MiRNA polymorphisms had potential to be biomarkers for hepatocellular cancer (HCC) susceptibility. Recently, miRNA single nucleotide polymorphisms (SNPs) were reported to be associated with HCC risk, but the results were inconsistent. We performed a systematic review with a meta-analysis for the association of miRNA SNPs with HCC risk. Thirty-seven studies were included with a total of 11821 HCC patients and 15359 controls in this meta-analysis. We found hsa-mir-146a rs2910164 was associated with a decreased HCC risk in the recessive model (P=0.017, OR = 0.90, 95% confidence interval (CI) = 0.83–0.98). While hsa-mir-34b/c rs4938723 was related with an increased HCC risk in the co-dominant model (P=0.016, odds ratio (OR) = 1.19, 95%CI = 1.03–1.37). When analyzing the Hepatitis B virus (HBV)-related HCC risk, hsa-mir-196a-2 rs11614913 was associated with a decreased HBV-related HCC risk in the co-dominant and allelic models. And hsa-mir-149 rs2292832 was found to be associated with a decreased HBV-related HCC risk in the dominant and recessive models. In conclusion, hsa-mir-146a rs2910164 and hsa-mir-34b/c rs4938723 could be biomarkers for the HCC risk while hsa-mir-196a-2 rs11614913 and hsa-mir-149 rs2292832 had potential to be biomarkers for HBV-related HCC risk.

Introduction

MiRNAs are 19–24 nts short nucleotide sequences, which could complementarily combine with multiple target sequences and one miRNA could regulate multiple different target genes [1]. Single nucleotide polymorphisms (SNPs) are the common variations in the genetic polymorphisms and are known as the potential biomarkers for predicting the cancer risk [2]. If there is a variation in miRNA gene, it could affect the quality and quantity of mature miRNA and even affect hundreds of targetted genes regulated by the changed miRNA [3]. There are two types of miRNA-SNP: pri-miRNA SNPs and pre-miRNA SNPs. pri-miRNA SNPs are located over approximately 500–3000bp of the miRNA gene, while pre-miRNA SNPs are found in a 60–70bp region. The function of miRNA-SNPs depends on its location; therefore, pri-miRNA SNPs may have more important roles than pre-miRNA SNPs.

Hepatocellular cancer (HCC) is now the second leading cause of cancer deaths worldwide [4]. In HCC patients, approximately 50% are related with Hepatitis B virus (HBV) [5,6], and HBV is still the major cause of HCC, especially in Asia-Pacific and Sub-Saharan Africa [7]. The etiology of HBV-related HCC is reported different from that of no chronic HBV infection, which is mainly caused by the HBV, host-related such as SNPs, and the dietary and lifestyle factors [8]. Thus, the prediction for the HCC risk, especially the HBV-related HCC risk is essential to prevent the incidence of HCC and increase the early diagnosis of HCC.

Until now, several miRNA-SNPs have been reported to be associated with many tumors such as gastric cancer [9], esophageal cancer [10], breast cancer [11], and neuroblastoma [12]. And miRNA-SNPs were also related with HCC risk [13,14] and could be biomarkers for the precaution for HCC risk, but system analysis or update meta-analysis for all the miRNA-SNPs associated with HCC risk was rare, especially the latest research progress. In addition, many studies supplied data about the HBV-related HCC risk, but few meta-analyses considered this important factor with the etiology of HCC incidence. In the present study, we systematically reviewed published data and comprehensively analyzed and integrated all individual studies for miRNA-SNPs and HCC and/or HBV-related HCC risk. On the basis of systematic review, we conducted a meta-analysis to combine all the available studies and to investigate for the five highly studied miRNA-SNPs whether miRNA polymorphisms contribute to the risk of HCC and/or HBV-related HCC risk.

Methods

Publication search

The present study was carried out on the basis of Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) [15]. Studies reporting on the association between the miRNAs polymorphism and HCC risk were identified by entering the following search terms into PubMed and Web of Science: ‘miRNA’; and ‘polymorphisms/variants/variation/single nucleotide polymorphism/SNPs’; ‘hepatocellular’; and ‘cancer/carcinoma/tumor/neoplasm’ published until 23 February 2018. Two independent investigators (B.-g.W. and Q.X.) performed this literature search. Eligible studies met the following criteria: (i) investigate the relationship between miRNA-SNPs and HCC risk and (ii) case–control study. Articles were excluded based on the following criteria: (i) duplicated articles or data; (ii) not relevant to HCC risk or miRNA-SNPs; (iii) functional studies; and (iv) lack of available data.

