# Quantitative Assessment of the Association between Genetic Variants in MicroRNAs and Colorectal Cancer Risk

Authors: Xiao-Xu Liu; Meng Wang; Dan Xu; Jian-Hai Yang; Hua-Feng Kang; Xi-Jing Wang; Shuai Lin; Peng-Tao Yang; Xing-Han Liu; Zhi-Jun Dai
Journal: BioMed Research International
 (2015)

---

## Abstract

Background. The associations between polymorphisms in microRNAs and the susceptibility of colorectal cancer (CRC) were inconsistent in previous studies. This study aims to quantify the strength of the correlation between the four common polymorphisms among microRNAs (hsa-mir-146a rs2910164, hsa-mir-149 rs2292832, hsa-mir-196a2 rs11614913, and hsa-mir-499 rs3746444) and CRC risk. Methods. We searched PubMed, Web of Knowledge, and CNKI to find relevant studies. The combined odds ratio (OR) with 95% confidence interval (95% CI) was used to estimate the strength of the association in a fixed or random effect model. Results. 15 studies involving 5,486 CRC patients and 7,184 controls were included. Meta-analyses showed that rs3746444 had association with CRC risk in Caucasians (OR = 0.57, 95% CI = 0.34–0.95). In the subgroup analysis, we found significant associations between rs2910164 and CRC in hospital based studies (OR = 1.24, 95% CI = 1.03–1.49). rs2292832 may be a high risk factor of CRC in population based studied (OR = 1.18, 95% CI = 1.08–1.38). Conclusion. This meta-analysis showed that rs2910164 and rs2292832 may increase the risk of CRC. However, rs11614913 polymorphism may reduce the risk of CRC. rs3746444 may have a decreased risk to CRC in Caucasians.


---

## Body

## 1. Introduction

Colorectal cancer (CRC) is the third leading cause of cancer death for both men and women in the USA [1]. In Europe, CRC represents one of the primary causes of cancer deaths [2], and, in Asia, CRC is the fourth leading cause of mortality by cancer, and its incidence is increasing [3]. Epidemiological evidences have suggested that the risk of colorectal neoplasm is affected by multiple factors including a positive family history, excessive meat consumption, smoking status, and alcohol consumption, as well as genetic alterations, such as genetic polymorphisms [2].However, the mechanism of colorectal carcinogenesis is still not fully understood. Compared with other complex diseases, CRC may be caused by both genetic and environmental factors [4]. Because well-recognized genetic predisposition syndromes account for less than 3% of CRC, low-penetrance genetic factors alone or in combination with environmental factors probably contribute to the development of CRC [5].MicroRNAs (miRNAs) are a large class of small noncoding RNAs, which participate in various biological processes and may regulate tumor suppressor genes or oncogenes [6]. Single nucleotide polymorphisms (SNPs) in miRNAs may alter the expression, processing, and transcription of miRNAs and thus contribute to cancer development [7]. Recently, many epidemiological studies have demonstrated that some SNPs in the miRNA genes could alter miRNA expression and/or maturation and also be associated with the susceptibility and progression of cancer [8]. Rs2910164 in hsa-mir-146a locus resides at position +60 relative to the first nucleotide of the pre-miR-146a gene. The previous studies have shown that miR-146a plays an important role in cell proliferation and metastatic ability in cancers and the variation of miR-146a may be involved in carcinogenesis [9, 10]. MiR-196a2 includes two different mature miRNAs (miR-196a-5P and miR-196a-3P) and rs11614913 located in the mature sequence of miR-196a-3P. Rs11614913 could influence the expression levels of mature miR-196a and may have an impact on the expression of its target gene, which may play a role in regulating carcinogenesis [10]. It has been identified that rs3746444 is located in the stem region of the mir-499 gene. And several studies have identified miR-499 rs3746444 as a possible biomarker for some cancers [11–13]. Compared with other three genes, there were few studies of rs2292932 in miR-149 up to now. But the T allele in miR-149 might be masked by the presence of other unidentified causal genes involved in cancer development [14]. Furthermore, several Genome-wide association studies have reported that these four common SNPs may be associated with cancer risk [8]. However, results of these studies remain inconsistent. To provide a more precise estimate of the association of miRNA polymorphisms with CRC risk, we performed a meta-analysis on all eligible published case-control studies and evaluated the effect of the four SNPs on CRC risk.


## 2. Methods

### 2.1. Publication Search

Relevant articles were independently searched by two authors (Wang M and Dai ZJ) in PubMed, Web of Science, and Chinese National Knowledge Infrastructure (CNKI). The keywords were as follows: colorectal cancer/colorectal carcinoma, microRNA 146a/149/196a2/499, and polymorphism/genotype/variant/SNP. All qualified studies prior to December 20, 2014, were included. The eligible literature must be published in English or Chinese. Furthermore, reference lists of main reports and review articles were also reviewed manually to identify additional relevant publications.


### 2.2. Selection Criteria

The following criteria were used to select eligible studies for further meta-analysis: (1) case-control design; (2) full-text study; (3) studies that evaluated the associations between miRNA polymorphisms and CRC risk; and (4) studies that included detailed genotyping data.


