Download Association between MiRMAs gene polymorphisms and the risk of

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MiRMAs gene polymorphisms and the risk of colorectal
cancer
Abstract
Objective: To investigate the association between the risk of CRC and the polymorphism
of all miRNAs that can be retrieved.
Methods: Multiple meta-analyses of reported data were conducted during January 2013
and January 2015 in the Second Affiliated Hospital of Soochow University, OR values and
95% confidence interval (95% CI) were used to assess this association. All data analyzes
were performed using the software Stata 11.0.
Results: we obtained 38 research literatures about the association between miRNAs
polymorphism and colorectal cancer risk and 15 met the requirements in accordance with
the opt-in and exclusion criteria were selected.
Conclusion: We found that let-7, miR-34b/c, miR-146a and miR-149 are positively
correlated to the risk of CRC except miR-192a and miR-27a. However, some miRNAs of
them lacked of more relevant literature, which made the results less universal. So,
analysis of a larger sample size needs to be made.
Keywords：miRNA, Polymorphism, Colorectal cancer, Risk, Meta-analysis
Introduction
Colorectal cancer （CRC） is one of common malignant tumors in the world. According to
statistics, in United States, both incidence and mortality of CRC are in the third place even
though its death rates have declined .The vast majority of cases (90%) occur in people
over 501. Apart from hereditary CRC syndromes, the development of this cancer type is
still poorlyunderstood
1, 2.
Both germline and somatic genetic variations have been
proposed as contributing factors in CRC development 2-4.
microRNAs (miRNAs) are a group of small non‑coding RNAs that are ~22 (18 to 25)
nucleotides (nt) and which regulate RNA expression in the translation level5-7
MiRNAs have been associated with a variety of diseases, including cancers. More and
more evidences have confirmed the importance of miRNAs on regulating the biological
characteristics of the tumor, and have common in different tumors such as: Self-growth
signals, insensitive to anti-growth signals, involved in abnormal apoptosis, unlimited
replication potential, sustained induction of angiogenesis and participating organizations
invasion and metastasis 8. Many researchers have identified tumor-specific miRNA
signatures which accurately distinguish malignant tumors from several different parts of
the benign tissues, and they show that some miRNA carcinogenic depending on the other
genes mutation in tumors9 9. MiRNA regulating tumor cell lines directly affects cell
proliferation and apoptosis, similar to this, many studies have confirmed the link between
the miRNA abnormal expression and intracellular signal transduction pathway
abnormalities as well as tumorigenesis
10-14.
For example: miR-9 is activated by
YC/MYCN which induces cancer metastasis by regulating metastasis suppressor protein
E-cadherin
15;
miR-449a can cause RB-dependent cell cycle arrest and cellular
senescence of prostate cancer 16.
MiRNAs constitute a new class of molecules through the interaction with oncogenes
and/or tumor suppressor genes; promote the formation of cancer
17.
But different miRNA
have different signaling pathways, different target proteins/genes to affect the biological
changes of cancer.
Studies show that human miRNAs single nucleotide polymorphisms (SNPs) can be one of
the main forms of genetic variation in human genomic DNA sequences and it may be core
message to the susceptibility of human disease. MiRNAs SNP has inter-individual
differences in disease diagnosis, treatment and prognosis in the present the research
fields. Recent large scale studies reported significant risk of different germline variations
for CRC development 3. Novel reports suggest a potential influence of single nucleotide
polymorphisms (SNPs) of microRNA-related genes (miRNAs) for the risk of cancer
development.
Therefore, we want to investigate the correlation between miRNAs gene polymorphism
and CRC by reviewing the literatures.
