miR-196a2 polymorphisms and susceptibility to cancer: A meta-analysis involving 24,697 subjects

  • Authors:
    • Pingyu Wang
    • Shuyang Xie
    • Aidong Cui
    • Yanxiang Zhang
    • Baofa Jiang
  • View Affiliations

  • Published online on: Thursday, December 1, 2011
  • Pages:324-330 DOI: 10.3892/etm.2011.399
0

Abstract

An increasing number of studies have shown that the hsa-miR-196a2 rs11614913 polymorphism occurs in different types of cancer, but the results are generally controversial and inadequate, mainly due to limited statistical power. To resolve this issue, the present meta-analysis was carried out. Databases, including PubMed and Embase, were searched using: (miR-196a2[All Fields] OR rs11614913[All Fields]) and (‘neoplasms’[MeSH Terms] OR ‘neoplasms’[All Fields] OR ‘cancer’[All Fields]). Crude odds ratios (ORs) with 95% confidence intervals (CIs) were summarized in forest plots and detailed in tables. A total of 20 studies, including 11,004 cases and 13,693 controls, were included in the meta-analysis. The hsa-miR-196a2 rs11614913 polymorphism was significantly associated with an increased cancer risk in all genetic models (CC vs. TT: OR=1.280, 95% CI 1.131-1.449, P<0.001; CT vs. TT: OR=1.187, 95% CI 1.079-1.306, P<0.001; CC/CT vs. TT: OR=1.216, 95% CI 1.104-1.341, P<0.001; and CC vs. CT/TT: OR=1.115, 95% CI 1.025-1.213, P=0.011). In conclusion, this meta-analysis provides compelling evidence that the hsa-miR-196a2 rs11614913 polymorphism plays a crucial role in the development of cancer. Screening of patients for the hsa-miR-196a2 rs11614913 polymorphism can prove clinically useful for the prediction and prevention of cancer.

Introduction

During the past several years, extensive effort has been invested in the field of microRNA (miRNA) polymorphisms and the risk of various types of human cancers. miRNAs, originally discovered in Caenorhabditis elegans in 1993 (1) and first reported in 2001 (2), are small, evolutionarily conserved, single-strand non-coding RNA molecules ∼22 nucleotides in length (3,4). It is predicted that miRNAs account for 1–5% of the human genome and regulate at least 30% of protein-coding genes and play a crucial role in cancer (57). To date, more than one thousand human miRNAs have been identified. Facilitated by continuing technological advances, to date, most registered miRNAs have been widely studied and the results show that both loss and gain of specific miRNA function contribute to cancer development, shedding more light on cancer prevention, diagnosis, progression and outcome.

hsa-miR-196a2, discovered by Lagos-Quintana et al (8), was initially reported to be a prognostic biomarker for non-small cell lung cancer by Hu et al (9). Since then, emerging molecular epidemiological studies have reported the association between the hsa-miR-196a2 polymorphism and susceptibility to diverse types of human cancer (1034). Although the precise processes controlling miRNA genetic variants in cancer susceptibility are largely unknown, the importance of miRNA SNPs has been implicated in many cancers. Common single-nucleotide polymorphisms (SNPs), such as hsa-miR-196a2 rs11614913 which is located in the pre-miRNA, may affect the expression and function of mature miRNAs, resulting in diverse functional consequences, thus opening up a new door through which to explore novel molecular mechanisms of cancer development.

These studies have shown that the hsa-miR-196a2 rs11614913 polymorphism occurs in different types of cancer, but the results are generally controversial and inadequate. In addition, the sample size in each study was relatively small, with statistical power too low to detect the association between the hsa-miR-196a2 polymorphism and cancer risk. To solve the problem of inadequate statistical power and controversial results, it is necessary to carry out a systematic review and meta-analysis to improve our current understanding of the association of the hsa-miR-196a2 rs11614913 polymorphism with human cancer risk.

Subjects and methods

Search strategy

The databases, including PubMed and Embase, were searched using: (miR-196a2[All Fields] OR rs11614913[All Fields]) AND (‘neoplasms’[MeSH Terms] OR ‘neoplasms’[All Fields] OR ‘cancer’[All Fields]). The search was restricted to case-control studies published in English and Chinese updated to July 1, 2011. All of the searched studies were reviewed and a manual search of citations from the original studies was performed to identify additional relevant articles.