Data extraction

Two investigators (B.-g.W. and Q.X.) extracted the data independently and reached consensus regarding all the items. Study descriptions were derived from the full text including the author’s name, year of publication, country of origin, source of control groups, genotyping method, total number of the case and control groups and each genotype. Considering parts of the studies supplied data concerning HBV related HCC risk, we collected them for a subgroup analysis.

False-positive report probability analysis and trial sequential analysis

The False-positive report probability (FPRP) values at different prior probability levels for all significant findings were calculated as published reference studies [16–18]. Briefly, 0.2 was set as FPRP threshold and assigned a prior probability of 0.1 for an association with genotypes under investigation. A FPRP value <0.2 denoted a noteworthy association.

TSA was performed as described by user manual for trial sequential analysis [18]. After adopting a level of significance of 5% for type I error and of 30% for type II error, the required information size was calculated, and TSA monitoring boundaries were built [19,20].

Statistics analysis

Hardy–Weinberg equilibrium (HWE) was calculated for control group using the Chi-square test and P<0.05 was considered to be significant disequilibrium. The strength of the association between the miRNA polymorphism and HCC risk was estimated by odds ratios (ORs) with 95% confidence intervals (CIs). In the absence of between-study heterogeneity for Q-statistic I2 < 50%, fixed-effect model was reported to conserve statistical power, otherwise, the random-effect model was used [19,20]. Risk of publication bias across studies were assessed by Begg’s rank correlation and the Egger’s linear regression, and if P>0.10 was considered to be lack of publication bias [21]. Sensitivity analysis was conducted by eliminating studies one by one. All analyses were conducted using Stata software 11.0 and the results were considered statistically significant when the P-value was less than 0.05.

Results

Characteristics of the eligible studies

As shown in the flow diagram in Figure 1, a total of 165 articles were included in this systematic review, and finally, 37 researches, 11821 HCC patients and 15359 controls were involved in our meta-analysis after multiple steps of selection (Figure 1). The characteristics of each included study and the genotype frequency distributions of each SNPs are presented in Table 1. We also listed the genotype of HBV-related HCC group as data for the subgroup analysis. Then, HWE was calculated and P of HWE in control group for several studies did not reach genetic equilibrium, then, studies for PHWE<0.05 were excluded in the following analysis.

Studies identified in this meta-analysis based on the criteria for inclusion and exclusion