Materials and methods
Screening and identification of relevant studies
Identification and eligibility of relevant studies search terms “miRNA/microRNA”,
“colorectal cancer”, “genotype”, “polymorphism” and “variant” were employed to explore
publications in PubMed, Ovid, Embase databases and the Cochrane Library for relevant
reports during January 2013 and January 2015 in the Second Affiliated Hospital of
Soochow University. The search was limited to English language papers and only
published studies with full text articles were included. We evaluated potentially relevant
publications by examining their titles and abstract by hand. All of the selected studies in
our meta-analysis should also match the following inclusion criteria: (1) assessment of
miRNA polymorphism and the risk of suffering from CRC, (2) a separate case-control
study about human , (3) statistics sufficient genotype data by OR values and 95%
confidence intervals. (4) Full-Text Search. Exclusion criteria: (1) lack of controlled studies,
(2) repeat the previous literature (3) summary, comments, reviews and editorials (4) focus
on benign tumor of CRC.
Data extraction and study characteristics
Two researchers extracted all data that meet the above inclusion criteria independently
and the existing differences were resolved by our team discussion. For each study, the
following information were extracted: first name of first author, year of publication,
ethnicity, miRNA type, SNP ID, source of research, Genotyping method, the number of
cases and controls, the number of various genotypes of cases and controls,
Hardy-Weinberg equilibrium (HWE) of control subjects. If one literature has not provided a
complete data, we sent request messages to the corresponding author for the data. A
total of 15 eligible studies met the inclusion criteria (Table 1).
Quality assessment
Two researchers evaluated uniform quality of literature which met the inclusion criteria,
and cross-checked in case disagreement is resolved by discussion. The quality of the
included studies using a modified Jadad score18, 1-3 points considered low quality studies,
4-7 points considered high-quality research. Evaluation include: whether to generate a
random sequence of proper randomized allocation is hidden; whether blinded; observe
whether lost, quit the case (Table 1)
Statistical analysis
According to the cases and controls genotype frequencies, the correlation between
miRNA polymorphism and CRC risk was assessed via OR values with 95% confidence
intervals.Statistical analysis of OR values and 95% confidence interval for five different
genetic makeup: the allele, the dominant genetic model, the recessive genetic model, the
homozygote comparison and the heterozygous comparison. The Chi-square based Q
statistic was used to assess heterogeneity between studies, P <0.05 were considered
significant heterogeneity between studies. I2 index is expressed as a percentage for the
total variability throughout the study. I2 value as 25%, 50% and 75% respectively meant
low, medium and high heterogeneity. Funnel plots was used to assess publication
bias. When the effects were assumed to be homogenous, the fixed-effects model was
used (Mantel–Haenszel method). If heterogeneity was present, the random-effects model
was applied (DerSimonian-Laird method) to account for inter-study heterogeneity instead
of the fixed-effect model. All data analyzes were performed using the software Stata 11.0,
all the P values are two-sided test19, 20.
Results
Study characteristics
A total of 681 papers relevant to the search words were identified and only 38 studies
were about the association between CRC and miRNA polymorphism. According to the
above inclusion and exclusion criteria, 15 publications (4 using population-based controls
and 11 using hospital-based controls) were included in the final meta-analysis
4-7, 19-35
(Fig.1). In the 15 articles miR-146a has seven; miRNA-149 has three; miRNA-196a2 has
eight; miRNA-27a and miRNA-34b/c has two each; miRNA-let-7, miRNA-603 and
miRNA-608 have one each. The main characteristics of the studies included in the
meta-analysis are summarized in Table 1.
