2.14). With regard to rs636832, as shown in Table 3-4, it is suggested that the AA genotype and A allele had an association with a lower risk of lymphatic metastasis stage 2 compared with stage 0 (P=0.016, OR=0.54, 95%CI=0.32-0.92 and P=0.041, OR=0.67, 95%CI=0.44-1.00 respectively), similar to the A allele which had a lower risk of lymphatic metastasis stage 3a (P=0.023, OR=0.65, 95%CI=0.43-0.96). Stratified analysis of rs10719 showed no significant differences in tumor size, tumor stages, degree of differentiation, or lymphatic metastasis of GC (Table 3-3). To demonstrate the mechanisms of these associations, further study is urgently needed.

In-silico analysis of microRNA-binding and function prediction

As for rs3742330, computational modeling suggested that this polymorphism was located in the potential target sequence of hsa-miR-632 in DICER 3'UTR region (Supplementary Figure 1). The G allele could reduce the affinity of microRNA-mRNA binding by disrupting the local structure of DICER mRNA, possibly leading an increased DICER expression. In addition rs7813(C>T, R1033C) was a missense variant in exon region of GEMIN4, which could alter the structure of GEMIN4 protein by turning Arginine into Cysteine (Supplementary Figure 2), thus reducing GEMIN4 expression. There was no function results for rs636832 obtained from the software.

DISCUSSION

Individual genetic factors play an important role in susceptibility and progression of GC. miRNA is a small single-stranded RNA of 21-23 nt in length and is widely recognized as regulators of gene expression. miRNAs participate in a variety of important biological processes including cell cycle, cell differentiation, and cell proliferation and apoptosis [16]. Previous studies have confirmed that miRNAs play an important role in a wide variety of tumor biological behaviors such as tumor cell proliferation and apoptosis. Clinically, there is abnormal expression of different levels of miRNAs in cancer patients, indicating that miRNA has a large influence on the development of tumor [17–19]. Ahn DH [20] chose four SNPs in miRNA and analyzed the genotypes and allele frequencies of these SNPs in 461 Korean GC patients. The study found that these polymorphisms in miRNA were associated with the risk of GC; in addition, genotypes rs2292832 and rs3746444 were associated with survival rates of GC patients. Xiong XD [21] found that rs895819 in pre-miR-27a could alter the expression level of the miRNA and thus was correlated with the incidence of cervical cancer. A previous study showed correlations between genetic variants in miRNA and gastric lesions or even GC. One study investigated rs112310158 in hsa-miR-449a in Chinese population and revealed that GG genotype of rs112310158 had a higher risk of GC than other genotypes [22]. Jin X [23] analyzed genotypes of SNPs in mir-421 and found it to be significantly associated with GC susceptibility, lymphatic metastasis, and prognosis.

The expression level and regulatory function of miRNA depend on the orderly division of function of genes in miRNA biogenesis pathways. Proteins such as GEMIN4, AGO1, DROSHA, DICER, and their complex regulating miRNA biogenesis pathways are key components of miRNA maturation, transfer, and function. Proper cooperation of these proteins enables the expression of genes that regulate miRNA. Genetic variants in miRNA machinery genes could affect the maturation and regulatory function of miRNA by influencing the transcription ability of genes (UTR region) or protein function (exon region), thus manifesting as a change in tumor susceptibility and malignant behavior [8, 24]. Recent studies have already revealed a relationship between SNPs in miRNA machinery genes and several tumors including GC [13, 25–27], and investigation of variants in miRNA machinery genes could clarify the mechanism of the occurrence and development of GC and provide new basis for its clinical diagnosis and management. Our group speculates that genetic polymorphisms of the important miRNA machinery genes (DICER, GEMIN4, DROSHA and AGO1) could play a role in GC susceptibility and malignant behavior by affecting the maturity and functioning of miRNA.