### 2.3. Data Extraction

Articles were reviewed independently by two reviewers and data with discrepancies were discussed by all authors. For each included study, the following information was collected: first author, year of publication, country of origin, ethnicity, source of control, total number of cases and controls, and genotyping methods as well as number of cases and controls with the different genotypes. Different ethnic groups were categorized as Caucasian, Asian, African, and “mixed.” All the case and control groups were well controlled.


### 2.4. Statistical Analysis

The associations between miRNA polymorphisms and CRC risk were measured by odds ratio (OR) with 95% confidence interval (CI). The significance of the pooled OR was determined by theZ test.The meta-analysis assessed association by using 5 different genetic models: homozygous genetic model, heterozygote genetic model, dominant genetic model, recessive genetic model, and allelic model. Hardy-Weinberg equilibrium (HWE) among the control subjects was tested by the Chi-square test. Statistical heterogeneity among studies was assessed with theQ and I
2 statistics. If the P value of heterogeneity test was more than 0.05 (P
>
0
.
05), the pooled OR estimate of the study was calculated by the fixed-effects model. Otherwise, the random-effects model was used. The value of the I index was used to assess the degree of heterogeneity (I
2
<
25%: no heterogeneity; 25
%
<
I
2
<
50%: moderate heterogeneity; 50
%
<
I
2
<
75%: high heterogeneity; and I
2
>
75%: extreme high heterogeneity). Publication bias was evaluated by the funnel plot. All statistical analyses were carried out with the review manager version 5.1 (Revman; The Cochrane Collaboration, Oxford, UK).


## 2.1. Publication Search

Relevant articles were independently searched by two authors (Wang M and Dai ZJ) in PubMed, Web of Science, and Chinese National Knowledge Infrastructure (CNKI). The keywords were as follows: colorectal cancer/colorectal carcinoma, microRNA 146a/149/196a2/499, and polymorphism/genotype/variant/SNP. All qualified studies prior to December 20, 2014, were included. The eligible literature must be published in English or Chinese. Furthermore, reference lists of main reports and review articles were also reviewed manually to identify additional relevant publications.


## 2.2. Selection Criteria

The following criteria were used to select eligible studies for further meta-analysis: (1) case-control design; (2) full-text study; (3) studies that evaluated the associations between miRNA polymorphisms and CRC risk; and (4) studies that included detailed genotyping data.


## 2.3. Data Extraction

Articles were reviewed independently by two reviewers and data with discrepancies were discussed by all authors. For each included study, the following information was collected: first author, year of publication, country of origin, ethnicity, source of control, total number of cases and controls, and genotyping methods as well as number of cases and controls with the different genotypes. Different ethnic groups were categorized as Caucasian, Asian, African, and “mixed.” All the case and control groups were well controlled.


## 2.4. Statistical Analysis

The associations between miRNA polymorphisms and CRC risk were measured by odds ratio (OR) with 95% confidence interval (CI). The significance of the pooled OR was determined by theZ test.The meta-analysis assessed association by using 5 different genetic models: homozygous genetic model, heterozygote genetic model, dominant genetic model, recessive genetic model, and allelic model. Hardy-Weinberg equilibrium (HWE) among the control subjects was tested by the Chi-square test. Statistical heterogeneity among studies was assessed with theQ and I
2 statistics. If the P value of heterogeneity test was more than 0.05 (P
>
0
.
05), the pooled OR estimate of the study was calculated by the fixed-effects model. Otherwise, the random-effects model was used. The value of the I index was used to assess the degree of heterogeneity (I
2
<
25%: no heterogeneity; 25
%
<
I
2
<
50%: moderate heterogeneity; 50
%
<
I
2
<
75%: high heterogeneity; and I
2
>
75%: extreme high heterogeneity). Publication bias was evaluated by the funnel plot. All statistical analyses were carried out with the review manager version 5.1 (Revman; The Cochrane Collaboration, Oxford, UK).