Inclusion and exclusion criteria

Study quality was assessed according to the proposed checklist of Little et al (35) for reporting and appraising studies of genotype prevalence and gene-disease associations. A study in which all or most of the criteria specified are satisfied would be graded as high quality. The specified inclusion criteria for this meta-analysis were the following: i) case-control studies: cases were patients newly diagnosed and histopathologically confirmed with different types of cancer, while controls were without cancer; ii) evaluation of the association between the rs11614913 and cancer risks; iii) correct statistical analysis and sufficient published data for estimating odds ratio (OR) with 95% confidence interval (CI); and iv) Hardy-Weinberg equilibrium (HWE). Animal studies, pure cell studies, studies not concerned with cancer risk, repeated or overlapping studies, studies without complete rs11614913 polymorphism distribution data and studies not fit for HWE were excluded.

Data extraction

Two investigators independently extracted data using standardized forms. When an agreement was not reached, a third investigator resolved the conflict.

The following characteristics were extracted from each study if available: i) first name of the author; ii) publication year; iii) country or region of origin; iv) ethnicity (different ethnic descents were categorized as Caucasian and Asian); v) genotyping method; vi) source of control (population- or hospital-based controls); vii) cancer type; viii) numbers of cases and controls with miR-196a2 rs11614913 CC, CT and TT genotypes, respectively; and ix) P-value for HWE.

Statistical analysis

Based on the complete hsa-miR-196a2 rs11614913 polymorphism distribution data in cases and controls, the crude ORs with their 95% CIs were performed and displayed as forest plots to assess the strength of association between the hsa-miR-196a2 rs11614913 polymorphism and susceptibility to cancer. The pooled ORs were calculated for homozygote comparison (CC vs. TT), heterozygote comparison (CC vs. CT), dominant model (CC vs. CT/TT) and recessive model (CC/CT vs. TT), respectively. The significance of the pooled OR was determined by the Z-test, and P<0.05 was considered to denote statistical significance. Subgroup analyses were performed for specific cancer types, genotypes, control sources and ethnicities.

Heterogeneity of the study was explored by using both Cochran Q statistic and estimating I2 test (36). When the presence of heterogeneity was detected (P-value <0.10 for the Q-test, I2 values >50%), the random effects model (DerSimonian Laird) was chosen. Otherwise, the fixed effects model (Mantel-Haenszel method) was appropriately used to calculate the pooled OR. HWE in the control group was assessed by the Chi-square test for goodness of fit using a web-based program (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl); P<0.05 was considered significant.

Sensitivity analysis was carried out to assess the stability of the results. A single study involved in the meta-analysis was deleted each time to reflect the influence of the ORs. Publication bias was assessed using Begg and Egger's formal statistical test (statistical significance was defined as P<0.10) (37,38). Statistical analyses were conducted with Stata 10.0 (Stata Corp., College Station, TX, USA), using two-sided P-values. Meta-analysis was performed using the ‘metan’ and ‘metabias’ STATA command.

Results

Characteristics of the included studies

A total of 55 studies were considered to be relevant by literature search from the PubMed and Embase databases (Fig. 1). Thirty studies were excluded by article review, including 17 studies repeated or overlapped in the PubMed and Embase databases; 6 studies were not concerned with cancer risk research, 1 study was an animal study, 1 study was a cell study, and 5 studies were meta-analysis. During the reading of the 25 full-text manuscripts, 4 studies were excluded due to incomplete rs11614913 polymorphism distribution data required for OR calculation (1013). For the remaining 21 records, baseline characteristics of the patients and control subjects were summarized, the study quality was assessed, HWE in particular was assessed by Chi-square test; two records involving Indian populations published in 2011 were excluded for disagreement with HWE (P<0.05) (14,15), thus leaving 19 articles identified with criteria for inclusion and exclusion (1634). All studies were case-control studies. The cases were patients with cancer, and the controls were without cancer. The reported age and gender distributions were also recorded. By quality assessment, these studies satisfied most of the criteria specified by Little et al (35). In the study of Catucci et al (20), the genotype frequencies were presented separately for a German and an Italian group, thus each group in the study was considered separately for meta-analysis. Therefore, a total of 20 studies including 11,004 cases and 13,693 controls were included in the meta-analysis.

Table I shows the characteristics of the 20 studies, including first name of the author, year of publication, country of origin, ethnicity, genotyping method, source of controls, cancer type, numbers of cases and controls with miR-196a2 rs11614913 CC, CT and TT genotypes, respectively and P-value for HWE. Among 20 studies, 2 studies were published in 2009, 12 studies were published in 2010, and 6 studies were published in 2011. There were 15 studies of Asians, 4 studies of Caucasians, and 1 study of mixed population with no detailed data on ethnicity. Multiple genotyping methods were employed in the studies included in our analysis: 10 studies using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), 3 studies using TaqMan SNP genotyping assay, 3 studies using polymerase chain reaction-ligation detection reaction (PCR-LDR), others using MassARRAY multiplex, and DNA sequencing. A blood sample was used for genotyping in all the studies. There were 4 breast cancer studies, 4 hepatocellular carcinoma (HCC) studies, 3 lung cancer studies, and other cancer types. The controls of 16 studies mainly came from a hospital-based healthy population (HB) matched for gender and age, and 4 studies had population-based controls (PB). The distribution of genotypes in the controls of all of the studies was in agreement with HWE (P>0.05).