This study analyzed the genotype and allele frequencies of four SNPs in miRNA machinery genes (GEMIN4, DROSHA, DICER and AGO1) in GC patients and healthy controls in Chinese Han population to investigate whether the genetic polymorphisms in these genes can affect the susceptibility, invasion and metastasis of GC. We found that among the chosen SNPs, the distribution of genotype and allele frequencies of rs3742330 in DICER and rs7813 in GEMIN4 were significantly different between GC patients and healthy controls, indicating that genetic variants in DICER and GEMIN4 were correlated with GC susceptibility in Chinese Han population. Tchernitsa O [28] analyzed the expression of DICER in adjacent normal and tumor samples of patients with GC by using immunohistochemistry and detected an elevated DICER level in GC tissues. However, another study using the same sample type and analytical method demonstrated a down regulation of DICER in GC tissues in both mRNA and protein levels [29]. There is no study demonstrating a definite association between GEMIN4 and GC. Xie Y [26] investigated SNPs in miRNA machinery genes including GEMIN4 (rs2740348) but found no significant correlation between this SNP in GEMIN4 and GC pathogenesis. Despite the controversial results reported, it is clear that DICER and GEMIN4 participated in the pathogenesis of tumors including GC, and polymorphisms in these genes could affect tumor susceptibility. Thus far, the influence of SNPs that we investigated in DICER and GEMIN4 on GC susceptibility had rarely been reported. Rs3742330 in DICER had been reported to be associated with the risk of larynx cancer in Polish population [30]. Another study in Korean population showed a significantly increased risk of colon cancer in individuals with AG genotype of rs3742330 [31]. The location of rs3742330 in the 3’-UTR region of DICER may potentially influence the stability and expression of DICER through changing the binding capacity of regulatory miRNAs [32, 33]. But the mechanism underlying how rs3742330 modified GC susceptibility remains unclear. Our group conducted the in-silico analysis and found that rs3742330 was located in the hsa-miR-632 potential target sequence in DICER 3'UTR region, which might probably upregulate the expression of DICER. Rs7813 in GEMIN4 was reported to be evidently associated with the risk of lung cancer [34], but another study showed no significant association of rs7813 with the risk of esophageal squamous cell carcinoma [35]. Our predicted analyses showed that rs7813 could alter the structure of GEMIN4 protein by turning Arginine into Cysteine and the alteration might reduce GEMIN4 expression. It was reported that rs7813 in GEMIN4 could induce Arg to Cys substitution at the 1033 amino acid position through the C to T transition [34], which could then affect the function of miRNAs. Our study found a correlation between the two polymorphisms in DICER and GEMIN4 and GC susceptibility, suggesting a predictive role of these SNPs in gastric carcinogenesis.

Furthermore we established a database of all the GC patients, including enormous clinical information such as tumor size, tumor stage, degree of differentiation, lymphatic metastasis and so on. Stratified analysis with all the clinical features revealed a notable correlation between rs3742330 (DICER) and rs7813 (GEMIN4) and the stage of GC, providing molecular markers of prognosis at an early stage. In addition, the TT genotype and the T allele of rs7813 (GEMIN4), and the AA genotype and A allele of rs636832 (AGO1) were related to lymphatic metastasis of GC. These three SNPs could be potential biomarkers for predicting the invasion and metastasis of GC. Previously, several researchers have reported the dysregulation and potential role of DICER, GEMIN4 and AGO1 in tumor progression, including GC. Down regulation of DICER has been reported to be highly correlated with tumor differentiation and lymph node invasion in GC tissues, while decrease of DICER was more common in GC cases with low tumor differentiation and lymph node metastasis [29]. Shi Z [36] further demonstrated the mechanism that DICER could process pre-miR-21 to mature miR-21, while the inhibitor of DICER (AC1MMYR2) blocked its ability for miRNA maturation and further suppressed proliferation, survival, and invasion in glioblastoma, breast cancer, and gastric cancer cells in vivo. According to an in vitro experiment, DEAD-box RNA helicase 6 (DDX6), which directly interacts with AGO1 in RNA-induced silencing complexes (RISC), was reported to down regulate miR-143/145 expression by prompting the degradation of its host gene product [37]. Thus far, no association has been found between GEMIN4 and GC progression. Consistent with our study, rs3742330 in DICER and rs7813 in GEMIN4 were found to participate in tumor progression. Mi Na Kim [38] demonstrated that rs3742330 was associated with the survival of hepatocellular carcinoma patients, while another study reported that the G allele of rs3742330 was associated with lower aggressiveness of prostate cancer [39]. Yang PW [40] showed a borderline significant association between rs7813 in GEMIN4 and the prognosis of esophageal squamous cell carcinoma (ESCC). In addition, AGO1 is located at chromosome 1p35-p34 and frequently lost in human malignant tumors, and rs636832 is located in the intron of AGO1, which might influence the conformation and function of proteins or the splicing of precursor miRNA [41], but no study reported the effect of rs636832 in AGO1 on tumor development, while current studies have not yet demonstrated a definite correlation between rs3742330 as well as rs7813 and GC invasion and metastasis. Our present study is the first to revealed an influence of the three SNPs in miRNA machinery genes on GC progression.