Table 1
Characteristics of literature included for this meta-analysis for HCC risk
Number First author Year Country Ethnicity Source of control groups Genotyping method hsa-miRNA Sample size Case Control HBV-related HCC P of HWE in control group Citation 
        Case Control Homozygote wild Heterozygote Homozygote variant Homozygote wild Heterozygote Homozygote variant Homozygote wild Heterozygote Homozygote variant   
H. Akkız 2011 Turkish Caucasian HB PCR-RFLP hsa-mir-196a-2 185 185 77 86 22 58 87 40 46 48 11 0.492 [49
Hikmet Akkız 2011 Turkish Caucasian HB PCR-RFLP hsa-mir-499 222 222 45 87 90 47 93 82    0.950 [50
Hikmet Akkız 2011 Turkish Caucasian HB PCR-RFLP hsa-mir-146a 222 222 137 75 10 144 67 11 75 51 0.384 [51
Yin-Hung Chu 2014 China Asian HB PCR-RFLP hsa-mir-146a 188 337 22 82 84 50 146 141 47  32 0.230 [24
     PCR-RFLP hsa-mir-196a-2 188 337 41 81 66 70 167 100 46  33 0.986  
     PCR-RFLP hsa-mir-499 188 337 119 60 281 55 46  27 0.321  
     Real-time PCR hsa-mir-149 188 337 13 36 139 27 64 246 19  54 <0.001  
Ning Cong 2014 China Asian HB PCR-RFLP hsa-mir-146a 206 218 27 85 94 17 84 117 15 35 39 0.723 [52
Yu-Xia Hao 2013 China Asian HB PCR-RFLP hsa-mir-146a 226 281 23 133 70 30 154 97    0.056 [53
      hsa-mir-196a-2 235 282 77 126 32 67 160 55 46 71 16 0.051  
      hsa-mir-499 235 281 160 51 24 204 61 16    <0.001  
Won Hee Kim 2012 Korea Asian PB PCR-RFLP hsa-mir-146a 159 201 14 88 57 24 103 74 13 71 43 0.190 [54
      hsa-mir-196a-2 159 201 34 84 41 45 107 49 24 70 33 0.356  
      hsa-mir-499 159 201 109 47 120 74 7 91 34 0.278  
      hsa-mir-149 159 201 14 64 81 21 97 83 68 49 10 0.345  
Jian-Tao Kou 2014 China Asian HB PCR-RFLP hsa-mir-146a 271 532 25 147 99 56 297 179    <0.001 [25
      hsa-mir-196a-2 271 532 84 150 37 125 304 103 56 85 18 <0.001  
      hsa-mir-499 271 532 210 49 12 391 110 31    <0.001  
      hsa-mir-149 270 532 113 122 35 202 253 77    0.877  
D. Li 2015 China Asian HB PCR-RFLP hsa-mir-146a 184 184 43 83 58 52 85 47 97 (allele)  101 (allele) 0.210 [55
      hsa-mir-499 184 184 128 39 17 117 43 24 146 (allele)  52 (allele) 0.780  
10 Juan Li 2016 China Asian NM Sequencing hsa-mir-196a-2 109 105 25 64 20 18 52 35    0.861 [56
11 Xinhong Li 2015 China Asian HB PCR-RFLP hsa-mir-146a 266 266 151 86 29 166 81 19    0.060 [57
      hsa-mir-196a-2 266 266 84 131 51 113 123 30 33  77 0.689  
      hsa-mir-499 266 266 150 92 24 166 83 17    0.140  
      hsa-mir-149 266 266 91 130 45 108 124 34    0.864  
12 Xiaodong Li 2010 China Asian HB PCR-RFLP hsa-mir-196a-2 310 222 78 150 82 42 102 78    0.402 [58
13 M.F. Liu 2014 China Asian NM Sequenom hsa-mir-149 327 327 84 143 100 56 138 133 109  23 0.054 [59
14 Y.F. Shan 2013 China Asian HB PCR-RFLP hsa-mir-146a 172 185 28 62 82 36 71 78 13 25 33 0.080 [60
      hsa-mir-499 172 185 128 37 123 48 14 54 14 0.120  
15 Eman A. Toraih 2016 Egypt Caucasian PB Real-time PCR hsa-mir-196a-2 60 150 25 32 80 53 17    0.082 [61
      hsa-mir-499 60 150 28 23 57 66 27    0.307  
16 X.H. Wang 2014 China Asian HB PCR-RFLP hsa-mir-499 152 304 98 32 22 218 62 24 59 18 12 <0.001a [62
      hsa-mir-149 152 304 13 72 67 43 148 113 40 42 0.623  