Table I.

Characteristics of the 20 studies included in the meta-analysis.

Table I.

Characteristics of the 20 studies included in the meta-analysis.

Cases
Controls
P-valueHWE
Study (ref.)YearCountryEthnicityGenotyping methodCancer typeControlCCCTTTCCCTTT
Hu et al (16)2009ChinaAsianPCR-RFLPBreastPB2394832872185173590.21
Hoffman et al (17)2009USAMixedMassARRAYBreastHB18120936166229710.58
Tian et al (18)2010ChinaAsianPCR-RFLPLungPB2535122932095193070.70
Peng et al (19)2010ChinaAsianPCR-RFLPGastricHB76944356107500.94
Catucci et al (20)2010GermanyCaucasianTaqManBreastHB4325121575846962160.71
Catucci et al (20)2010ItalyCaucasianTaqManBreastHB334330875325501610.32
Dou et al (21)2010ChinaAsianPCR-LDRGliomaHB1113431891433052080.12
Kim et al (22)2010KoreaAsianFluorescenceLungHB1873051621553001850.13
Li et al (23)2010ChinaAsianPCR-RFLPHCCHB781508242102780.40
Liu et al (24)2010USACaucasianPCR-RFLPHNSCCHB3505651943835452020.74
Okubo et al (25)2010JapanAsianPCR-RFLPGastricHB1052811661243502230.51
Qi et al (26)2010ChinaAsianPCR-LDRHCCHB821791001253021610.45
Srivastava et al (27)2010IndiaAsianPCR-RFLPGallbladderPB119951613675190.07
Wang et al (28)2010ChinaAsianSnapshotECHB148262481282501110.60
Chen et al (29)2011ChinaAsianPCR-LDRCRCHB276435942061070.79
Hong et al (30)2011KoreaAsianTaqManLungHB8622496961981340.16
Zhan et al (31)2011ChinaAsianPCR-RFLPCRCHB68128561132671630.85
Zhou et al (32)2011ChinaAsianPCR-RFLPCDCCHB461235758169820.08
Zhang et al (33)2011ChinaAsianPIRA-PCRHCCPB2084492773288174770.52
Akkız et al (34)2011TurkeyCaucasianPCR-RFLPHCCHB7786225887400.49

[i] PB, population-based; HB, hospital-based; HCC, hepatocellular carcinoma; HNSCC, head and neck squamous cell carcinoma; CRC, colorectal cancer; EC, esophageal carcinoma; CSCC, cervical squamous cell carcinoma.

Main meta-analysis results

When all studies were pooled into the meta-analysis, the hsa-miR-196a2 rs11614913 polymorphism was significantly associated with an increased cancer risk in all genetic models (CC vs. TT: OR=1.280; 95% CI 1.131–1.449, P<0.001; CT vs. TT: OR=1.187, 95% CI 1.079–1.306, P<0.001; CC/CT vs. TT: OR=1.216; 95% CI 1.104–1.341, P<0.001; and CC vs. CT/TT: OR=1.115, 95% CI 1.025–1.213, P=0.011) (Figs. 2 and 3).

Next we performed the subgroup analysis of different specific cancer types, genotypes, control sources and ethnicities (Table II). In the different cancer types, individuals carrying the CC genotype had an elevated risk of breast cancer (CC vs. TT: OR=1.305, 95% CI 1.012–1.684, P=0.041; and CC vs. CT/TT: OR=1.114, 95% CI 1.011–1.227, P=0.029), lung cancer (CC vs. TT: OR=1.299, 95% CI 1.096–1.540, P=0.003; and CC vs. CT/TT: OR=1.791, 95% CI 1.022–1.360, P=0.024), digestive system cancer including gastric and CRC (CC vs. TT: OR=1.292, 95% CI 1.041–1.603, P=0.020; and CC vs. CT/TT: OR=1.215, 95% CI 1.015–1.455, P=0.034) and HCC (CC vs. CT/ TT: OR=1.200, 95% CI 1.038–1.387, P=0.014) compared with those with the TT or TC/TT genotypes. In addition, individuals carrying the CT genotype had an elevated risk for breast cancer (CT vs. TT: OR=1.151, 95% CI 1.012–1.310, P=0.032) and other cancers (CT vs. TT: OR=1.352, 95% CI 1.008–1.814, P=0.044) compared with those with the TT genotype. Individuals carrying the CC/ CT genotype had an elevated risk of lung cancer (CC/CT vs. TT: OR=1.206, 95% CI 1.054–1.380, P=0.007) compared with those with the TT genotype.