The results from this study demonstrated that genetic polymorphisms in miRNA machinery genes (DICER, GEMIN4 and AGO1) affected the susceptibility and the invasion and metastasis of GC in Chinese Han population, extremely probably by affecting maturing and functioning of relevant miRNAs. We confirmed in a relatively large sample size that these polymorphisms participated in the development of GC and its malignant behavior, genetically proving the essential roles of these genes in tumorigenesis and progression of tumor. Follow-up studies with larger sample size are required to further verify the results and design innovative experiments and functional verification to investigate the specific mechanism by which polymorphisms in these miRNA machinery genes influence the maturation of miRNA and then participate in the genesis and development of GC. The subsequent research could further reveal the molecular mechanism of GC and provide new molecular markers for GC diagnosis and treatment.

MATERIALS AND METHODS

Study populations

The study involved 628 cases and 502 controls. The cases were from West China Hospital outpatient or inpatients with GC between July 2010 and July 2016. The diagnosis of GC was based on both clinical criteria and pathological confirmation. The controls included unrelated healthy individuals screened from the physical examination center of West China Hospital of Sichuan University. All the controls had no significant history of disease. The controls were matched with the cases in the age and gender and came from the same region and same period as the cases. All participants provided informed consent to participate in the study, and this study was approved by the ethical committee of West China Hospital of Sichuan University.

Genomic DNA was extracted from the peripheral blood of the participants by using QIAamp® DNA Blood mini kit (Qiagen, Düsseldorf, Germany) following the manufacturer’s instructions. The samples were selected from patients with GC who had not been treated with chemotherapy but had been pathologically confirmed. Each sample used in the experiment had detailed clinical information and DNA met the requirements of concentration and purity.

SNP selection and genotyping

Based on the data from the International HapMap Project (http://www.hapmap.org), dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/), and miRBase registry (http://microrna.sanger.ac.uk), we identified 20 potential polymorphisms in the miRNA biogenesis pathway (Supplementary Table 1) that met the criteria of minor allele frequency (MAF) > 0.01 in Chinese population. Thirty subjects including 15 healthy individuals and 15 patients with GC were randomly involved in SNP screening by high resolution melting (HRM). Finally, only five GC-associated SNPs with a high frequency (>0.1) of the minor allele were selected (rs3742330 in DICER, rs3744741 in GEMIN4, rs7813 in GEMIN4, rs10719 in DROSHA, and rs636832 in AGO1).

The isolated DNA was stored in a freezer at -80°C. Genotyping of the SNPs was performed by the HRM method. The data were analyzed using the LightCycler®480 Gene Scanning software (v1.2, Roche Diagnostics, Bavaria, Germany). Polymerase chain reaction (PCR) amplifications were conducted in the LightCycler® 480 (Roche Diagnostics). The PCR reaction mixture (20 μL) included the following: 0.5 μL forward primer (10 μM), 0.5 μL reverse primer (10 μM), 0.2 μL Hot Star Taq® Plus DNA Polymerase (5 U/μL), 1 μL 20×EVA-GREEN, 2 μL dNTP (10 mM), 1 μL genomic DNA (10 ng/μL), 2 μL MgCl2 (25 mM), 2 μL 10×buffer, and 10.8 μL H2O. Real-time PCR was performed with the following conditions: an initial denaturation at 95°C for 15 min, followed by 50 cycles of denaturation at 95°C for 10 s, annealing at 60°C for 15 s, extension at 72°C for 25 s. Following the completion of the cycle program, PCR products were denatured at 95°C for 1 min and cooled to 40°C for 1 min to form double-stranded DNA. The HRM analysis was then performed by gradually increasing the temperature from 65°C to 95°C at a rate of 0.01°C/s. Three DNA samples with known genotypes were run simultaneously in each experiment as a reference, and 10% of the samples were randomly selected to genotype twice; all results were identical.

