The association between hsa-miR-499 T > C polymorphism and cancer risk: A meta-analysis

Gene 508 (2012) 9–14

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The association between hsa-miR-499 T > C polymorphism and cancer risk: A meta-analysis☆ Lina Wang a, b,⁎, Shasha Qian c, Hong Zhi d, Yu Zhang a, Bei Wang a, Zuhong Lu b a

Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China School of Biological Science and Medical Engineering, Southeast University, Nanjing, China National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China d Department of Cardiology, ZhongDa Hospital, Southeast University, Nanjing, China b c

a r t i c l e

i n f o

Article history: Accepted 2 August 2012 Available online 10 August 2012 Keywords: hsa-miR-499 rs3746444 Gene polymorphism Cancer risk Meta-analysis

a b s t r a c t MicroRNAs regulate gene expression at the post-transcriptional level and were involved in diverse biological and pathological processes. Single nucleotide polymorphism (SNP) which is located in the pre-miRNA may affect the processing and then influence the expression of mature miRNA. Previous studies yielded conflicting results as to the association of a common polymorphism in pre-miRNAs (i.e. hsa-miR-499 rs3746444) with various diseases. Therefore, here we performed a meta-analysis to address the association between this polymorphism and cancer risks. A total of twenty studies involving 10,584 cases and 12,414 controls were retrieved based on PubMed. No significant association was found either in cancers and other diseases in all genetic models. And then in the stratified analysis by ethnicity, significantly increased risks were found in Asians (OR=1.11; 95% CI =1.00–1.23 for C vs. T; OR=1.16; 95% CI =1.00–1.36 for TC vs. TT; OR=1.15; 95%CI=1.01–1.31 for TC/CC vs. TT), but not in Caucasians in all comparison models tested. Our meta-analysis suggested that polymorphism of hsa-miR-499 rs3746444 T >C was not associated with the increased susceptibility to cancers and other diseases. © 2012 Elsevier B.V. All rights reserved.

1. Introduction MicroRNAs (miRNAs) are small, single-stranded, nonprotein-coding RNAs of about 22 nucleotides. To date, hundreds of miRNA molecules have been identified in the human genome that plays key roles in a broad range of physiologic and pathologic processes (Ambros, 2004; Bartel, 2004). These miRNAs regulate the expression of roughly 10–30% of all human genes through post-transcriptional mechanisms pairing to complementary sequences in the 3′ untranslated region (3′UTR) of target mRNAs, leading to mRNA degradation or translational repression (Abelson et al., 2005). Although their biologic functions largely remain unclear, recent studies have demonstrated the involvement of this novel class of gene regulators not only in cancer-related processes but also in cardiovascular disease and other diseases (Sethupathy and Collins, 2008). Small variation in expression of a specific miRNA may effect on thousands of target mRNAs and result in diverse functional Abbreviations: LD, Linkage-disequilibrium; SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval. ☆ Grant sponsors: This study was supported by a grant from the National Natural Science Foundation of China (No. 30901230). ⁎ Corresponding author at: Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, School of Biological Science and Medical Engineering, Southeast University, 87 Ding Jiaqiao Rd., Nanjing 210009, China. Tel.: +86 25 832 72569; fax: +86 25 833 24322. E-mail address: [email protected] (L. Wang). 0378-1119/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.gene.2012.08.005

consequences (Paranjape et al., 2009), so miRNAs represent ideal candidates for cancer predisposition genes. In the cardiovascular system, miRNAs are not only important for heart and vascular development but also play an essential role in cardiac pathophysiology, such as arrhythmia, ischemia and coronary atherogenesis (D'Alessandra et al., 2010; Huang et al., 2010; Small et al., 2010). Single nucleotide polymorphisms (SNPs) or mutations occurring in the miRNA gene region may affect the property of miRNAs through altering miRNA expression and/or maturation (Saunders et al., 2007). However, the role of genetic variants in miRNAs on disease susceptibility remains largely unknown. Recently, several reports identified genetic variant in the precursor or mature miRNA sequence of hsa-miR-499 (rs3746444 T >C, thymine to cytosine) (http://www.ncbi.nlm.nih. gov/projects/SNP) as possible biomarkers, which were associated with multiple kind of diseases, such as those that occur in the lung cancer (Tian et al., 2009), breast cancer (Catucci et al., 2010; Hu et al., 2009), Gastric cancer (Okubo et al., 2010a), gallbladder cancer (Srivastava et al., 2010), Head and neck cancer (Liu et al., 2010), Prostate cancer (George et al., 2010), bladder cancer (Mittal et al., 2010), cervical squamous cell carcinoma (Zhou et al., 2011), Congenital heart disease (Xu et al., 2009), rheumatoid arthritis (Yang et al., 2011a), dilated cardiomyopathy (Zhou et al., 2010), coal workers' pneumoconiosis (Wang et al., 2010), chronic obstructive pulmonary disease (Li et al., 2011a), systemic lupus erythematosus (Zhang et al., 2011), tuberculosis (Li et al., 2011b),