Table II.

Subgroup meta-analysis results of the hsa-miR-196a2 rs11614913 polymorphism and cancer risk.

Table II.

Subgroup meta-analysis results of the hsa-miR-196a2 rs11614913 polymorphism and cancer risk.

VariablesnCC vs. TT OR (95% CI)P-HP-zCT vs. TT OR (95% CI)P-HP-zCC+CT vs. TT OR (95% CI)P-HP-zCC vs. CT+TT OR (95% CI)P-HP-z
Cancer types
  Breast41.305 (1.012–1.684)0.0280.0411.151 (1.012–1.310)0.1610.0321.223 (0.998–1.499)0.0610.0521.114 (1.011–1.227)0.2080.029
  HCC41.374 (0.985–1.915)0.0400.0611.120 (0.873–1.438)0.0870.3731.215 (0.918–1.609)0.0260.1741.200 (1.038–1.387)0.4070.014
  Gastro41.292 (1.041–1.603)0.1870.0201.118 (0.936–1.337)0.5640.2191.170 (0.988–1.385)0.3460.0681.215 (1.015–1.455)0.2590.034
  Lung31.299 (1.096–1.540)0.8950.0031.204 (0.956–1.516)0.0940.1141.206 (1.054–1.380)0.2810.0071.171 (1.022–1.360)0.2890.024
  Others51.203 (0.798–1.815)0.0000.3771.352 (1.008–1.814)0.0070.0441.289 (0.939–1.769)0.0010.1160.939 (0.756–1.166)0.0240.567
Genotyping
  PCR-RFLP101.318 (1.127–1.541)0.0830.0011.140 (1.038–1.252)0.6740.0061.181 (1.081–1.290)0.3520.0001.170 (1.014–1.350)0.0150.032
  PCR-LDR30.925 (0.740–1.155)0.6850.4911.091 (0.911–1.307)0.3640.3411.041 (0.878–1.235)0.6960.6410.884 (0.730–1.070)0.2140.206
  TaqMan31.104 (0.933–1.306)0.6240.2491.189 (0.922–1.533)0.0880.1821.148 (0.987–1.336)0.1560.0731.022 (0.913–1.144)0.7430.708
  Others41.662 (1.113–2.482)0.0010.0131.437 (0.955–2.161)0.0000.0821.518 (1.012–2.278)0.0000.0441.237 (1.097–1.395)0.6870.001
Controls
  PB41.225 (1.071–1.401)0.5510.0031.051 (0.940–1.175)0.3550.3851.100 (0.990–1.221)0.4160.0761.120 (0.943–1.331)0.0860.197
  HB161.314 (1.117–1.544)0.0000.0011.233 (1.097–1.386)0.0130.0001.361 (1.114–1.428)0.0010.0001.115 (1.010–1.231)0.0170.031
Ethnicity
  Asian151.283 (1.120–1.470)0.0110.0001.187 (1.058–1.331)0.0100.0031.214 (1.088–1.354)0.0080.0011.129 (1.019–1.251)0.0370.021
  Caucasian51.308 (0.972–1.761)0.0030.0761.151 (1.079–1.228)0.1100.0461.253 (0.988–1.590)0.0160.0631.086 (0.934–1.263)0.0480.283
Total201.280 (1.131–1.449)0.0000.0001.187 (1.079–1.306)0.0090.0001.216 (1.104–1.341)0.0020.0001.115 (1.025–1.213)0.0100.011

[i] PB, population-based; HB, hospital-based.

In the different genotypes, the hsa-miR-196a2 rs11614913 polymorphism was associated with a significantly increased cancer risk in all genetic models by PCR-RELP (CC vs. TT: OR=1.318, 95% CI 1.127–1.541, P=0.001; CT vs. TT: OR=1.410, 95% CI 1.038–1.252, P=0.006; CC/CT vs. TT: OR=1.181, 95% CI 1.081–1.290, P<0.001; and CC vs. CT/TT: OR=1.170, 95% CI 1.014–1.350, P=0.032), and others (CC vs. TT: OR=1.662, 95% CI 1.113–2.482, P=0.013; CC/CT vs. TT: OR=1.518, 95% CI 1.012–2.278, P=0.044; and CC vs. CT/TT: OR=1.237, 95% CI 1.097–1.395, P=0.001), but no significant associations were observed by PCR-LDR and TaqMan.