DNA sequencing

PCR products were purified using shrimp alkaline phosphatase (SAP). Sequencing primers for the five SNPs were the same as primers in PCR. Nucleotide sequencing was detected by BigDye Terminator v3.1 Cycle Sequencing Kit and ABI 3130 genetic analyzer (Applied Biosystems, California, USA).

In-silico analysis of microRNA-binding and function prediction

The mature human microRNA sequences were obtained from the microRNA database (miRBase) (http://microrna.sanger.ac.uk). A region comprising the rs3742330 plus 15 bp 5' and 3' was included for analyzing hybridization of putative microRNAs using miRanda software with default parameters. The predicted analysis for rs7813 and rs636832 was conducted using Polyphen2 online software (http://genetics.bwh.harvard.edu/pph2/).

Statistical analysis

The Goodness-of-fit chi-square test (χ2) was used for testing Hardy-Weinberg Equilibrium (HWE) with cases and controls. Differences in demographic characteristics were assessed by Student’s t-test (for continuous variables) or χ2 test (for categorical variables). Logistic regression was used to analyze the associations between SNPs and susceptibility of GC, adjusted by sex, age, smoking status, and drinking status. All the statistical analyses were two-sided and P < 0.05 was set as a criterion for statistical significance. All statistical analyses were performed using SPSS statistical software (version 20.0, SPSS Inc., USA).

SUPPLEMENTARY MATERIALS FIGURES AND TABLE

Acknowledgments

This work was supported by grants from the National Natural Science Foundation of China [81672096] and the Projects in the Science and Technology Department of Sichuan Province pillar program [2017FZ0065].

Footnotes

CONFLICTS OF INTEREST

The authors declare that they have no conflicts of interest.