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L. Wang et al. / Gene 508 (2012) 9–14

Table 1 Main characteristics of all studies included in the meta-analysis. First author (year)

Country

1.Hu, 2008 2. Tian, 2009 3.Xu, 2009 4. Okubo, 2010 5. Yang, 2010 6. Zhou, 2010 7. Srivastava, 2010 8. Wang, 2010 9. Catucci, 2010

China China China Japan China China Indian China Italy

Ethnicity

Asian Asian Asian Asian Asian Asian Asian Asian Caucasian

Disease type

Breast cancer Lung cancer Congenital heart disease Gastric cancer Rheumatoid arthritis Dilated cardiomyopathy Gallbladder cancer Pneumoconiosis. Breast cancer

Germany Caucasian Breast cancer 10. Liu, 2010 11.Li, 2011 12. Zhang, 2011 13.Li, 2011

USA China China China China

14. 15. 16. 17. 18. 19. 20.

George, 2011 Mittal, 2011 Zhou, 2011 Okubo, 2011 Zou, 2011 Zhi, 2012 Zhou J, 2012

Indian India China Japan China China China

Caucasian Asian Asian Asian (Tibetan) Asian (Han)

SCCHN COPD SLE Tuberculosis

Tuberculosis

Asian Asian Asian Asian Asian Asian Asian

Prostate cancer Bladder cancer Cervical carcinoma Ulcerative Colitis Schizophrenia Coronary heart disease Primary liver cancer

The cases' characters

The controls' characters

Study Matched⁎⁎ Genotype design⁎ detection⁎⁎⁎

Age (M ± SD)

Male (%)

Age (M ± SD)

Male (%)

51.60 ± 11.08 59.78 ± 10.04 6.78 ± 9.73 64.4 ± 11.2 48 ± 13 52.2 ± 12.8 Range [37–80] 69.5 ± 9.7 Median42 [21–80] Median46 [19–87] 57.2 ± 11.1 68.5 ± 8.2 34.5 ± 12.2 M:38.3 ± 17.2

Female 790(74.7) 528(52.6) 396(71.6) 36(17.3) 137(62.0) 79(34.3) 496(100) Female

51.77 ± 11.19 59.66 ± 9.83 6.72 ± 9.66 61.0 ± 13.5 45 ± 12 46.6 ± 10.9 Range [37–80] 65.7 ± 6.8 Median 45 [18–71]

Female 780(75.4) 558(53.3) 400(57.4) 45(18.8) 209(61.5) 78(33.9) 513(100) Female

H-B H-B H-B H-B H-B H-B H-B P-B H-B

Y Y Y Y Y N Y Y N

PCR-R PCR-R PCR-R PCR-R PCR-R PCR-R PCR-R PCR-R Taqman

Female

Median42 [16–68]

Female

H-B

N

PCR-R

837(75.5) 244(56.5) F:201(94.5) 98(66.7)

56.8 ± 11.0 65.3 ± 9.37 42.5 ± 12.6 M:34.9 ± 17.9

860(76.1) 273(53.4) F:95(93.3) 101(59.1)

H-B H-B H-B H-B

Y Y N N

PCR-R PCR-R PCR-R PCR-R

277(48.9)

H-B

N

PCR-R

230(100) 215(86.0) F:309(100) 195(48.4) 386(66.1) 386(66.1) NA

H-B H-B H-B H-B H-B H-B H-B

Y Y Y Y Y Y Y

PCR-R PCR-R PCR-R PCR-R PCR-R PCR-R PCR-R

F:31.3 ± 16.0 M:34.9 ± 15.6 F:35.6 ± 14.7 66.6 ± 6.2 59.0 44.96 ± 9.48 40.2 ± 13.9 26.53 ± 10.6 63.38 ± 9.61 52.10 ± 15.20