In Asian, but not Caucasian ethnicity, significantly increased risks were observed in all genetic models (CC vs. TT: OR=1.283, 95% CI 1.120–1.470, P<0.001; CT vs. TT: OR=1.187, 95% CI 1,058–1.331, P=0.003; CC/CT vs. TT: OR=1.214, 95% CI 1.088–1.354, P=0.001; and CC vs. CT/TT: OR=1.129, 95% CI 1.019–1.251, P=0.021).

Hospital-based studies demonstrated a significantly increased risk in all genetic models (CC vs. TT: OR=1.314, 95% CI 1.117–1.544, P=0.001; CT vs. TT: OR=1.233; 95% CI 1.097–1.386, P<0.001; CC/CT vs. TT: OR=1.361, 95% CI 1.114–1.428, P<0.001; and CC vs. CT/TT: OR=1.115, 95% CI 1.010–1.231, P=0.031). Population-based studies demonstrated significantly increased risks only for the CC genotype when compared with the TT genotype (OR=1.225, 95% CI 1.071–1.401, P=0.003).

Sensitivity analysis and publication bias

The overall results in the random model or fixed model were similar. In addition, sensitivity analysis was also carried out by deleting a single study in the meta-analysis each time. The results showed that no individual study affected the overall OR dominantly (data not shown). There was no evidence for publication bias according to Begg’s (z=1.40, P=0.163) and Egger’s tests (t=1.32, P=0.202) for CC vs. CT/TT.

Discussion

Principal findings

In the present meta-analysis including 11,004 cases and 13,693 controls, we found that the hsa-miR-196a2 rs11614913 polymorphism was associated with significantly increased overall cancer risk in all genetic models. This meta-analysis provides compelling evidence that the hsa-miR-196a2 rs11614913 polymorphism may play a crucial role in the development of cancer and may be used as a candidate biomarker for cancer susceptibility. Moreover, in our subgroup analysis, we indicated that individuals carrying the CC genotype had a significantly elevated risk of breast cancer, lung cancer, digestive system cancer (including gastric and CRC) and HCC compared with those with TT or TC/TT genotypes, consistent with the total results. Ryan et al (39) suggested that variations in miRNAs may be related to the risk of cancer and reported that the rs11614913 polymorphism located in the hsa-miR-196a2 3′ mature sequence affects the maturation and may affect target mRNA. Cell culture experiments also indicated that high hsa-miR-196a levels could suppress the activities of various cancer-related genes, such as ANXA1 (Annexin A1), suppression of which is well documented in various cancer types (40,41). Li et al (23) conducted an analysis of rs11614913 genotypes and the expression of mature miR-196a. They found that the expression level of hsa-miR-196a was significantly higher in CC patients or patients carrying at least one C allele than in TT patients. The CC homozygotes were associated with a statistically significant increase in mature miR-196a. Therefore, the altered expression patterns of miR-196a influence its potential targets and may play a role in the regulatory processes that occur during cancer development.

Over the past several years, a large number of distinct genotyping approaches have been designed for SNP detection and identification, typically involving the amplification of the target DNA sequence and the detection of SNPs, but each genotyping method has its merit and demerit. A proper technology platform should be adopted according to the sample size and the amount of SNPs (42). The results of PCR-RFLP, PCR-LDR, TaqMan and other methods to detect the hsa-miR-196a2 rs11614913 polymorphism may not be in full accord. We found that the hsa-miR-196a2 rs11614913 polymorphism significantly increased cancer risk in all genetic models using the PCR-RFLP method, but not using the PCR-LDR method. Therefore, a great deal of effort should be devoted to developing more accurate, rapid, and cost-effective technologies for SNP analysis.

In the subgroup analysis of source of controls and ethnicities, hospital-based and population-based studies demonstrated significantly increased risks for the CC genotype compared with the TT genotype. A population-based control can better represent the population, but hospital-based controls are more readily obtainable in research. In addition, significantly increased risks were observed in an Asian but not in a Caucasian ethnic population, suggesting potentially different mechanisms in different populations according to different genetic background and environment. More studies in other ethnic groups may be necessary for further progress in this area.

Strengths and limitations of the meta-analysis

Several limitations of this meta-analysis should be mentioned. First, the meta-analysis was limited by a relatively small number of available studies. It is difficult to perform subgroup analysis for every type of cancer. Second, our analysis was limited to Asian and Caucasian ethnicities, so it is uncertain whether these results are generalizable to other populations. Third, restriction to studies published in English or Chinese may confer potential language bias. In addition, studies with no statistically significant results often have less chance for publication. It is still difficult to rule out potential publication bias in the meta-analysis. To confirm the role of the hsa-miR-196a2 rs11614913 polymorphism in cancer risk requires further larger studies in different populations and in different types of cancer.