## 3. Results

### 3.1. Characteristics of Studies

According to the inclusion criteria defined above (Figure1), fifteen studies on miRNA polymorphisms with CRC risk were identified [8], including 5,486 CRC patients and 7,184 cancer-free controls. All the included eligible studies were published in English or Chinese. Among the eligible fifteen studies, eleven studies were based on Asian backgrounds which were carried out in China and Korea. Only four studies were based on Caucasian, having been carried out in Greek, Lithuania, Italy, and Czech, respectively. Simultaneously, there were seven hospital based studies and eight population based studies. Main characteristics of the included studies were listed in Table 1.Table 1
Characteristics of the studies included in the meta-analysis.
First author
Year
Country
Ethnicity
Genotyping method
Source of control
Number (case/control)
SNP number
Dikaiakos [28]
2015
Greek
Caucasian
PCR-RFLP
HB
157/299
1, 3, 4
Kupcinskas [29]
2014
Lithuania, Latvia
Caucasian
TaqMan
HB
193/428
1, 3
Mao [30]
2014
China
Asian
SNPScan
PB
554/566
1
Hu [31]
2014
China
Asian
PCR-RFLP
HB
276/373
1, 4
Wu [15]
2014
China
Asian
ASA
HB
175/300
1, 2, 4
Lv [8]
2013
China
Asian
PCR-RFLP
PB
353/540
1, 2, 3, 4
Chae [21]
2013
Korea
Asian
PCR-RFLP
PB
399/568
1
Ma [32]
2013
China
Asian
TaqMan
PB
1147/1203
1
Vinci [16]
2013
Italy
Caucasian
TaqMan
HB
160/178
1, 2, 3, 4
Zhang [17]
2012
China
Asian
PCR-RFLP
PB
478/477
2, 3
Hezova [20]
2012
Czech
Caucasian
TaqMan
HB
197/212
1, 3
Min [22]
2012
Korea
Asian
PCR-RFLP
PB
446/502
1, 2, 3, 4
Zhu [33]
2012
China
Asian
TaqMan
PB
573/588
3
Chen [34]
2012
China
Asian
PCR-LDR
HB
126/407
3
Zhan [35]
2011
China
Asian
PCR-RFLP
PB
252/543
3
HWE: Hardy-Weinberg equilibrium; PB: population based; HB: hospital based; PCR: polymerase chain reaction; RFLP: restriction fragment length polymorphism; LDR: ligation detection reaction; ASA: allele-specific amplification; SNP: single-nucleotide polymorphisms; SNP number 1: miR-146-a G>C (rs2910164); 2: miR-149 C>T (rs2292832); 3: miR-196a-2 C>T (rs11614913); 4: miR-499 A>G (rs3746444).Figure 1
Flow chart of study selection.