105(55.3) 159(100) 187(88.2) F:226(100) 96(56.5) 129(41.3) 637(69.5) 154(82.8)

F:37.0 ± 17.1 M:31.9 ± 13.5 F:35.4 ± 14.2 65.8 ± 7.3 57.8 NA 59.8 ± 13.4 65.0 ± 11.69 65.00 ± 11.69 NA

Abbr: COPD: chronic obstructive pulmonary disease; SLE: systemic lupus erythematosus; SCCHN: squamous cell carcinoma of the head and neck; NA: not available. ⁎ Study design: H-B, hospital-based; P-B: population-based. ⁎⁎ Matched: the cases and controls were matched by sex and/or age. Y: yes; N: no. ⁎⁎⁎ Genotype detection: PCR-R: PCR-RFLP.

ulcerative colitis (Okubo et al., 2011), schizophrenia (Zou et al., 2011), coronary heart disease (Zhi et al., 2012), and primary liver cancer (Zhou et al., 2012). Because the relatively small sample size in a single study might have low power to detect the effect of the polymorphisms on disease risk, here we reported our meta-analysis that may improve our evaluation of the association of hsa-miR-499 rs3746444 T > C polymorphism with disease risks.

published data for calculating odds ratios (ORs) with their 95% confidence intervals (95% CIs). 2.3. Data extraction

2. Materials and methods

The following data were abstracted independently in duplicate by two investigators using a standard protocol and data-collection form according to the inclusion criteria listed above: First author's surname, publication date, country, ethnicity, disease type, number of case/control, genotypes of cases and controls and P value for Hardy–Weinberg equilibrium were described (Table 1).

2.1. Study selection

2.4. Meta-analysis

We search of MEDLINE (http://www.ncbi.nlm.nih.gov/PubMed), EMBASE and CNKI database from Jan. 1, 2000 to Jan 1, 2012 using the following index terms: microRNA, miR-499, miR-499 and polymorphism(s), rs3746444. Hand searches were also performed to identify additional articles in the reference lists of included articles not retrieved by electronic search. Two researchers (Wang LN. and Qian SS.) independently reviewed all retrieved articles to identify articles that met inclusion criteria. When they disagreed, the reviewers met to discuss and achieve consensus. If the essential information was not presented, authors were contacted with best effort to identify published, unpublished and ongoing studies related to hsa-miR-499 rs3746444 polymorphism and disease risk.

OR corresponding to 95% CI was used to assess the strength of association between hsa-miR-499 rs3746444 T >C polymorphism and disease risk. The significance of the pooled OR was determined by the Z-test, and Pb 0.05 was considered as statistically significant. The meta-analysis examined the association between C allele and disease risk compared

Relevant studies identified and screened (n=25) Review (n=3) The same study population (n=1) Case only design (n=1)

2.2. Inclusion criteria The following criteria were set to choose the studies included in the current meta-analysis: (1) evaluation of the hsa-miR-499 rs3746444 polymorphism and disease risk; (2) a case–control design; (3) diseases were confirmed by histology or imaging or pathology; (4) and sufficient

Report finally included (n=20) Fig. 1. Studies identified with criteria for inclusion and exclusion.

L. Wang et al. / Gene 508 (2012) 9–14

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Table 2 The genotype distributions between the cases and controls of all studies included in the meta-analysis. First author (year)

Cases/Controls (n/n)

1.Hu, 2008 2. Tian, 2009 3.Xu, 2009 4. Okubo, 2010 5. Yang, 2010 6. Zhou, 2010 7. Srivastava, 2010 8. Wang, 2010 9. Catucci, 2010 10. Liu, 2010 11.Li, 2011 12. Zhang, 2011 13.Li, 2011 14. 15. 16. 17. 18. 19. 20.