REFERENCES

  • 1.Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65:87–108. doi: 10.3322/caac.21262. [DOI] [PubMed] [Google Scholar]
  • 2.Stadtländer CT, Waterbor JW. Molecular epidemiology, pathogenesis and prevention of gastric cancer. Carcinogenesis. 1999;20:2195–208. doi: 10.1093/carcin/20.12.2195. [DOI] [PubMed] [Google Scholar]
  • 3.Ueda T, Volinia S, Okumura H, Shimizu M, Taccioli C, Rossi S, Alder H, Liu CG, Oue N, Yasui W, Yoshida K, Sasaki H, Nomura S, et al. Relation between microRNA expression and progression and prognosis of gastric cancer: a microRNA expression analysis. Lancet Oncol. 2010;11:136–46. doi: 10.1016/S1470-2045(09)70343-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Zhao XD, Lu YY, Guo H, Xie HH, He LJ, Shen GF, Zhou JF, Li T, Hu SJ, Zhou L, Han YN, Liang SL, Wang X, et al. MicroRNA-7/NF-κB signaling regulatory feedback circuit regulates gastric carcinogenesis. J Cell Biol. 2015;210:613–27. doi: 10.1083/jcb.201501073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Chen J, Sun D, Chu H, Gong Z, Zhang C, Gong B, Li Y, Li N, Jiang L. Screening of differential microRNA expression in gastric signet ring cell carcinoma and gastric adenocarcinoma and target gene prediction. Oncol Rep. 2015;33:2963–71. doi: 10.3892/or.2015.3935. https://doi.org/10.3892/or.2015.3935. [DOI] [PubMed] [Google Scholar]
  • 6.Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA Targets Cell. 2005;120:15–20. doi: 10.1016/j.cell.2004.12.035. https://doi.org/10.1016/j.cell.2004.12.035. [DOI] [PubMed] [Google Scholar]
  • 7.Lee Y, Kim M, Han J, Yeom KH, Lee S, Baek SH, Kim VN. MicroRNA genes are transcribed by RNA polymerase II. EMBO J. 2004;23:4051–60. doi: 10.1038/sj.emboj.7600385. https://doi.org/10.1038/sj.emboj.7600385. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Lee Y, Ahn C, Han J, Choi H, Kim J, Yim J, Lee J, Provost P, Rådmark O, Kim S, Kim VN. The nuclear RNase III Drosha initiates microRNA processing. Nature. 2003;425:415–19. doi: 10.1038/nature01957. [DOI] [PubMed] [Google Scholar]
  • 9.Han J, Lee Y, Yeom KH, Nam JW, Heo I, Rhee JK, Sohn SY, Cho Y, Zhang BT, Kim VN. Molecular basis for the recognition of primary microRNAs by the Drosha-DGCR8 complex. Cell. 2006;125:887–901. doi: 10.1016/j.cell.2006.03.043. [DOI] [PubMed] [Google Scholar]
  • 10.McCarthy N. Cancer: small losses, big gains with microRNAs. Nat Rev Genet. 2010;11:8. doi: 10.1038/nrg2722. [DOI] [PubMed] [Google Scholar]
  • 11.Chendrimada TP, Gregory RI, Kumaraswamy E, Norman J, Cooch N, Nishikura K, Shiekhattar R. TRBP recruits the Dicer complex to Ago2 for microRNA processing and gene silencing. Nature. 2005;436:740–44. doi: 10.1038/nature03868. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Arribas-Hernández L, Kielpinski LJ, Brodersen P. mRNA decay of most arabidopsis miRNA targets requires slicer activity of AGO1. Plant Physiol. 2016;171:2620–32. doi: 10.1104/pp.16.00231. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Slaby O, Bienertova-Vasku J, Svoboda M, Vyzula R. Genetic polymorphisms and microRNAs: new direction in molecular epidemiology of solid cancer. J Cell Mol Med. 2012;16:8–21. doi: 10.1111/j.1582-4934.2011.01359.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Tolia NH, Joshua-Tor L. Slicer and the argonautes. Nat Chem Biol. 2007;3:36–43. doi: 10.1038/nchembio848. [DOI] [PubMed] [Google Scholar]