17 Yu Xiang 2012 China Asian HB PCR-RFLP hsa-mir-146a 100 100 27 45 28 21 46 33 18 34 21 0.506 [63
      hsa-mir-499 100 100 36 40 24 54 36 10 27 30 16 0.284  
18 Teng Xu 2008 China Asian HB PCR-RFLP hsa-mir-146a 479 504 80 241 158 58 249 197    0.119 [64
19 Pingping Yan 2015 China Asian HB PCR-RFLP hsa-mir-146a 274 328 35 145 94 36 169 123    0.050 [65
      hsa-mir-196a-2 274 328 46 147 81 27 165 136 46 81 41 0.018a  
      hsa-mir-499 274 328 147 98 29 188 112 28    0.060  
      hsa-mir-149 274 328 66 133 75 72 156 100    0.449  
20 Jun Zhang 2013 China Asian PB Sequenom hsa-mir-146a 997 998 163 503 331 156 475 367 124 390 257 0.911 [66
      hsa-mir-196a-2 996 995 214 488 294 165 502 328 171 376 224 0.245  
21 L.H. Zhang 2016 China Asian HB PCR-RFLP hsa-mir-146a 175 302 37 86 52 30 135 137    0.697 [67
      hsa-mir-196a-2 175 302 25 85 65 42 138 122    0.766  
      hsa-mir-499 175 302 115 49 11 197 87 18    0.052  
22 Xin-wei Zhang 2011 China Asian PB PIRA-PCR hsa-mir-146a 925 840 156 450 319 151 386 303    0.149 [68
      hsa-mir-196a-2 934 837 208 449 277 181 417 239    0.972  
23 Bing Zhou 2014 China Asian NM Sequenom hsa-mir-146a 266 281 40 153 73 30 154 97 24 89 40 0.007a [69
      hsa-mir-196a-2 266 281 93 139 34 66 160 55 57 80 16 0.019b  
      hsa-mir-499 266 281 184 59 23 204 61 16    <0.001a  
24 Juan Zhou 2012 China Asian NM PCR-RFLP hsa-mir-146a 186 483 33 86 67 71 254 158    0.056 [70
      hsa-mir-499 186 483 141 41 371 100 12    0.100  
25 Hong-Zhi Zou 2013 China Asian HB PCR-RFLP hsa-mir-499 185 204 136 44 139 52 13 54 14 0.060 [71
26 Xi-Dai Long 2016 China Asian HB Real-time PCR hsa-mir-146a 1706 2270 464 858 384 639 1187 444    0.011c [46
      hsa-mir-196a-2 1704 2270 484 867 353 718 1138 414    0.318  
      hsa-mir-499 1706 2270 1073 492 141 1460 598 212    <0.001c  
      hsa-mir-149 1706 2270 1104 395 207 1503 512 255    <0.001c  
27 Rui Wang 2014 China Asian PB Sequenom hsa-mir-149 172 267 21 68 83 36 105 126 16 50 57 0.066 [72
28 Jia-Hui Qi 2014 China Asian PB HRM-PCR hsa-mir-146a 314 406 165 149 244 159    <0.001a [73
      hsa-mir-196a-2 314 406 45 209 60 71 214 121    0.156  
      hsa-mir-499 314 406 195 117 301 101    0.157  
29 Yanyun Ma 2014 China Asian HB Sequenom hsa-mir-499 981 969 724 241 16 765 179 25 558 189 13 <0.001b [74
30 Yifang Han 2013 China Asian PB and HB mixed qPCR hsa-mir-34b/c 1013 999 451 444 118 456 424 119    0.183 [22
     qPCR hsa-mir-196a-2 1017 1009 207 505 305 220 485 304    0.310 [75
31 Myung Su Son 2013 Korea Asian HB PCR-RFLP hsa-mir-34b/c 157 201 69 75 13 110 74 17    0.371  
32 Yan Xu 2011 China Asian PB PCR-RFLP hsa-mir-34b/c 502 549 204 236 62 266 229 54    0.647 [36
33 L.L. Chen 2016 China Asian HB PCR-RFLP hsa-mir-34b/c 286 572 102 146 38 272 267 33    0.002a [76
34 Pornpitra Pratedrat 2015 Thailand Asian PB Real-time PCR hsa-mir-101-1 104 95 37 51 16 39 43 13    0.835 [77
      hsa-mir-149 104 95 11 27 66 24 62    0.010c  
35 Olfat Shaker 2017 Egypt Caucasian NM Real-time PCR hsa-mir-101-1 36 32 14 12 10 11 20    0.029c [78
36 Z.Y. Sui 2016 China Asian HB Sequencing let-7i 89 95 25 64  55  40    0.482 [79
37 Fang Huang 2011 China Asian HB qPCR let-7i 1261 1319 542 564 155 581 585 153    0.756 [80