### 3.2. Meta-Analysis Results

As shown in Table2, the frequencies of the minor allele varied widely across the eligible studies, ranging from 0.14 to 0.80 (rs2910164), 0.33 to 0.82 (rs2292832), 0.34 to 0.66 (rs11614913), and 0.14 to 0.68 (rs3746444). The average frequencies of the minor allele in the four polymorphisms were 0.47, 0.66, 0.51, and 0.27, respectively. The distributions of genotypes in the controls were all in agreement with HWE except four studies [8, 15–17].Table 2
MiRNA polymorphisms genotype distribution and allele frequency in cases and controls.
First author
Genotype (N)
Allele frequency (N)
Case
Control
Case
Control
MAF
HWE  (P value)
Total
AA
Aa
aa
Total
AA
Aa
aa
A
a
A
a
rs2910164
Dikaiakos 2015 [28]
157
8
48
101
299
21
120
158
64
250
162
436
0.80
0.782
Kupcinskas 2014 [29]
193
140
50
2
428
275
134
15
330
54
684
164
0.14
0.789
Mao 2014 [30]
554
70
291
186
566
85
271
205
431
663
441
681
0.61
0.768
Hu 2014 [31]
200
34
82
84
373
44
187
142
250
150
275
471
0.38
0.137
Wu 2014 [15]
175
22
59
80
300
53
120
114
103
219
226
348
0.68
0.035
Lv 2013 [8]
331
54
230
47
513
96
274
143
338
324
560
466
0.49
0.080
Chae 2013 [21]
399
61
182
156
568
121
282
165
304
494
524
612
0.62
0.980
Ma 2013 [36]
1147
444
534
169
1203
397
614
192
1422
872
1408
998
0.38
0.075
Vinci 2013 [16]
160
17
57
86
178
13
65
100
91
229
91
265
0.72
0.590
Hezova 2012 [20]
197
115
70
12
212
124
79
9
300
94
327
97
0.24
0.415
Min 2012 [22]
446
151
233
62
502
188
245
69
535
357
621
383
0.40
0.443
rs2292832
Wu 2014 [15]
175
21
123
28
300
76
58
116
165
179
210
290
0.52
<0.001
Lv 2013 [8]
347
30
64
253
459
48
103
308
124
570
199
719
0.82
<0.001
Vinci 2013 [16]
160
79
58
23
178
86
75
17
216
104
247
109
0.33
0.912
Zhang 2012 [14]
443
50
190
203
435
46
202
187
290
596
294
576
0.67
0.431
Min 2012 [22]
446
48
177
221
502
51
219
232
273
619
321
683
0.69
0.948
rs11614913
Dikaiakos 2015 [28]
157
19
69
69
299
33
149
117
107
207
215
383
0.66
0.156
Kupcinskas 2014 [29]
193
79
87
27
428
199
174
54
245
141
572
282
0.37
0.104
Lv 2013 [8]
374
10
223
114
531
109
331
91
243
451
549
413
0.60
<0.001
Vinci 2013 [16]
160
62
86
12
178
83
84
11
210
110
250
106
0.34
0.087
Hezova 2012 [20]
197
82
89
26
212
87
103
22
253
141
277
147
0.36
0.291
Zhang 2012 [14]
455
79
204
172
463
81
197
185
362
548
359
567
0.60
0.026
Min 2012 [22]
466
120
201
125
502
100
254
148
441
451
454
550
0.48
0.633
Zhu 2012 [33]
573
140
303
130
588
121
295
172
583
563
537
639
0.49
0.790
Chen 2012 [34]
126
27
64
35
407
94
206
107
118
134
394
420
0.53
0.788
Zhan 2011 [35]
252
68
128
56
543
112
267
163
264
240
493
593
0.48
0.890
rs3746444
Dikaiakos 2015 [28]
157
85
64
8
299
182
99
18
234
80
463
135
0.25
0.361
Hu 2014 [31]
211
157
49
5
373
282
81
10
363
59
654
101
0.14
0.162
Wu 2014 [15]
175
96
17
38
300
141
44
63
209
93
326
170
0.31
<0.001
Lv 2013 [8]
346
258
88
504
366
138
—
—
Vinci 2013 [16]
160
35
32
93
178
17
56
105
102
218
90
266
0.68
0.026
Min 2012 [22]
446
292
142
12
502
334
154
14
726
166
822
182
0.19
0.453
A: the major allele; a: the minor allele; MAF: minor allele frequencies.The main results of this meta-analysis were presented in Table S1, in Supplementary Material available online athttp://dx.doi.org/10.1155/2015/276410. There were 11 studies with 3,937 cases and 5,120 controls for rs2910164. miR-146a rs2910164 polymorphism has no association with CRC risk in the overall population (C versus G: OR = 0.96, 95% CI = 0.82–1.12, P
=
0.62, Figure 2; CC versus GG: OR = 1.02, 95% CI = 0.78–1.33, P
=
0.89; GC versus GG: OR = 1.00, 95% CI = 0.83–1.20, P
=
0.97; CC versus GG + GC: OR = 1.03, 95% CI = 0.80–1.31, P
=
0.83; GC + CC versus GG: OR = 1.01, 95% CI = 0.84–1.22, P
=
0
.
89). After omitting the study which was not according with the HWE, the results were in accordance with the overall population (C versus G: OR = 0.93, 95% CI = 0.79–1.09, P
=
0.38; CC versus GG: OR = 0.97, 95% CI = 0.73–1.28, P
=
0.83; GC versus GG: OR = 0.98, 95% CI = 0.81–1.20, P
=
0.88; CC versus GG + GC: OR = 0.99, 95% CI = 0.76–1.27, P
=
0.91; GC + CC versus GG: OR = 0.99, 95% CI = 0.82–1.20, P
=
0.90). When stratifying analysis by ethnicity, there was also no significant association observed between the rs2910164 and CRC susceptibility in the five genetic models. In the analysis stratified by the source of control, significant associations were observed in the hospital based studies for recessive genetic model (CC versus GG + GC: OR = 1.24, 95% CI = 1.03–1.49, P
=
0.02). No associations were found in population based studies.Figure 2
Forest plot of miR-146a rs2910164 polymorphism and CRC risk (C versus G). The squares and horizontal lines correspond to the study specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.The association of the rs2292832 polymorphism with CRC susceptibility was investigated in 5 studies with 1,568 cases and 1,824 controls. We failed to find any significant associations in any genotype (T versus C: OR = 1.05, 95% CI = 0.95–1.17,P
=
0
.
36, Figure 3; TT versus CC: OR = 1.09 95% CI = 0.87–1.37, P
=
0
.
44; CT versus CC: OR = 1.35, 95% CI = 0.64–2.86, P
=
0
.
43; TT versus CC + CT: OR = 0.91, 95% CI = 0.54–1.53, P
=
0
.
71; TT + CT versus CC: OR = 1.24, 95% CI = 0.82–1.87, P
=
0
.
31). And there was also no association existing in any genetic models after we rejected the studies which were not in agreement with HWE. There was only one study based on Caucasian. When excluding the Caucasian study, the null association remained in Asians. We further made stratified analysis based on the source of control. And we observed significant associations in population based studies in the recessive genetic model (TT versus CC + CT: OR = 1.18, 95% CI = 1.08–1.38, P
=
0
.
04).Figure 3
Forest plot of miR-149 rs2292832 polymorphism and CRC risk (T versus C). The squares and horizontal lines correspond to the study specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.Ten studies with 2,906 cases and 4,150 controls were used to evaluate the relationship between rs11614913 polymorphism and CRC risk. No significant association was detected under all the genetic models (haploid model: OR = 1.10, 95% CI = 0.84–1.43,P
=
0
.
50; homozygote comparison: OR = 1.18, 95% CI = 0.76–1.83, P
=
0
.
47; heterozygote comparison: OR = 1.11, 95% CI = 0.82–1.50, P
=
0
.
49; dominant model: OR = 1.13, 95% CI = 0.83–1.55, P
=
0
.
44 and recessive model: OR = 1.07, 95% CI = 0.82–1.38, P
=
0
.
63, Figure 4). When excluding the studies which were inconsistent with HWE, the results showed a significant association between rs11614913 polymorphism and CRC risk in the homozygous genetic model (TT versus CC: OR = 0.80, 95% CI = 0.68–0.95, P
=
0
.
009). Further subgroup analysis by ethnicity showed no association between rs11614913 and CRC risk either in Caucasians or in Asians. And no significant associations were observed in population based studies and hospital based studies for all genetic models.Figure 4
Forest plot of miR-196a2 rs11614913 polymorphism and CRC risk (TT versus CC + CT). The squares and horizontal lines correspond to the study specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.For miR-499 rs3746444 polymorphism, our meta-analysis contained 6 studies with 1,471 cases and 2,104 controls. Overall, the rs3746444 polymorphism has no association with CRC risk (haploid model: OR = 0.96, 95% CI = 0.84–1.10,P
=
0
.
58; homozygote comparison: OR = 0.90, 95% CI = 0.67–1.22, P
=
0
.
50; heterozygote comparison: OR = 0.83, 95% CI = 0.54–1.26, P
=
0
.
38; dominant model: OR = 0.91, 95% CI = 0.71–1.17, P
=
0
.
45 and recessive model: OR = 0.95, 95% CI = 0.73–1.25, P
=
0
.
73). Further analysis of the studies which were in agreement with HWE also showed no association between the rs3746444 polymorphism and CRC. And null associations were found in hospital based studies or population based studies. However, in the stratified analysis according to ethnicity, the homozygote model demonstrated a significant decrease in the CRC risk in Caucasians (GG versus AA: OR = 0.57, 95% CI = 0.34–0.95, P
=
0
.
03, Figure 5).Figure 5
Forest plot of miR-499 rs3746444 polymorphism and CRC risk (GG versus AA). The squares and horizontal lines correspond to the study specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.