In spite of these limitations, our meta-analysis had several strengths. In genetic association studies, the sample size and statistical power are often of particular importance. We overcame the limitations of a single study involving a relative small number of subjects and the limitation in the statistical significance, as relatively more sufficient number of cases and controls were pooled from different studies, which significantly increased the statistical power of the analysis. In addition, this meta-analysis not only assessed the total strength of association between the hsa-miR-196a2 rs11614913 polymorphism and overall cancer risk in all genetic models, but also further performed subgroup analysis of different specific cancer types, genotypes and control sources to assess the polymorphism with cancer risk.

In conclusion, this meta-analysis provides compelling evidence that the hsa-miR-196a2 rs11614913 polymorphism may play a crucial role in the development of cancer, and that screening of patients for the hsa-miR-196a2 rs11614913 polymorphism is clinically useful for the prediction and prevention of cancer.

Abbreviations:

miRNAs

microRNAs

SNPs

single nucleotide polymorphisms

OR

odds ratio

CI

confidence interval

HWE

Hardy-Weinberg equilibrium

PCR-RFLP

polymerase chain reaction-restriction fragment length polymorphism

PCR-LDR

polymerase chain reaction-ligation detection reaction

HCC

hepatocellular carcinoma

HB

hospital-based healthy controls

PB

population-based controls

ANXA1

Annexin A1

Acknowledgements

This study was supported by grants from the NCET-10-0919, National Natural Science Foundation (no. 30801324), the Foundation of Shandong Natural Science (nos. ZR2009CL005 and ZR2009CQ033), and the Scientific Research Foundation of Shandong Educational Committee (nos. J09LF11 and J08LG15).

References

1. 

RC LeeRL FeinbaumV AmbrosThe C. elegans heterochronic gene lin encodes small RNAs with antisense complementarity to linCell758438541993

2. 

NC LauLP LimEG WeinsteinDP BartelAn abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegansScience294858862200110.1126/science.106506211679671

3. 

LA MacfarlanePR MurphyMicroRNA: biogenesis, function and role in cancerCurr Genomics11537561201010.2174/13892021079317589521532838

4. 

DP BartelMicroRNAs: genomics, biogenesis, mechanism, and functionCell116281297200410.1016/S0092-8674(04)00045-514744438

5. 

W LiuSY MaoWY ZhuImpact of tiny miRNAs on cancersWorld J Gastroenterol13497502200710.3748/wjg.v13.i4.49717278213

6. 

CD DavisSA RossEvidence for dietary regulation of microRNA expression in cancer cellsNutr Rev66477482200810.1111/j.1753-4887.2008.00080.x18667010

7. 

LB GaoP BaiXM PanJ JiaLJ LiWB LiangM TangLS ZhangYG WeiL ZhangThe association between two polymorphisms in pre-miRNAs and breast cancer risk: a meta-analysisBreast Cancer Res Treat125571574201110.1007/s10549-010-0993-x20640596

8. 

M Lagos-QuintanaR RauhutJ MeyerA BorkhardtT TuschlNew microRNAs from mouse and humanRNA9175179200310.1261/rna.2146903

9. 

Z HuJ ChenT TianX ZhouH GuL XuY ZengR MiaoG JinH MaY ChenH ShenGenetic variants of miRNA sequences and non-small cell lung cancer survivalJ Clin Invest11826002608200818521189

10. 

H YangCP DinneyY YeY ZhuHB GrossmanX WuEvaluation of genetic variants in microRNA-related genes and risk of bladder cancerCancer Res6825302537200810.1158/0008-5472.CAN-07-599118381463

11. 

BC ChristensenM Avissar-WhitingLG OuelletRA ButlerHH NelsonMD McCleanCJ MarsitKT KelseyMature microRNA sequence polymorphism in MIR196A2 is associated with risk and prognosis of head and neck cancerClin Cancer Res1637133720201010.1158/1078-0432.CCR-10-065720501619

12. 

Y YeKK WangJ GuH YangJ LinJA AjaniX WuGenetic variations in microRNA-related genes are novel susceptibility loci for esophageal cancer riskCancer Prev Res (Phila)1460469200810.1158/1940-6207.CAPR-08-013519138993

13. 

Y HorikawaCG WoodH YangH ZhaoY YeJ GuJ LinT HabuchiX WuSingle nucleotide polymorphisms of microRNA machinery genes modify the risk of renal cell carcinomaClin Cancer Res1479567962200810.1158/1078-0432.CCR-08-119919047128

14. 