Number First author Year Country Ethnicity Source of control groups Genotyping method hsa-miRNA Sample size Case Control HBV-related HCC P of HWE in control group Citation 
        Case Control Homozygote wild Heterozygote Homozygote variant Homozygote wild Heterozygote Homozygote variant Homozygote wild Heterozygote Homozygote variant   
H. Akkız 2011 Turkish Caucasian HB PCR-RFLP hsa-mir-196a-2 185 185 77 86 22 58 87 40 46 48 11 0.492 [49
Hikmet Akkız 2011 Turkish Caucasian HB PCR-RFLP hsa-mir-499 222 222 45 87 90 47 93 82    0.950 [50
Hikmet Akkız 2011 Turkish Caucasian HB PCR-RFLP hsa-mir-146a 222 222 137 75 10 144 67 11 75 51 0.384 [51
Yin-Hung Chu 2014 China Asian HB PCR-RFLP hsa-mir-146a 188 337 22 82 84 50 146 141 47  32 0.230 [24
     PCR-RFLP hsa-mir-196a-2 188 337 41 81 66 70 167 100 46  33 0.986  
     PCR-RFLP hsa-mir-499 188 337 119 60 281 55 46  27 0.321  
     Real-time PCR hsa-mir-149 188 337 13 36 139 27 64 246 19  54 <0.001  
Ning Cong 2014 China Asian HB PCR-RFLP hsa-mir-146a 206 218 27 85 94 17 84 117 15 35 39 0.723 [52
Yu-Xia Hao 2013 China Asian HB PCR-RFLP hsa-mir-146a 226 281 23 133 70 30 154 97    0.056 [53
      hsa-mir-196a-2 235 282 77 126 32 67 160 55 46 71 16 0.051  
      hsa-mir-499 235 281 160 51 24 204 61 16    <0.001  
Won Hee Kim 2012 Korea Asian PB PCR-RFLP hsa-mir-146a 159 201 14 88 57 24 103 74 13 71 43 0.190 [54
      hsa-mir-196a-2 159 201 34 84 41 45 107 49 24 70 33 0.356  
      hsa-mir-499 159 201 109 47 120 74 7 91 34 0.278  
      hsa-mir-149 159 201 14 64 81 21 97 83 68 49 10 0.345  
Jian-Tao Kou 2014 China Asian HB PCR-RFLP hsa-mir-146a 271 532 25 147 99 56 297 179    <0.001 [25
      hsa-mir-196a-2 271 532 84 150 37 125 304 103 56 85 18 <0.001  
      hsa-mir-499 271 532 210 49 12 391 110 31    <0.001  
      hsa-mir-149 270 532 113 122 35 202 253 77    0.877  
D. Li 2015 China Asian HB PCR-RFLP hsa-mir-146a 184 184 43 83 58 52 85 47 97 (allele)  101 (allele) 0.210 [55
      hsa-mir-499 184 184 128 39 17 117 43 24 146 (allele)  52 (allele) 0.780  
10 Juan Li 2016 China Asian NM Sequencing hsa-mir-196a-2 109 105 25 64 20 18 52 35    0.861 [56
11 Xinhong Li 2015 China Asian HB PCR-RFLP hsa-mir-146a 266 266 151 86 29 166 81 19    0.060 [57
      hsa-mir-196a-2 266 266 84 131 51 113 123 30 33  77 0.689  
      hsa-mir-499 266 266 150 92 24 166 83 17    0.140  
      hsa-mir-149 266 266 91 130 45 108 124 34    0.864  
12 Xiaodong Li 2010 China Asian HB PCR-RFLP hsa-mir-196a-2 310 222 78 150 82 42 102 78    0.402 [58
13 M.F. Liu 2014 China Asian NM Sequenom hsa-mir-149 327 327 84 143 100 56 138 133 109  23 0.054 [59
14 Y.F. Shan 2013 China Asian HB PCR-RFLP hsa-mir-146a 172 185 28 62 82 36 71 78 13 25 33 0.080 [60
      hsa-mir-499 172 185 128 37 123 48 14 54 14 0.120  
15 Eman A. Toraih 2016 Egypt Caucasian PB Real-time PCR hsa-mir-196a-2 60 150 25 32 80 53 17    0.082 [61
      hsa-mir-499 60 150 28 23 57 66 27    0.307  
16 X.H. Wang 2014 China Asian HB PCR-RFLP hsa-mir-499 152 304 98 32 22 218 62 24 59 18 12 <0.001a [62
      hsa-mir-149 152 304 13 72 67 43 148 113 40 42 0.623  
17 Yu Xiang 2012 China Asian HB PCR-RFLP hsa-mir-146a 100 100 27 45 28 21 46 33 18 34 21 0.506 [63