### 3.3. Publication Bias

In this meta-analysis, we performed funnel plot to access the publication bias. As showed in Figure6, the funnel plots failed to reveal any obvious asymmetry in all genotypes in overall population. Therefore, the results indicated that publication bias was not significant in this meta-analysis.Figure 6
Funnel plot assessing evidence of publication bias from the eligible studies. (a) miR-146a rs2910164; (b) miR-149 rs2292832; (c) miR-196a2 rs11614913; (d) miR-499 rs3746444.
(a)
(b)
(c)
(d)


## 3.1. Characteristics of Studies

According to the inclusion criteria defined above (Figure1), fifteen studies on miRNA polymorphisms with CRC risk were identified [8], including 5,486 CRC patients and 7,184 cancer-free controls. All the included eligible studies were published in English or Chinese. Among the eligible fifteen studies, eleven studies were based on Asian backgrounds which were carried out in China and Korea. Only four studies were based on Caucasian, having been carried out in Greek, Lithuania, Italy, and Czech, respectively. Simultaneously, there were seven hospital based studies and eight population based studies. Main characteristics of the included studies were listed in Table 1.Table 1
Characteristics of the studies included in the meta-analysis.
First author
Year
Country
Ethnicity
Genotyping method
Source of control
Number (case/control)
SNP number
Dikaiakos [28]
2015
Greek
Caucasian
PCR-RFLP
HB
157/299
1, 3, 4
Kupcinskas [29]
2014
Lithuania, Latvia
Caucasian
TaqMan
HB
193/428
1, 3
Mao [30]
2014
China
Asian
SNPScan
PB
554/566
1
Hu [31]
2014
China
Asian
PCR-RFLP
HB
276/373
1, 4
Wu [15]
2014
China
Asian
ASA
HB
175/300
1, 2, 4
Lv [8]
2013
China
Asian
PCR-RFLP
PB
353/540
1, 2, 3, 4
Chae [21]
2013
Korea
Asian
PCR-RFLP
PB
399/568
1
Ma [32]
2013
China
Asian
TaqMan
PB
1147/1203
1
Vinci [16]
2013
Italy
Caucasian
TaqMan
HB
160/178
1, 2, 3, 4
Zhang [17]
2012
China
Asian
PCR-RFLP
PB
478/477
2, 3
Hezova [20]
2012
Czech
Caucasian
TaqMan
HB
197/212
1, 3
Min [22]
2012
Korea
Asian
PCR-RFLP
PB
446/502
1, 2, 3, 4
Zhu [33]
2012
China
Asian
TaqMan
PB
573/588
3
Chen [34]
2012
China
Asian
PCR-LDR
HB
126/407
3
Zhan [35]
2011
China
Asian
PCR-RFLP
PB
252/543
3
HWE: Hardy-Weinberg equilibrium; PB: population based; HB: hospital based; PCR: polymerase chain reaction; RFLP: restriction fragment length polymorphism; LDR: ligation detection reaction; ASA: allele-specific amplification; SNP: single-nucleotide polymorphisms; SNP number 1: miR-146-a G>C (rs2910164); 2: miR-149 C>T (rs2292832); 3: miR-196a-2 C>T (rs11614913); 4: miR-499 A>G (rs3746444).Figure 1
Flow chart of study selection.