George, 2011 Mittal, 2011 Zhou, 2011 Okubo, 2011 Zou, 2011 Zhi, 2012 Zhou J, 2012

1009/1093 1058/1035 1003/1046 552/697 208/240 221/321 230/230 496/513 756/1242 823/925 1109/1130 432/504 213/209 190/567 147/171 159/230 212/250 226/309 170/403 268/232 916/584 186/483

Cases [n (%)]

Controls [n (%)]

P-HWE

TT

TC

CC

TT

TC

CC

C (%)

707(70.1) 781(73.8) 746(74.4) 364(65.9) 159(76.4) 104(47.1) 112(48.7) 358(72.2) 414(54.8) 536(65.1) 745(67.2) 333(77.1) 157(73.7) 153(80.5) 95(64.6) 48(30.2) 95(44.8) 134(59.3) 102(60.0) 201(75.0) 629(68.7) 141(75.8)

258(25.6) 253(23.9) 236(23.5) 151(27.4) 42(20.2) 104(47.1) 97(42.2) 129(26.0) 295(39.0) 250(30.4) 309(27.9) 97(22.4) 50(23.5) 34(17.9) 43(29.3) 98(61.6) 92(43.4) 84(37.2) 62(36.5) 55(20.5) 201(21.9) 41(22.0)

44(4.3) 24(2.3) 21(2.1) 37(6.7) 7(3.4) 13(5.9) 21(9.1) 9(1.8) 47(6.2) 37(4.5) 55(4.9) 2(0.5) 6(2.8) 3(1.6) 9(6.1) 13(8.3) 25(11.8) 8(3.5) 6(3.5) 12(4.5) 86(9.4) 4(2.2)

816(74.7) 755(73.0) 774(74.0) 466(66.9) 182(75.8) 219(68.2) 121(52.6) 349(68.0) 704(56.7) 601(65.0) 710(62.8) 366(72.6) 158(75.6) 416(73.4) 134(78.4) 104(45.2) 121(48.4) 223(68.2) 272(67.5) 180(77.6) 396(67.8) 371(76.8)

248(22.7) 254(24.5) 239(22.8) 198(28.4) 53(22.1) 83(25.9) 94(40.9) 154(30.0) 452(36.4) 290(31.3) 366(32.4) 121(24.0) 45(21.5) 133(23.5) 32(18.7) 92(40.0) 94(37.6) 71(23.0) 111(27.5) 45(19.4) 167(28.6) 100(20.7)

29(2.6) 26(2.5) 33(3.2) 33(4.7) 5(2.1) 19(5.9) 15(6.5) 10(2.0) 86(6.9) 34(3.7) 54(4.8) 17(3.4) 6(2.9) 18(3.2) 5(2.9) 34(14.8) 35(14.0) 15(4.8) 20(5.0) 7(3.0) 21(3.6) 12(2.5)

14.8 14.0 14.6 18.9 13.1 18.8 26.9 17.0 25.1 19.4 21.0 15.4 13.6 14.9 12.3 34.8 32.8 16.3 18.7 12.7 17.9 12.8

with T allele (C vs T); homozygote CC was contrasted with TT (CC vs. TT) and recessive (CC vs. TT+CT) and dominant (CC+CT vs. TT) models were also used. Subgroup analyses were done by racial descent and disease types. For each subgroup, we estimated the between-study heterogeneity across the eligible comparisons using the Cochrane Q-test and the heterogeneity was considered significant for P b 0.10. Values from single studies were combined using models of both fixed effects (Mantel– Haenszel) and random effects (DerSimonian and Laird). Random effects incorporate an estimate of the between-study variance and tend to provide wider confidence intervals, when the results of the constituent studies differ among themselves. In the absence of between-study heterogeneity, the two methods provide identical results. For sensitivity analyses, we examined whether the excluding studies with substantial deviation from HWE among controls affected our pooled estimates of ORs. Publication bias was evaluated by a Begg modified funnel plot, in which the OR was plotted on a logarithmic scale against its standard error from each study. The Egger regression asymmetry

0.057 0.404 0.008 0.048 0.624 0.006 0.566 0.136 0.250 0.893 0.441 0.082 0.214 0.073 0.086 0.073 0.020 0.005 0.055 0.055 0.517 0.100

test was also performed to formally test publication bias. Finally, we use the formula to estimate the fail-safe number. All analyses were performed in Statistical Analysis System software (v.9.1; SAS Institute, Cary, NC) and Review Manage (v.4.2; Oxford, England). All the P values were two-sided.