      hsa-mir-499 100 100 36 40 24 54 36 10 27 30 16 0.284  
18 Teng Xu 2008 China Asian HB PCR-RFLP hsa-mir-146a 479 504 80 241 158 58 249 197    0.119 [64
19 Pingping Yan 2015 China Asian HB PCR-RFLP hsa-mir-146a 274 328 35 145 94 36 169 123    0.050 [65
      hsa-mir-196a-2 274 328 46 147 81 27 165 136 46 81 41 0.018a  
      hsa-mir-499 274 328 147 98 29 188 112 28    0.060  
      hsa-mir-149 274 328 66 133 75 72 156 100    0.449  
20 Jun Zhang 2013 China Asian PB Sequenom hsa-mir-146a 997 998 163 503 331 156 475 367 124 390 257 0.911 [66
      hsa-mir-196a-2 996 995 214 488 294 165 502 328 171 376 224 0.245  
21 L.H. Zhang 2016 China Asian HB PCR-RFLP hsa-mir-146a 175 302 37 86 52 30 135 137    0.697 [67
      hsa-mir-196a-2 175 302 25 85 65 42 138 122    0.766  
      hsa-mir-499 175 302 115 49 11 197 87 18    0.052  
22 Xin-wei Zhang 2011 China Asian PB PIRA-PCR hsa-mir-146a 925 840 156 450 319 151 386 303    0.149 [68
      hsa-mir-196a-2 934 837 208 449 277 181 417 239    0.972  
23 Bing Zhou 2014 China Asian NM Sequenom hsa-mir-146a 266 281 40 153 73 30 154 97 24 89 40 0.007a [69
      hsa-mir-196a-2 266 281 93 139 34 66 160 55 57 80 16 0.019b  
      hsa-mir-499 266 281 184 59 23 204 61 16    <0.001a  
24 Juan Zhou 2012 China Asian NM PCR-RFLP hsa-mir-146a 186 483 33 86 67 71 254 158    0.056 [70
      hsa-mir-499 186 483 141 41 371 100 12    0.100  
25 Hong-Zhi Zou 2013 China Asian HB PCR-RFLP hsa-mir-499 185 204 136 44 139 52 13 54 14 0.060 [71
26 Xi-Dai Long 2016 China Asian HB Real-time PCR hsa-mir-146a 1706 2270 464 858 384 639 1187 444    0.011c [46
      hsa-mir-196a-2 1704 2270 484 867 353 718 1138 414    0.318  
      hsa-mir-499 1706 2270 1073 492 141 1460 598 212    <0.001c  
      hsa-mir-149 1706 2270 1104 395 207 1503 512 255    <0.001c  
27 Rui Wang 2014 China Asian PB Sequenom hsa-mir-149 172 267 21 68 83 36 105 126 16 50 57 0.066 [72
28 Jia-Hui Qi 2014 China Asian PB HRM-PCR hsa-mir-146a 314 406 165 149 244 159    <0.001a [73
      hsa-mir-196a-2 314 406 45 209 60 71 214 121    0.156  
      hsa-mir-499 314 406 195 117 301 101    0.157  
29 Yanyun Ma 2014 China Asian HB Sequenom hsa-mir-499 981 969 724 241 16 765 179 25 558 189 13 <0.001b [74
30 Yifang Han 2013 China Asian PB and HB mixed qPCR hsa-mir-34b/c 1013 999 451 444 118 456 424 119    0.183 [22
     qPCR hsa-mir-196a-2 1017 1009 207 505 305 220 485 304    0.310 [75
31 Myung Su Son 2013 Korea Asian HB PCR-RFLP hsa-mir-34b/c 157 201 69 75 13 110 74 17    0.371  
32 Yan Xu 2011 China Asian PB PCR-RFLP hsa-mir-34b/c 502 549 204 236 62 266 229 54    0.647 [36
33 L.L. Chen 2016 China Asian HB PCR-RFLP hsa-mir-34b/c 286 572 102 146 38 272 267 33    0.002a [76
34 Pornpitra Pratedrat 2015 Thailand Asian PB Real-time PCR hsa-mir-101-1 104 95 37 51 16 39 43 13    0.835 [77
      hsa-mir-149 104 95 11 27 66 24 62    0.010c  
35 Olfat Shaker 2017 Egypt Caucasian NM Real-time PCR hsa-mir-101-1 36 32 14 12 10 11 20    0.029c [78
36 Z.Y. Sui 2016 China Asian HB Sequencing let-7i 89 95 25 64  55  40    0.482 [79
37 Fang Huang 2011 China Asian HB qPCR let-7i 1261 1319 542 564 155 581 585 153    0.756 [80