## 3.2. Meta-Analysis Results

As shown in Table2, the frequencies of the minor allele varied widely across the eligible studies, ranging from 0.14 to 0.80 (rs2910164), 0.33 to 0.82 (rs2292832), 0.34 to 0.66 (rs11614913), and 0.14 to 0.68 (rs3746444). The average frequencies of the minor allele in the four polymorphisms were 0.47, 0.66, 0.51, and 0.27, respectively. The distributions of genotypes in the controls were all in agreement with HWE except four studies [8, 15–17].Table 2
MiRNA polymorphisms genotype distribution and allele frequency in cases and controls.
First author
Genotype (N)
Allele frequency (N)
Case
Control
Case
Control
MAF
HWE  (P value)
Total
AA
Aa
aa
Total
AA
Aa
aa
A
a
A
a
rs2910164
Dikaiakos 2015 [28]
157
8
48
101
299
21
120
158
64
250
162
436
0.80
0.782
Kupcinskas 2014 [29]
193
140
50
2
428
275
134
15
330
54
684
164
0.14
0.789
Mao 2014 [30]
554
70
291
186
566
85
271
205
431
663
441
681
0.61
0.768
Hu 2014 [31]
200
34
82
84
373
44
187
142
250
150
275
471
0.38
0.137
Wu 2014 [15]
175
22
59
80
300
53
120
114
103
219
226
348
0.68
0.035
Lv 2013 [8]
331
54
230
47
513
96
274
143
338
324
560
466
0.49
0.080
Chae 2013 [21]
399
61
182
156
568
121
282
165
304
494
524
612
0.62
0.980
Ma 2013 [36]
1147
444
534
169
1203
397
614
192
1422
872
1408
998
0.38
0.075
Vinci 2013 [16]
160
17
57
86
178
13
65
100
91
229
91
265
0.72
0.590
Hezova 2012 [20]
197
115
70
12
212
124
79
9
300
94
327
97
0.24
0.415
Min 2012 [22]
446
151
233
62
502
188
245
69
535
357
621
383
0.40
0.443
rs2292832
Wu 2014 [15]
175
21
123
28
300
76
58
116
165
179
210
290
0.52
<0.001
Lv 2013 [8]
347
30
64
253
459
48
103
308
124
570
199
719
0.82
<0.001
Vinci 2013 [16]
160
79
58
23
178
86
75
17
216
104
247
109
0.33
0.912
Zhang 2012 [14]
443
50
190
203
435
46
202
187
290
596
294
576
0.67
0.431
Min 2012 [22]
446
48
177
221
502
51
219
232
273
619
321
683
0.69
0.948
rs11614913
Dikaiakos 2015 [28]
157
19
69
69
299
33
149
117
107
207
215
383
0.66
0.156
Kupcinskas 2014 [29]
193
79
87
27
428
199
174
54
245
141
572
282
0.37
0.104
Lv 2013 [8]
374
10
223
114
531
109
331
91
243
451
549
413
0.60
<0.001
Vinci 2013 [16]
160
62
86
12
178
83
84
11
210
110
250
106
0.34
0.087
Hezova 2012 [20]
197
82
89
26
212
87
103
22
253
141
277
147
0.36
0.291
Zhang 2012 [14]
455
79
204
172
463
81
197
185
362
548
359
567
0.60
0.026
Min 2012 [22]
466
120
201
125
502
100
254
148
441
451
454
550
0.48
0.633
Zhu 2012 [33]
573
140
303
130
588
121
295
172
583
563
537
639
0.49
0.790
Chen 2012 [34]
126
27
64
35
407
94
206
107
118
134
394
420
0.53
0.788
Zhan 2011 [35]
252
68
128
56
543
112
267
163
264
240
493
593
0.48
0.890
rs3746444
Dikaiakos 2015 [28]
157
85
64
8
299
182
99
18
234
80
463
135
0.25
0.361
Hu 2014 [31]
211
157
49
5
373
282
81
10
363
59
654
101
0.14
0.162
Wu 2014 [15]
175
96
17
38
300
141
44
63
209
93
326
170
0.31
<0.001
Lv 2013 [8]
346
258
88
504
366
138
—
—
Vinci 2013 [16]
160
35
32
93
178
17
56
105
102
218
90
266
0.68
0.026
Min 2012 [22]
446
292
142
12
502
334
154
14
726
166
822
182
0.19
0.453
A: the major allele; a: the minor allele; MAF: minor allele frequencies.The main results of this meta-analysis were presented in Table S1, in Supplementary Material available online athttp://dx.doi.org/10.1155/2015/276410. There were 11 studies with 3,937 cases and 5,120 controls for rs2910164. miR-146a rs2910164 polymorphism has no association with CRC risk in the overall population (C versus G: OR = 0.96, 95% CI = 0.82–1.12, P
=
0.62, Figure 2; CC versus GG: OR = 1.02, 95% CI = 0.78–1.33, P
=
0.89; GC versus GG: OR = 1.00, 95% CI = 0.83–1.20, P
=
0.97; CC versus GG + GC: OR = 1.03, 95% CI = 0.80–1.31, P
=
0.83; GC + CC versus GG: OR = 1.01, 95% CI = 0.84–1.22, P
=
0
.
89). After omitting the study which was not according with the HWE, the results were in accordance with the overall population (C versus G: OR = 0.93, 95% CI = 0.79–1.09, P
=
0.38; CC versus GG: OR = 0.97, 95% CI = 0.73–1.28, P
=
0.83; GC versus GG: OR = 0.98, 95% CI = 0.81–1.20, P
=
0.88; CC versus GG + GC: OR = 0.99, 95% CI = 0.76–1.27, P
=
0.91; GC + CC versus GG: OR = 0.99, 95% CI = 0.82–1.20, P
=