GP GeorgeR GangwarRK MandalSN SankhwarRD MittalGenetic variation in microRNA genes and prostate cancer risk in North Indian populationMol Biol Rep3816091615201110.1007/s11033-010-0270-420842445

15. 

RD MittalR GangwarGP GeorgeT MittalR KapoorInvestigative role of pre-microRNAs in bladder cancer patients: a case-control study in North IndiaDNA Cell Biol30401406201110.1089/dna.2010.115921345130

16. 

Z HuJ LiangZ WangT TianX ZhouJ ChenR MiaoY WangX WangH ShenCommon genetic variants in pre-microRNAs were associated with increased risk of breast cancer in Chinese womenHum Mutat307984200910.1002/humu.2083718634034

17. 

AE HoffmanT ZhengC YiD LeadererJ WeidhaasF SlackY ZhangT ParanjapeY ZhuMicroRNA miR-196a-2 and breast cancer: a genetic and epigenetic association study and functional analysisCancer Res6959705977200910.1158/0008-5472.CAN-09-023619567675

18. 

T TianY ShuJ ChenZ HuL XuG JinJ LiangP LiuX ZhouR MiaoH MaY ChenH ShenA functional genetic variant in microRNA-196a2 is associated with increased susceptibility of lung cancer in ChineseCancer Epidemiol Biomarkers Prev1811831187200910.1158/1055-9965.EPI-08-081419293314

19. 

S PengZ KuangC ShengY ZhangH XuQ ChengAssociation of microRNA-196a-2 gene polymorphism with gastric cancer risk in a Chinese populationDig Dis Sci5522882293201010.1007/s10620-009-1007-x19834808

20. 

I CatucciR YangP VerderioS PizzamiglioL HeesenK HemminkiC SutterB WappenschmidtM DickN ArnoldP BugertD NiederacherA MeindlRK SchmutzlerCC BartramF FicarazziL TizzoniD ZaffaroniS ManoukianM BarileMA PierottiP RadiceB BurwinkelP PeterlongoEvaluation of SNPs in miR-146a, miR196a2 and miR-499 as low-penetrance alleles in German and Italian familial breast cancer casesHum Mutat31E1052E1057201010.1002/humu.2114119847796

21. 

T DouQ WuX ChenJ RibasX NiC TangF HuangL ZhouD LuA polymorphism of microRNA196a genome region was associated with decreased risk of glioma in Chinese populationJ Cancer Res Clin Oncol13618531859201010.1007/s00432-010-0844-520229273

22. 

MJ KimSS YooYY ChoiJY ParkA functional polymorphism in the pre-microRNA-196a2 and the risk of lung cancer in a Korean populationLung Cancer69127129201010.1016/j.lungcan.2010.04.01520466450

23. 

XD LiZG LiXX SongCF LiuA variant in microRNA-196a2 is associated with susceptibility to hepatocellular carcinoma in Chinese patients with cirrhosisPathology42669673201010.3109/00313025.2010.52217521080878

24. 

Z LiuG LiS WeiJ NiuAK El-NaggarEM SturgisQ WeiGenetic variants in selected pre-microRNA genes and the risk of squamous cell carcinoma of the head and neckCancer11647534760201010.1002/cncr.2532320549817

25. 

M OkuboT TaharaT ShibataH YamashitaM NakamuraD YoshiokaJ YonemuraT IshizukaT ArisawaI HirataAssociation between common genetic variants in pre-microRNAs and gastric cancer risk in Japanese PopulationHelicobacter15524531201010.1111/j.1523-5378.2010.00806.x21073609

26. 

P QiTH DouL GengFG ZhouX GuH WangCF GaoAssociation of a variant in MIR 196A2 with susceptibility to hepatocellular carcinoma in male Chinese patients with chronic hepatitis B virus infectionHum Immunol71621626201010.1016/j.humimm.2010.02.01720188135

27. 

K SrivastavaA SrivastavaB MittalCommon genetic variants in premicroRNAs and risk of gallbladder cancer in North Indian populationJ Hum Genet55495499201010.1038/jhg.2010.5420520619

28. 

K WangH GuoH HuG XiongX GuanJ LiX XuK YangY BaiA functional variation in pre-microRNA-196a is associated with susceptibility of esophageal squamous cell carcinoma risk in Chinese HanBiomarkers15614618201010.3109/1354750X.2010.50529920722507

29. 

H ChenLY SunLL ChenHQ ZhengQF ZhangA variant in microRNA-196a2 is not associated with susceptibility to and progression of colorectal cancer in ChineseIntern Med JJan172011(Epub ahead of print)10.1111/j.1445-59942011.02434.x

30. 