3. Result 3.1. Study characteristics A total of 25 articles were achieved by literature search, from the PubMed, EMBASE and CNKI database, using different combinations of key terms (Fig. 1). Among these, 20 studies that included a total of 10,584 disease cases and 12,414 controls appeared to meet the inclusion criteria and were subjected to further examination. We excluded five studies (three were review (Fichtlscherer et al., 2011; Kukreja et al., 2011; Lafferty-Whyte et al., 2009), one was case only

Table 3 Meta-analysis for the hsa-miR-499 rs3746444 T> C polymorphism and cancer risk. Variables

na

OR(95%CI)

OR(95%CI)

OR(95%CI)

OR(95%CI)

OR(95%CI)

Total

20 1.07(0.99– 1.17)

b0.001 0.11 1.12(0.98– 1.26)

b0.001 0.09 1.10(0.90– 1.34)

0.030 0.35 1.11(0.99– 1.24)

10 1.05(0.99– 1.12) 10 1.05(0.96– 1.13)

0.060

0.10 1.13(0.98– 1.32) b0.001 0.28 1.09(0.87– 1.37)

b0.001 0.13 1.10(0.94– 1.30) b0.001 0.44 1.06(0.67– 1.68)

b0.001 0.05 1.16(1.00– 1.36) 0.240 0.60 0.95(0.79– 1.15)

b0.001 0.05 1.14(0.97– 1.35) 0.060 0.60 1.01(0.80– 1.28)

Disease variety Cancers Other diseases Ethnicities Asian

C vs T

18 1.11(1.00– 1.23) Caucasian 2 0.97(0.87– 1.08)

a b c

Pb

Pc

TC vs TT

Pb

Pc

CC vs TT

Pb

Pc

Pb

Pc

b0.001 0.07 1.04(0.84– 1.24)

0.002

0.72

0.550 0.24 1.12(0.98– 1.27) 0.004 0.55 1.10(0.89– 1.34)

0.002

0.09 1.05(0.90– 1.23) b0.001 0.38 1.14(0.91– 1.44)

0.190

0.57

0.020 0.12 1.15(1.01– 1.31) 0.660 0.95 0.96(0.82– 1.13)

b0.001 0.04 1.02(0.78– 1.34) 0.100 0.60 1.02(0.81– 1.28)

0.001

0.21

0.560

0.88

TC/CC vs TT (dominant)

Pb

Pc

CC vs TC/TT (recessive)

Number of comparisons. P value of Q-test for heterogeneity test. Random-effects model was used when P value for heterogeneity test b0.05; otherwise, fix-effects model was used. P value for significance.

b0.001 0.25

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Fig. 2. ORs of cancers and other diseases associated with hsa-miR-499 for the TC/CC genotypes compared with the TT genotype.

study design (Okubo et al., 2010b) and one had the same study population with the included study(Yang et al., 2011b). The selected study characteristics are summarized in Table 1, including the mean ages of the study population, the male proportion, the hospital based design or the population based design, frequency matched by the sex and/or age and genotype detection method. All studies were case–control studies, including nineteen kinds of diseases, among which there were eighteen studies of Asians, and two studies of Caucasians (Catucci et al., 2010; Liu et al., 2010). In the studies conducted by Catucci et al. and Li et al., the genotype frequencies were presented separately according to German study and Italian study, Tibetan and Han ethnic (Catucci et al. 2010; Li et al. 2010), and thus each of these studies was considered separately for meta-analysis. The distribution of genotypes in the controls of the studies was all in agreement with HWE except for five studies (Mittal et al., 2010; Okubo et al., 2010a; Xu et al., 2009; Zhou et al., 2010; Zhou et al., 2011) (P b 0.05) (Table 2). 3.2. Quantitative synthesis There was a variation in the C allele frequency of the hsa-miR-499 rs3746444 T>C polymorphism among the controls across different

ethnicities, ranging from 0.123 to 0.348. For European controls, the C allele frequency was 0.218, which was higher than that in Asian controls (0.179). As shown in Table 3, the hsa-miR-499 rs3746444 T > C polymorphism could not increase the risk of diseases in all genetic models, when all the eligible studies were pooled into the meta-analysis. Next we performed the meta-analysis under other genetic comparisons. Firstly, in different variety of disease, no significant association was found either in cancers and other disease groups in all genetic models. And then in the stratified analysis by ethnicity, significantly increased risks were found in Asians (OR =1.11; 95% CI= 1.00–1.23 for C vs. T; OR= 1.16; 95% CI= 1.00–1.36 for TC vs. TT; OR= 1.15; 95% CI = 1.01–1.31 for TC/CC vs. TT), however, no significant association of hsa-miR-499 rs3746444 T > C polymorphism with disease risks was found in Caucasians in all genetic models (Table 3, Figs. 2,3). 3.3. Sensitivity analysis We conducted a sensitivity analysis on the hsa-miR-499 rs3746444 T > C polymorphism and risk of disease excluding study departure from HWE among controls. The pooled OR estimates were similar with that of excluded those studies, so the results were not shown.