Abbreviations: HB, hospital based; HRM-PCR, high resolution melting-PCR; NM, not mentioned; PB, population based; PCR-RFLP, PCR-restriction fragment length polymorphism; PIRA-PCR, primer introduced restriction analysis–PCR.

qPCR, quantitative polymerase chain reaction. The bold values used in ‘P of HWE in control group’ means studies did not reach genetic equilibrium and were excluded in the following analysis.

Quantitative data synthesis of miRNA SNPs

We found hsa-mir-146a rs2910164 was associated with a decreased HCC risk in the recessive model (P=0.017, OR = 0.90, 95%CI = 0.83–0.98; Table 2 and Figure 2). While hsa-mir-34b/c rs4938723 was related with an increased HCC risk in the co-dominante model (P=0.016, OR = 1.19, 95%CI = 1.03–1.37). In the stratified analysis, individuals carrying hsa-mir-146a rs2910164 variant genotype were associated with a decreased HCC risk in the Asian population subgroup (P=0.017, OR = 0.90, 95%CI = 0.83–0.98) while individuals carrying hsa-mir-196a-2 rs11614913 variant genotype were related with a decreased HCC risk in the Caucasian population subgroup (P=0.005, OR = 0.44, 95%CI = 0.25–0.78).

Forest plot of ORs for the association of hsa-mir-146a and hsa-mir-34b/c polymorphism with HCC risks

Figure 2
Forest plot of ORs for the association of hsa-mir-146a and hsa-mir-34b/c polymorphism with HCC risks

(A) hsa-mir-146a polymorphism stratified by ethnicity in recessive model; (B) hsa-mir-34b/c polymorphism in co-dominant model (heterozygote compared with wild-type).

Figure 2
Forest plot of ORs for the association of hsa-mir-146a and hsa-mir-34b/c polymorphism with HCC risks

(A) hsa-mir-146a polymorphism stratified by ethnicity in recessive model; (B) hsa-mir-34b/c polymorphism in co-dominant model (heterozygote compared with wild-type).