0.90). When stratifying analysis by ethnicity, there was also no significant association observed between the rs2910164 and CRC susceptibility in the five genetic models. In the analysis stratified by the source of control, significant associations were observed in the hospital based studies for recessive genetic model (CC versus GG + GC: OR = 1.24, 95% CI = 1.03–1.49, P
=
0.02). No associations were found in population based studies.Figure 2
Forest plot of miR-146a rs2910164 polymorphism and CRC risk (C versus G). The squares and horizontal lines correspond to the study specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.The association of the rs2292832 polymorphism with CRC susceptibility was investigated in 5 studies with 1,568 cases and 1,824 controls. We failed to find any significant associations in any genotype (T versus C: OR = 1.05, 95% CI = 0.95–1.17,P
=
0
.
36, Figure 3; TT versus CC: OR = 1.09 95% CI = 0.87–1.37, P
=
0
.
44; CT versus CC: OR = 1.35, 95% CI = 0.64–2.86, P
=
0
.
43; TT versus CC + CT: OR = 0.91, 95% CI = 0.54–1.53, P
=
0
.
71; TT + CT versus CC: OR = 1.24, 95% CI = 0.82–1.87, P
=
0
.
31). And there was also no association existing in any genetic models after we rejected the studies which were not in agreement with HWE. There was only one study based on Caucasian. When excluding the Caucasian study, the null association remained in Asians. We further made stratified analysis based on the source of control. And we observed significant associations in population based studies in the recessive genetic model (TT versus CC + CT: OR = 1.18, 95% CI = 1.08–1.38, P
=
0
.
04).Figure 3
Forest plot of miR-149 rs2292832 polymorphism and CRC risk (T versus C). The squares and horizontal lines correspond to the study specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.Ten studies with 2,906 cases and 4,150 controls were used to evaluate the relationship between rs11614913 polymorphism and CRC risk. No significant association was detected under all the genetic models (haploid model: OR = 1.10, 95% CI = 0.84–1.43,P
=
0
.
50; homozygote comparison: OR = 1.18, 95% CI = 0.76–1.83, P
=
0
.
47; heterozygote comparison: OR = 1.11, 95% CI = 0.82–1.50, P
=
0
.
49; dominant model: OR = 1.13, 95% CI = 0.83–1.55, P
=
0
.
44 and recessive model: OR = 1.07, 95% CI = 0.82–1.38, P
=
0
.
63, Figure 4). When excluding the studies which were inconsistent with HWE, the results showed a significant association between rs11614913 polymorphism and CRC risk in the homozygous genetic model (TT versus CC: OR = 0.80, 95% CI = 0.68–0.95, P
=
0
.
009). Further subgroup analysis by ethnicity showed no association between rs11614913 and CRC risk either in Caucasians or in Asians. And no significant associations were observed in population based studies and hospital based studies for all genetic models.Figure 4
Forest plot of miR-196a2 rs11614913 polymorphism and CRC risk (TT versus CC + CT). The squares and horizontal lines correspond to the study specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.For miR-499 rs3746444 polymorphism, our meta-analysis contained 6 studies with 1,471 cases and 2,104 controls. Overall, the rs3746444 polymorphism has no association with CRC risk (haploid model: OR = 0.96, 95% CI = 0.84–1.10,P
=
0
.
58; homozygote comparison: OR = 0.90, 95% CI = 0.67–1.22, P
=
0
.
50; heterozygote comparison: OR = 0.83, 95% CI = 0.54–1.26, P
=
0
.
38; dominant model: OR = 0.91, 95% CI = 0.71–1.17, P
=
0
.
45 and recessive model: OR = 0.95, 95% CI = 0.73–1.25, P
=
0
.
73). Further analysis of the studies which were in agreement with HWE also showed no association between the rs3746444 polymorphism and CRC. And null associations were found in hospital based studies or population based studies. However, in the stratified analysis according to ethnicity, the homozygote model demonstrated a significant decrease in the CRC risk in Caucasians (GG versus AA: OR = 0.57, 95% CI = 0.34–0.95, P
=
0
.
03, Figure 5).Figure 5
Forest plot of miR-499 rs3746444 polymorphism and CRC risk (GG versus AA). The squares and horizontal lines correspond to the study specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.