  • 15.Carthew RW, Sontheimer EJ. Origins and Mechanisms of miRNAs and siRNAs. Cell. 2009;136:642–55. doi: 10.1016/j.cell.2009.01.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Farazi TA, Spitzer JI, Morozov P, Tuschl T. miRNAs in human cancer. J Pathol. 2011;223:102–15. doi: 10.1002/path.2806. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Fernández C, Bellosillo B, Ferraro M, Seoane A, Sánchez-González B, Pairet S, Pons A, Barranco L, Vela MC, Gimeno E, Colomo L, Besses C, Navarro A, Salar A. MicroRNAs 142-3p, miR-155 and miR-203 are deregulated in gastric MALT lymphomas compared to chronic gastritis. Cancer Genomics Proteomics. 2017;14:75–82. doi: 10.21873/cgp.20020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Segura MF, Belitskaya-Lévy I, Rose AE, Zakrzewski J, Gaziel A, Hanniford D, Darvishian F, Berman RS, Shapiro RL, Pavlick AC, Osman I, Hernando E. Melanoma MicroRNA signature predicts post-recurrence survival. Clin Cancer Res. 2010;16:1577–86. doi: 10.1158/1078-0432.CCR-09-2721. https://doi.org/10.1158/1078-0432.CCR-09-2721. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Nagano Y, Toiyama Y, Okugawa Y, Imaoka H, Fujikawa H, Yasuda H, Yoshiyama S, Hiro J, Kobayashi M, Ohi M, Araki T, Inoue Y, Mohri Y, Kusunoki M. MicroRNA-7 is associated with malignant potential and poor prognosis in human colorectal cancer. Anticancer Res. 2016;36:6521–26. doi: 10.21873/anticanres.11253. [DOI] [PubMed] [Google Scholar]
  • 20.Ahn DH, Rah H, Choi YK, Jeon YJ, Min KT, Kwack K, Hong SP, Hwang SG, Kim NK. Association of the miR-146aC>G, miR-149T>C, miR-196a2T>C, and miR-499A>G polymorphisms with gastric cancer risk and survival in the Korean population. Mol Carcinog. 2013;52(Suppl 1):E39–51. doi: 10.1002/mc.21962. [DOI] [PubMed] [Google Scholar]
  • 21.Xiong XD, Luo XP, Cheng J, Liu X, Li EM, Zeng LQ. A genetic variant in pre-miR-27a is associated with a reduced cervical cancer risk in southern Chinese women. Gynecol Oncol. 2014;132:450–54. doi: 10.1016/j.ygyno.2013.12.030. [DOI] [PubMed] [Google Scholar]
  • 22.Shi J, Liu Y, Liu J, Zhou J. Hsa-miR-449a genetic variant is associated with risk of gastric cancer in a Chinese population. Int J Clin Exp Pathol. 2015;8:13387–92. [PMC free article] [PubMed] [Google Scholar]
  • 23.Jin X, Yu N. MicroRNA-421 Gene Polymorphism in Gastric Carcinoma. Med Sci Monit. 2016;22:1467–71. doi: 10.12659/MSM.895652. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Kumar MS, Lu J, Mercer KL, Golub TR, Jacks T. Impaired microRNA processing enhances cellular transformation and tumorigenesis. Nat Genet. 2007;39:673–77. doi: 10.1038/ng2003. [DOI] [PubMed] [Google Scholar]
  • 25.Jiang Y, Chen J, Wu J, Hu Z, Qin Z, Liu X, Guan X, Wang Y, Han J, Jiang T, Jin G, Zhang M, Ma H, et al. Evaluation of genetic variants in microRNA biosynthesis genes and risk of breast cancer in Chinese women. Int J Cancer. 2013;133:2216–24. doi: 10.1002/ijc.28237. [DOI] [PubMed] [Google Scholar]
  • 26.Xie Y, Wang Y, Zhao Y, Guo Z. Single-nucleotide polymorphisms of microRNA processing machinery genes are associated with risk for gastric cancer. Onco Targets Ther. 2015;8:567–71. doi: 10.2147/OTT.S79150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Zhao Y, Du Y, Zhao S, Guo Z. Single-nucleotide polymorphisms of microRNA processing machinery genes and risk of colorectal cancer. Onco Targets Ther. 2015;8:421–25. doi: 10.2147/OTT.S78647. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Tchernitsa O, Kasajima A, Schäfer R, Kuban RJ, Ungethüm U, Györffy B, Neumann U, Simon E, Weichert W, Ebert MP, Röcken C. Systematic evaluation of the miRNA-ome and its downstream effects on mRNA expression identifies gastric cancer progression. J Pathol. 2010;222:310–19. doi: 10.1002/path.2759. [DOI] [PubMed] [Google Scholar]
  • 29.Zhang J, Zhang XH, Wang CX, Liu B, Fan XS, Wen JJ, Shi QL, Zhou XJ. Dysregulation of microRNA biosynthesis enzyme Dicer plays an important role in gastric cancer progression. Int J Clin Exp Pathol. 2014;7:1702–07. [PMC free article] [PubMed] [Google Scholar]