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Fig. 3. ORs of Asian and Caucasian associated with hsa-miR-499 for the TC/CC genotypes compared with the TT genotype.

3.4. Bias diagnostics Begger's funnel plot and Egger's test were performed to assess the publication bias of included studies. As shown in Fig. 4, the shapes of the funnel plots did not reveal any evidence of obvious asymmetry in all comparison models (i.e. C vs. T). Then, Egger's test was used to provide statistical evidence of funnel plot symmetry. The results still did not show any evidence of publication bias (t=1.22, P=0.238 for C vs. T), and the fail-safe number also showed no publication bias in this meta-analysis (Nfs0.05=283.913, Nfs0.01=129.556 for C vs. T). 4. Discussion MiRNAs participate in crucial biological process, including development, differentiation, apoptosis and proliferation (Bartel, 2004). Genetic variations in the miRNA genes could potentially influence the processing or target selection of miRNAs (Duan et al., 2007). Much research effort has been directed toward understanding the role of SNPs present in precursor and mature miRNA and their influences on susceptibility and

progression of various diseases. Hu et al. thought that hsa-miR-499 rs3746444 T >C polymorphism was associated with the increased risk of breast cancer (Hu et al., 2009), while Tian et al. observations suggested that hsa-miR-499 rs3746444 T>C polymorphism could not contribute to the risk of lung cancer (Tian et al., 2009). And then, so far there had been twenty published studies about the association between hsa-miR499 rs3746444 T>C polymorphism and nineteen kinds of disease risks. Given the controversial results in previous studies, we performed this meta-analysis to address the association of this sequence variant with various disease risk including 10,584 cases and 12,414 controls and quantify the potential between-study heterogeneity. We found that individuals carrying C allele, TC/CC and TC genotypes of hsa-miR499 rs3746444 T > C polymorphism were associated with an increased disease risk in the Asians but not in Caucasians, suggesting potentially different mechanisms underlying various disease pathogenesis such as tumorigenesis in different ethnicities. Due to the limited number of studies included in this analysis, we performed sub-analysis by disease types just for cancers and other disease. The pooled ORs of 10 studies conducted on the cancers in

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Fig. 4. Begg's funnel plot for publication bias test (C vs T). Each point represents a separate study for the indicated association. Log OR, natural logarithm of OR.

diversity genetic models, including nine kinds of cancers, suggested no significant association between this polymorphism and cancers, consistent with the results previously reported (Tian et al., 2009). While for the other ten kinds of diseases, no significant association was found. One important property of the genetic polymorphisms is that their incidence can vary substantially between different racial or ethnic populations. In this meta-analysis, we found significant differences in the prevalence of the rs3746444 C allele among controls of Asian (0.179) and Caucasian (0.218). Some limitations of this meta-analysis should be considered. First, caution must be made about the interpretation of the results because of the relatively large heterogeneity in hsa-miR-499 rs3746444 T>C polymorphism studies. In the subgroup analyses stratified by disease type and racial descent respectively, it can be found that the heterogeneity of the subgroup reduced significantly especially in subgroups of cancers and European. Therefore, it can be presumed that the relatively large heterogeneity mainly results from differences of ethnicity and various disease types. Second, publication bias may exist, because only published studies were included in this meta-analysis, although the result for publication bias was not statistically significant. Finally, lack of the original data of the reviewed studies limited our further evaluation of potential interactions, because the interactions between gene-to-gene and geneto-environment may modulate various disease risks. Therefore, further studies regarding the relationships among the hsa-miR-499 rs3746444 T>C genetic variations, miR-499 levels, the risk of various diseases, and the factors mentioned above will be urgently needed. In conclusion, this meta-analysis suggested that hsa-miR-499 rs374 6444 T>C polymorphism is associated with an increased disease risk especially in Asians. Larger well-designed studies with subjects of the same ethnic background and tissue-specific biochemical and biological characterization are warranted to validate these findings. Conflict of interest statement None of the authors has any potential financial conflict of interest related to this manuscript. References Abelson, J.F., et al., 2005. Sequence variants in SLITRK1 are associated with Tourette's syndrome. Science 310, 317–320.

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