Overall analyses
Table 2 showed that the overall analysis of all studies revealed a statistically significant
positive association between let-7, miR-34b/c, miR-146a, miR-603 and miR-149
polymorphism and risk of CRC. However, miR-192a, miR-608 and miR-27a have no
correlation with it. Results are as follows: let-7:Allele T vs. G: OR=1.49, 95%
CI:(1.15-1.94); Dominant genetic model TT + TG vs. GG:OR=1.48, 95% CI:(1.08-2.03);
Recessive genetic model TT vs. TG + GG:OR=2.52, 95% CI:(1.19-5.31); Homozygous TT
vs.GG: OR=2.81, 95% CI:(1.32-5.98); miR-34b/c: Allele T vs. C: OR=1.15, 95%
CI:(1.00-1.33), P heterogeneity= 0.154; Dominant genetic model TT + TC vs. CC:OR=1.49, 95%
CI:(1.08-2.06), P heterogeneity=0.519; Homozygous TT vs.CC: OR=1.52, 95% CI:(1.08-2.13),
P
heterogeneity=0.342;
Heterozygous TC vs. CC: OR=1.46, 95% CI:(1.03-2.05), P
heterogeneity=0.812;
miR-146a: Allele G vs. C: OR=1.09, 95% CI :( 1.01-1.18), P
heterogeneity=0.227;
Recessive genetic model GG vs. GC + CC:OR=1.13, 95%
CI:(1.00-1.27), P heterogeneity=0.207; Homozygous GG vs.CC: OR=1.19, 95% CI:(1.01-1.40),
P
heterogeneity=0.237;
MiR-603: Allele C vs. T: OR=3.65, 95% CI :( 1.84-7.24); MiR-149:
Recessive genetic model CC vs. CG + GG: OR=1.24, 95% CI :( 1.03-1.5), P
heterogeneity=0.582
These results indicate that: let-7, miR-34b/c, miR-146a, miR-603 and miR-149
polymorphism were positively correlated to colorectal carcinoma.
Statistical sensitivity
Statistical sensitivity in which one study was removed and the rest were analyzed, the
pooled RRs were similar with the overall pooled RRs (data not shown), supporting the
robustness of our results 1
Publication bias
Egger’s test was used to assess the publication bias of literatures (Table 2). However, the
results show some evidence of publication bias in some comparisons.
Discussion
At present, many articles reported miRNAs gene polymorphisms affect tumor
susceptibility to, such as: De et al found that a let-7 microRNA polymorphism in the KRAS
3'-UTR is prognostic in oropharyngeal cancer
4, 7.
Study has shown that miR-146aG>C
and miR-196a2C>T polymorphism are associated with risk of HCC patients in China,
especially in patients with HBV infection37. Palmieri et al found that miR-146a
polymorphism is not associated with tumor development. However, a slight increase in the
frequency of the variant allele was observed in Stage II tumors4.
There is also a lot of meta-analysis which is a statistical correlation analysis of miRNA and
cancer risk. Some meta-analysis analyzed the correlation between one miRNA and
certain cancer susceptibility,such as WA NG et al confirmed the association of the
polymorphism miR-196a2 rs11614913 with the risk of CRC, but not with tumor stage and
grade5. Some meta-analysis analyzed the correlation between one miRNA and a variety
of cancers susceptibility such as Tao et al suggested that hsa-miR-34b/c rs4938723
polymorphism may play an opposite role in different types of cancer based on current
studies, subgroup analysis revealed that the variant CT and genotypes were associated
with an increased risk of hepatocellular carcinoma (HCC) compared with wild-type TT
genotype. However, a decreased risk of colorectal cancer (CRC) was found in the genetic
model of CC/TT and CC/CT+TT
6.
Li et al insisted that miR-146a rs2910164
polymorphism may decrease the susceptibility of digestive system cancers, especially in
Asian population
7, 36.
Some meta-analysis analyzed the correlation between a variety of
miRNAs and a variety of cancers susceptibility such as one meta-analysis provided
evidence that miR-196a2 rs11614913 polymorphism is associated with an increased
cancer risk and rs2910164 in miR-146a might be associated with susceptibility to papillary
thyroid carcinoma and cervical cancer33. In addition, some meta-analysis analyzed the
correlation between a variety of miRNAs and one certain cancer susceptibility such as
Dikeakos et al did an investigation on the association of the miR-146aC>G, miR-149T>C,
and miR-196a2T>C polymorphisms with gastric cancer risk and survival in the Greek
population. And they found that that the risk for GC was significantly higher for the carriers of
miR-149 rs2292832CC and miR-196a2 rs11614913CC genotypes, as well as for the carriers
of
the
rs2910164/rs2292832/rs11614913
CCC
and
GTC
haplotype.