## 3.3. Publication Bias

In this meta-analysis, we performed funnel plot to access the publication bias. As showed in Figure6, the funnel plots failed to reveal any obvious asymmetry in all genotypes in overall population. Therefore, the results indicated that publication bias was not significant in this meta-analysis.Figure 6
Funnel plot assessing evidence of publication bias from the eligible studies. (a) miR-146a rs2910164; (b) miR-149 rs2292832; (c) miR-196a2 rs11614913; (d) miR-499 rs3746444.
(a)
(b)
(c)
(d)


## 4. Discussion

Allelic variants in the sequence of mature miRNAs represent a particularly interesting potential source of phenotypic diversity of genetic diseases, which may contribute directly to disease susceptibility [18]. Accumulating evidence has shown that miRNAs regulate the expression of roughly 30% of the all human genes through posttranscriptional mechanisms [19]. SNPs in miRNA genes could function through three ways: firstly, the transcription of the primary transcript; secondly, pri-miRNA and pre-miRNA processing; and thirdly, effects on miRNA-mRNA interactions [7]. Genetic effects connected to SNPs at the level of miRNA genes may have a significant relationship with the expression and clinical features in CRC [16].A large number of studies had highlighted several associations between SNPs in miRNAs and the risk of CRC [8]. However, controversial experimental data were obtained. For example, there was no significant association between rs2910164 and CRC in Hezova et al.’s study [20], whereas Chae et al. reported that miR-146a rs2910164 polymorphism of genotype CC may contribute to a higher risk of CRC [21]. Min et al. from Korea [22] demonstrated that miR-196a2 rs11614913 polymorphism had a decreased risk to CRC, whereas Lv et al. [8] reported that T allele in rs11614913 was associated with an increased risk of CRC compared with the C allele.The present meta-analysis, including 5,486 CRC patients and 7,184 cancer-free controls from 15 case-control studies, was conducted to evaluate the association between the four common SNPs in miRNAs (miR-146a rs2910164, miR-149 rs2292832, miR-196a2 rs11614913, and miR-499 rs3746444) and CRC risk. In this study, we found that rs3746444 polymorphism has no association with CRC risk in the overall population. However, accumulative data from two studies based on Caucasian background (317 cases and 477 controls) showed significantly decreased cancer risks in the homozygous model. Considering the limited sample size, we need more large well-designed studies to evaluate the result. Simultaneously, we failed to find any significant correlation between the rs2910164, rs2292832, and rs11614913 polymorphisms and risk of CRC in overall studies or in different ethnic groups for all genetic models. But significant association between rs2910164 and CRC was observed in hospital based studies in the recessive model. And we found that rs2292832 polymorphisms may be associated with increased the risk of CRC in population based studies for recessive model. While T allele of rs11614913 was a protect factor of CRC in the homozygous model when we excluded the studies which were not in agreement with HWE.Although the biological mechanism of SNPs in miRNAs contributing to the regulation of cancer susceptibility and development remains unknown, a large quantity of meta-analyses has been engaged in the newly developed field in the past few years [23]. A previous meta-analysis by K. Srivastava and A. Srivastava [23] was demonstrated that miR-196a2 rs11614913 polymorphisms have significant associations with overall cancer risk. In Xu et al.’s meta-analysis [24], TT genotype of rs11614913 polymorphism was associated with decreased cancer risk. Rs2910164 C allele was associated with decreased overall cancer risk especially for cervical cancer and prostate cancer risk in Chinese population. Rs3746444 G allele was a risk factor in Chinese population, especially for breast cancer. Different results were also presented in several meta-analyses which evaluated the relationship between miRNAs and CRC risk [25]. Wan et al.’s study demonstrates that miR-196a2 rs11614913 most likely contributes to decreased risk of CRC, whereas miR-146a rs2910164 may not be associated with the susceptibility to CRC [25]. While the pooled data from Du et al.’s meta-analysis supported that the miR-196a2 rs11614913 and miR-149 rs2292832 polymorphisms may contribute to susceptibility to CRC [26]. The latest meta-analysis, which related to the associations between miRNA polymorphisms and colorectal cancer, performed by Wu et al. indicated that SNP rs11614913 but not SNP rs2910164 and SNP rs2292832 may contribute to susceptibility to CRC in an Asian-specific manner [27]. This result was unconformable with our results. Compared with Wu’s meta-analysis which included 9 relevant studies, our research totally identified 15 studies including 5,486 CRC cases and 7,184 controls. Thus, our meta-analysis contained the newest data and largest sample size on the study of the relationship between miRNAs and colorectal cancer. Additionally, we performed subgroup analyses by different data information in more detail.Some limitations still existed in this meta-analysis. Firstly, the included studies are mainly based on Asian background. There were only five studies based on Caucasian background and no studies on African background. Secondly, some detailed information (such as sex, age, life-style, and environmental factors) was not considered. Further large scale multicenter studies based on Caucasian or African will be needed to clarify the possible roles of these polymorphisms in CRC. Though many investigators have been devoted to identify the roles of the miRNAs in carcinogenesis, the mechanisms were still unclear. Further biologically functional studies are warranted to explain the molecular mechanisms.In summary, this meta-analysis showed that there were no associations between the four polymorphisms and CRC risk in the overall population. Further analysis stratified by the source of control showed that rs2910164 polymorphism is associated with CRC risk in hospital based studies, and rs2292832 polymorphism may contribute to the susceptibility of CRC in population based studies. In the pooled analysis from all studies which are in agreement with HWE revealed that rs11614913 TT carriers may have a decreased CRC risk. Moreover, in the stratified analysis according to ethnicity, rs3746444 polymorphism may reduce the risk of CRC in Caucasians.



---

*Source: 276410-2015-05-20.xml*