  • 30.Osuch-Wojcikiewicz E, Bruzgielewicz A, Niemczyk K, Sieniawska-Buccella O, Nowak A, Walczak A, Majsterek I. Association of Polymorphic Variants of miRNA Processing Genes with Larynx Cancer Risk in a Polish Population. BioMed Res Int. 2015;2015:298378. doi: 10.1155/2015/298378. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Cho SH, Ko JJ, Kim JO, Jeon YJ, Yoo JK, Oh J, Oh D, Kim JW, Kim NK. 3′-UTR Polymorphisms in the MiRNA Machinery Genes DROSHA, DICER1, RAN, and XPO5 Are Associated with Colorectal Cancer Risk in a Korean Population. PLoS One. 2015;10:e0131125. doi: 10.1371/journal.pone.0131125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Persson H, Kvist A, Rego N, Staaf J, Vallon-Christersson J, Luts L, Loman N, Jonsson G, Naya H, Hoglund M, Borg A, Rovira C. Identification of new microRNAs in paired normal and tumor breast tissue suggests a dual role for the ERBB2/Her2 gene. Cancer Res. 2011;71:78–86. doi: 10.1158/0008-5472.CAN-10-1869. [DOI] [PubMed] [Google Scholar]
  • 33.Friedländer MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40:37–52. doi: 10.1093/nar/gkr688. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Fang X, Yin Z, Li X, Xia L, Zhou B. Polymorphisms in GEMIN4 and AGO1 Genes Are Associated with the Risk of Lung Cancer: A Case-Control Study in Chinese Female Non-Smokers. Int J Environ Res Public Health. 2016;13:939. doi: 10.3390/ijerph13100939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Qu Y, Qu H, Luo M, Wang P, Song C, Wang K, Zhang J, Dai L. MicroRNAs related polymorphisms and genetic susceptibility to esophageal squamous cell carcinoma. Mol Genet Genomics. 2014;289:1123–30. doi: 10.1007/s00438-014-0873-x. [DOI] [PubMed] [Google Scholar]
  • 36.Shi Z, Zhang J, Qian X, Han L, Zhang K, Chen L, Liu J, Ren Y, Yang M, Zhang A, Pu P, Kang C. AC1MMYR2, an inhibitor of dicer-mediated biogenesis of Oncomir miR-21, reverses epithelial-mesenchymal transition and suppresses tumor growth and progression. Cancer Res. 2013;73:5519–31. doi: 10.1158/0008-5472.CAN-13-0280. [DOI] [PubMed] [Google Scholar]
  • 37.Iio A, Takagi T, Miki K, Naoe T, Nakayama A, Akao Y. DDX6 post-transcriptionally down-regulates miR-143/145 expression through host gene NCR143/145 in cancer cells. Biochim Biophys Acta. 2013;1829:1102–10. doi: 10.1016/j.bbagrm.2013.07.010. [DOI] [PubMed] [Google Scholar]
  • 38.Kim MN, Kim JO, Lee SM, Park H, Lee JH, Rim KS, Hwang SG, Kim NK. Variation in the Dicer and RAN genes are associated with survival in patients with hepatocellular carcinoma. PLoS One. 2016;11:e0162279. doi: 10.1371/journal.pone.0162279. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Nikolić Z, Savić Pavićević D, Vučić N, Cerović S, Vukotić V, Brajušković G. Genetic variants in RNA-induced silencing complex genes and prostate cancer. World J Urol. 2017;35:613–24. doi: 10.1007/s00345-016-1917-0. [DOI] [PubMed] [Google Scholar]
  • 40.Yang PW, Huang YC, Hsieh CY, Hua KT, Huang YT, Chiang TH, Chen JS, Huang PM, Hsu HH, Kuo SW, Kuo ML, Lee JM. Association of miRNA-related genetic polymorphisms and prognosis in patients with esophageal squamous cell carcinoma. Ann Surg Oncol. 2014;21(Suppl 4):S601–09. doi: 10.1245/s10434-014-3709-3. [DOI] [PubMed] [Google Scholar]
  • 41.Koesters R, Adams V, Betts D, Moos R, Schmid M, Siermann A, Hassam S, Weitz S, Lichter P, Heitz PU, von Knebel Doeberitz M, Briner J. Human eukaryotic initiation factor EIF2C1 gene: cDNA sequence, genomic organization, localization to chromosomal bands 1p34-p35, and expression. Genomics. 1999;61:210–18. doi: 10.1006/geno.1999.5951. [DOI] [PubMed] [Google Scholar]

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