The
rs2910164/rs2292832/rs11614913 CTT and CCT haplotypes seems to have a protective role
against GC. Their data demonstrate that specific miRNA SNPs are associated with GC
susceptibility in the Greek population25.
In this study, we made an overview statistical analysis of the relationship between miRNA
polymorphism and CRC.We investigated the effects of miRNA-196a2, miRNA-146a,
miRNA-27a, miRNA-34b / c, miRNA-let-7, miRNA-603, miRNA-608 and miRNA-149 on
CRC susceptibility respectively. This is a more comprehensive statistical analysis of
various miRNAs affect the risk of CRC. However, although the above miRNA and
colorectal cancer risk is not highly relevant or not, but they are combined with other factors
such as Helicobacter pylori
33,
smoking
4, 7,
age, and drugs can lead to increased cancer
risk. Therefore, stratified analysis is necessary. In addition, this study is the lack of some
samples collected mostly from Asian populations, so the results do not lead to global.
Therefore, we need a larger sample size of the statistical analysis, the relationship
between the collection of more miRNA gene polymorphism and colorectal cancer among
non-Asian populations, have been more reliable conclusions.
Conclusion
We analyze the relationship between the recently published miRNA gene polymorphism
and colorectal cancer prevalence among statistical, informed of let-7, miR-34b / c,
miR-146a, miR-603 and miR-149 gene polymorphism can significantly increase the risk of
colorectal cancer, while miR-192a and miR-27a polymorphism nothing to do with the risk
of colorectal cancer. Analysis of a larger sample size needs to be.
Acknowledgements
This work was partially supported by National Natural Science Foundation of China (Grant
No: 81172348, 81301933 and 81172597), Health Research Projects in Jiangsu province
(H201313).
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Figure 1. Flowchart of study selection
Table 1. Characteristics of the 15 studies included in the meta-analysis
First
miRNA
SNP ID
author
miRNA-146a
rs2910164
Min
Year Ethnicity
2012
Asian
Hezova 2012 Caucasian
rs2292832
Cases
Controls
design
method
Allele
number
number
PB
PCR-RFLP
C/G
446
HB
TaqMan
C/G
HWE（P）
Jadad
502
0.44
7
197
212
0.41
7
2013
Asian
HB
Taqman
C/G
1147
1203
0.075
7
Lv
2013
Asian
HB
PCR–RFLP
C/G
353
540
0.08
7
2013 Caucasian
PB
HRM
C/G
160
178
0.11
6
Hu
2013
Asian
HB
PCR–RFLP
C/G
276
373
0.14
7
Parlayan
2014
Asian
HB
Taqman
C/G
116
524
0.75
7
Min
2012
Asian
PB
PCR-RFLP
C/T
446
502
0.17
7
Lv
2013
Asian
HB
PCR–RFLP
C/T
353
540
＜0.05
7
2013 Caucasian
PB
HRM
C/T
160
178
0.91
6
Zhu
2011
Asian
HB
TaqMan
T/C
573
588
0.17
7
Zhan
2011
Asian
HB
PCReRFLP
T/C
252
543