YS HongHJ KangJY KwakBL ParkCH YouYM KimH KimAssociation between microRNA196a2 rs11614913 genotypes and the risk of non-small cell lung cancer in Korean populationJ Prev Med Public Health44125230201110.3961/jpmph.2011.44.3.12521617338

31. 

JF ZhanLH ChenZX ChenYW YuanGZ XieAM SunY LiuA functional variant in microRNA-196a2 is associated with susceptibility of colorectal cancer in a Chinese populationArch Med Res42144148201110.1016/j.arcmed.2011.04.00121565628

32. 

B ZhouK WangY WangM XiZ ZhangY SongL ZhangCommon genetic polymorphisms in pre-microRNAs and risk of cervical squamous cell carcinomaMol Carcinog50499505201110.1002/mc.2074021319225

33. 

XW ZhangSD PanYL FengJB LiuJ DongYX ZhangJG ChenZB HuHB ShenRelationship between genetic polymorphism in microRNAs precursor and genetic prediposition of hepatocellular carcinomaZhonghua Yu Fang Yi Xue Za Zhi452392432011(In Chinese).

34. 

H AkkızS BayramA BekarE AkgöllüY UlgerA functional polymorphism in pre-microRNA-196a-2 contributes to the susceptibility of hepatocellular carcinoma in a Turkish population: a case-control studyJ Viral Hepat18e399e407201121692953

35. 

J LittleL BradleyMS BrayM ClyneJ DormanDL EllsworthJ HansonM KhouryJ LauTR O’BrienN RothmanD StroupE TaioliD ThomasH VainioS WacholderC WeinbergReporting, appraising, and integrating data on genotype prevalence and gene-disease associationsAm J Epidemiol156300310200210.1093/oxfordjournals.aje.a00017912181099

36. 

JP HigginsSG ThompsonJJ DeeksDG AltmanMeasuring inconsistency in meta-analysesBMJ327557560200310.1136/bmj.327.7414.55712958120

37. 

CB BeggM MazumdarOperating characteristics of a rank correlation test for publication biasBiometrics5010881101199410.2307/25334467786990

38. 

M EggerSG DaveyM SchneiderC MinderBias in meta-analysis detected by a simple, graphical testBMJ315629634199710.1136/bmj.315.7109.6299310563

39. 

BM RyanAI RoblesCC HarrisGenetic variation in microRNA networks: the implications for cancer researchNat Rev Cancer10389402201010.1038/nrc286720495573

40. 

R LuthraRR SinghMG LuthraYX LiC HannahAM RomansBA BarkohSS ChenJ EnsorDM MaruRR BroaddusA RashidCT AlbarracinMicroRNA-196a targets annexin A1: a microRNAmediated mechanism of annexin A1 downregulation in cancersOncogene2766676678200810.1038/onc.2008.25618663355

41. 

D ShenF NooraieY ElshimaliV LonsberryJ HeS BoseD ChiaD SeligsonHR ChangL GoodglickDecreased expression of annexin A1 is correlated with breast cancer development and progression as determined by a tissue microarray analysisHum Pathol3715831591200610.1016/j.humpath.2006.06.00116949910

42. 

S KimA MisraSNP genotyping: technologies and biomedical applicationsAnnu Rev Biomed Eng9289320200710.1146/annurev.bioeng.9.060906.152037

Related Articles

Journal Cover

February 2012
Volume 3 Issue 2

Print ISSN: 1792-0981
Online ISSN:1792-1015

2015 Impact Factor: 1.28
Ranked #64/123 Medicine Research and Experimental
(total number of cites)

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
APA
Wang, P., Xie, S., Cui, A., Zhang, Y., & Jiang, B. (2012). miR-196a2 polymorphisms and susceptibility to cancer: A meta-analysis involving 24,697 subjects. Experimental and Therapeutic Medicine, 3, 324-330. http://dx.doi.org/10.3892/etm.2011.399
MLA
Wang, P., Xie, S., Cui, A., Zhang, Y., Jiang, B."miR-196a2 polymorphisms and susceptibility to cancer: A meta-analysis involving 24,697 subjects". Experimental and Therapeutic Medicine 3.2 (2012): 324-330.
Chicago
Wang, P., Xie, S., Cui, A., Zhang, Y., Jiang, B."miR-196a2 polymorphisms and susceptibility to cancer: A meta-analysis involving 24,697 subjects". Experimental and Therapeutic Medicine 3, no. 2 (2012): 324-330. http://dx.doi.org/10.3892/etm.2011.399