0.77
7
Chen
2012
Asian
HB
PCR–LDR
T/C
126
407
0.82
7
HB
TaqMan
T/C
197
212
0.81
7
Vinci
miRNA-196a2 rs11614913
Genotyping
Ma
Vinci
miRNA-149
Study
Hezova 2012 Caucasian
Min
2012
Asian
PB
PCR-RFLP
T/C
446
502
0.63
7
Lv
2013
Asian
PB
PCR–RFLP
T/C
353
540
＜0.05
7
2013 Caucasian
PB
HRM
T/C
160
178
0.09
6
2014
HB
TaqMan
T/C
116
524
0.78
7
Vinci
Parlayan
Asian
miRNA-27a
miRNA-34b/c
miRNA-let-7
rs895819
Hezova 2012 Caucasian
HB
TaqMan
G/A
197
212
0.78
7
Wang
2014
Asian
HB
TaqMan
G/A
205
455
＜0.05
7
Gao
2013
Asian
HB
PCR-RFLP
T/C
347
488
0.83
7
Oh
2014
Asian
PB
PCR-RFLP
T/C
428
545
0.4
7
Pan
2014
Asian
HB
PCR-RFLP
G/T
339
313
0.41
7
rs4938723
rs712
Sequenom
miRNA-603
rs11014002
Wang
2014
Asian
miRNA-608
rs4919510
Ryan
2014 Caucasian
HB
Mass
C/T
102
204
0.59
7
PB
Taqman
C/G
245
446
0.94
7
PB: population-based case–control studies; HB: hospital-based case–control studies; PCR–RFLP: polymerase chain reaction with restriction fragment
length polymorphism; SNP: single nucleotide polymorphisms; HWE: Hardy–Weinberg equilibrium; Jadad: Modified Jadad score
Table 2. Analysis of the association between eight kinds of miRNAs gene polymorphism and colorectal cancer risk
miRNA
a
2
b
OR (95% CI)
I (%)
P
G/C
GC + GG/CC
1.09(1.01,1.18)
1.13(0.99,1.27)
26.4
75.1
0.227
0.001
0.036
0.527
GG/CC + GC
GG/CC + GC
GC/CC
1.13(1,1.28)
1.19(1.01,1.41)
1.09(0.94,1.24)
28.9
25.2
79.8
0.207
0.237
＜0.001
0.155
0.141
0.646
1.13(0.96,1.3)
1.02(0.79,1.31)
0
0
0.533
0.655
0.805
0.06
Contrast
miRNA-146a
N
Pc
7
miRNA-149
3
T/C
TT+CT/CC
TT/TC+CC
TT/CC
1.24(1.03,1.5)
1.19(0.89,1.6)
0
0
0.582
0.591
0.332
0.4
TC/CC
0.88(0.67,1.16)
0
0.89
0.191
T/C
TT+TC/CC
1.01(0.94,1.1)
1.03(0.9,1.17)
89.2
87.6
＜0.001
TT/TC+CC
TT/CC
TC/CC
1.01(0.89,1.15)
1.03(0.87,1.21)
1.05(0.91,1.2)
83
90.2
84.7
＜0.001
＜0.001
＜0.001
＜0.001
0.595
0.011
G/A
（GG+GA）/AA
1.19(0.99,1.43)
1.14(0.9,1.55)
66.4
48.1
0.085
0.165
-
GG/(GA+AA)
GG/AA
GA/AA
1.3(0.97,1.74)
1.38(0.97,1.95)
1.09(0.81,1.47)
3.3
39
0
0.309
0.201
0.408
-
1.15(1,1.33)
1.49(1.08,2.06)
1.11(0.92,1.33)
1.52(1.08,2.13)
1.46(1.03,2.05)
50.9
0
50
0
0
0.154
0.519
0.157
0.342
0.812
-
1.49(1.15,1.94)
1.48(1.08,2.03)
2.52(1.19,5.31)
-
-
-
miRNA-196a2
8
miRNA-27a
0.636
0.061
0.023
2
miRNA-34b/c
2
T/C
TT+TC/CC
TT/（TC+CC）
TT/CC
TC/CC
miRNA-let-7
1
T/G
TT+TG/GG
TT/TG+GG
TT/GG
TG/GG
2.81(1.32,5.98)
1.35(0.97,1.88)
-
-
-
C/T
CC+CT/TT
CC/CT+TT
0.97(0.76,1.25)
1.05(0.59,1.87)
0.94(0.69,1.29)
-
-
-
CC/TT
CT/TT
1.02(0.56,1.85)
1.10(1.60,2.02)
-
-
-
C/G
0.97(0.76,1.25)
-
-
-
CG+CC/GG
CC/CG+GG
CC/GG
CG/GG
1.05(0.59,1.87)
0.94(0.69,1.29)
1.02(0.56,1.85)
1.10(0.59,2.02)
-
-
-
miRNA-603
1
miRNA-608
a Number of comparison
b P value of test for overall effect
c Egger’s test