Table 2
Meta-analysis of the association between common SNPs and HCC risk
Stratification n Heterozygote compared with wild-type Mutation homozygote compared with wild-type Dominant model Recessive model Allelic model 
  OR (95%CI) P I2 (%) OR (95%CI) P I2 (%) OR (95%CI) P I2 (%) OR (95%CI) P I2 (%) OR (95%CI) P I2 (%) 
hsa-mir-146a 15 0.98 0.812 20.4 0.90 0.297 59.41 0.94 0.472 50.01 0.90 0.017 40.7 1.05 0.315 61.21 
rs2910164 G/C  (0.88–1.10)   (0.73–1.10)   (0.80–1.11)   (0.83–0.98)   (0.95–1.16)   
 Asians 14 0.97 0.636 22.4 0.89 0.306 62.31 0.93 0.383 52.11 0.90 0.017 44.9 1.06 0.272 63.21 
  (0.87–1.09)   (0.71–1.11)   (0.78–1.10)   (0.83–0.98)   (0.96–1.18)   
 Caucasian 1.18 0.430 NA 0.96 0.920 NA 1.45 0.491 NA 0.91 0.823 NA 0.92 0.619 NA 
  (0.79–1.76)   (0.39–2.32)   (0.78–1.69)   (0.38–2.18)   (0.67–1.27)   
hsa-mir-196a-2 14 1.00 0.992 53.41 0.86 0.179 73.51 0.96 0.636 64.91 0.88 0.122 72.11 1.06 0.244 74.01 
rs11614913 C/T  (0.87–1.15)   (0.70–1.07)   (0.83–1.12)   (0.74–1.04)   (0.96–1.18)   
 Asians 12 0.99 0.929 50.21 0.92 0.420 73.21 0.97 0.703 63.91 0.92 0.305 72.01 1.05 0.400 74.11 
  (0.87–1.14)   (0.70–1.07)   (0.83–1.13)   (0.78–1.08)   (0.94–1.16)   
 Caucasian 1.17 0.743 82.81 0.44 0.005 0.0 0.99 0.976 83.01 0.47 0.005 0.0 1.19 0.517 73.81 
  (0.46–2.97)   (0.25–0.78)   (0.40–2.42)   (0.28–0.79)   (0.70–2.02)   
hsa-mir-499 13 1.10 0.376 67.41 1.04 0.850 58.31 1.11 0.410 76.71 1.04 0.829 48.63 0.92 0.418 81.01 
rs3746444 A/G  (0.89–1.37)   (0.71–1.51)   (0.87–1.40)   (0.75–1.43)   (0.74–1.13)   
 Asians 11 1.14 0.264 70.71 1.07 0.779 63.91 1.15 0.315 79.41 1.04 0.861 56.01 0.89 0.367 83.41 
  (0.90–1.45)   (0.67–1.71)   (0.88–1.40)   (0.68–1.57)   (0.70–1.14)   
 Caucasian 0.87 0.448 0.0 1.00 0.993 2.5 0.91 0.613 11.1 1.09 0.632 0.0 1.000 1.000 41.1 
  (0.58–1.29)   (0.65–1.55)   (0.63–1.31)   (0.77–1.54)   (0.80–1.26)   
hsa-mir-149 0.97 0.696 16.6 1.03 0.882 68.21 0.99 0.962 56.61 1.03 0.828 61.11 1.02 0.670 73.41 
rs2292832 C/T  (0.82–1.14)   (0.72–1.47)   (0.77–1.28)   (0.81–1.30)   (0.93–1.12)   
hsa-mir-34b/c 1.19 0.016 52.62 1.15 0.221 20.4 1.25 0.065 58.61 1.06 0.580 0.0 0.87 0.100 54.21 
rs4938723 T/C  (1.03–1.37)   (0.92–1.44)   (0.99–1.58)   (0.86–1.31)   (0.74–1.03)   
Stratification n Heterozygote compared with wild-type Mutation homozygote compared with wild-type Dominant model Recessive model Allelic model 
  OR (95%CI) P I2 (%) OR (95%CI) P I2 (%) OR (95%CI) P I2 (%) OR (95%CI) P I2 (%) OR (95%CI) P I2 (%) 
hsa-mir-146a 15 0.98 0.812 20.4 0.90 0.297 59.41 0.94 0.472 50.01 0.90 0.017 40.7 1.05 0.315 61.21 
rs2910164 G/C  (0.88–1.10)   (0.73–1.10)   (0.80–1.11)   (0.83–0.98)   (0.95–1.16)   
 Asians 14 0.97 0.636 22.4 0.89 0.306 62.31 0.93 0.383 52.11 0.90 0.017 44.9 1.06 0.272 63.21 
  (0.87–1.09)   (0.71–1.11)   (0.78–1.10)   (0.83–0.98)   (0.96–1.18)   
 Caucasian 1.18 0.430 NA 0.96 0.920 NA 1.45 0.491 NA 0.91 0.823 NA 0.92 0.619 NA 
  (0.79–1.76)   (0.39–2.32)   (0.78–1.69)   (0.38–2.18)   (0.67–1.27)   
hsa-mir-196a-2 14 1.00 0.992 53.41 0.86 0.179 73.51 0.96 0.636 64.91 0.88 0.122 72.11 1.06 0.244 74.01 
rs11614913 C/T  (0.87–1.15)   (0.70–1.07)   (0.83–1.12)   (0.74–1.04)   (0.96–1.18)   
 Asians 12 0.99 0.929 50.21 0.92 0.420 73.21 0.97 0.703 63.91 0.92 0.305 72.01 1.05 0.400 74.11 
  (0.87–1.14)   (0.70–1.07)   (0.83–1.13)   (0.78–1.08)   (0.94–1.16)   
 Caucasian 1.17 0.743 82.81 0.44 0.005 0.0 0.99 0.976 83.01 0.47 0.005 0.0 1.19 0.517 73.81 
  (0.46–2.97)   (0.25–0.78)   (0.40–2.42)   (0.28–0.79)   (0.70–2.02)   
hsa-mir-499 13 1.10 0.376 67.41 1.04 0.850 58.31 1.11 0.410 76.71 1.04 0.829 48.63 0.92 0.418 81.01 
rs3746444 A/G  (0.89–1.37)   (0.71–1.51)   (0.87–1.40)   (0.75–1.43)   (0.74–1.13)   
 Asians 11 1.14 0.264 70.71 1.07 0.779 63.91 1.15 0.315 79.41 1.04 0.861 56.01 0.89 0.367 83.41 
  (0.90–1.45)   (0.67–1.71)   (0.88–1.40)   (0.68–1.57)   (0.70–1.14)   
 Caucasian 0.87 0.448 0.0 1.00 0.993 2.5 0.91 0.613 11.1 1.09 0.632 0.0 1.000 1.000 41.1 
  (0.58–1.29)   (0.65–1.55)   (0.63–1.31)   (0.77–1.54)   (0.80–1.26)   
hsa-mir-149 0.97 0.696 16.6 1.03 0.882 68.21 0.99 0.962 56.61 1.03 0.828 61.11 1.02 0.670 73.41 
rs2292832 C/T  (0.82–1.14)   (0.72–1.47)   (0.77–1.28)   (0.81–1.30)   (0.93–1.12)   
hsa-mir-34b/c 1.19 0.016 52.62 1.15 0.221 20.4 1.25 0.065 58.61 1.06 0.580 0.0 0.87 0.100 54.21 
rs4938723 T/C  (1.03–1.37)   (0.92–1.44)   (0.99–1.58)   (0.86–1.31)   (0.74–1.03)   

The results were in bold, if P<0.05.

1, means the heterogeneity exists and random-effect model based on DerSimonian and Laird method was used, otherwise, a fixed-effect model based on the Mantel–Haenszel method was employed.

2, Pheterogeneity is 0.121 which is higher than 0.10, thus fixed model is used.

3, Pheterogeneity is 0.025 which is lower than 0.10, thus random model is used.

When analyzing the HBV-related HCC risk, we found that hsa-mir-196a-2 rs11614913 was associated with a decreased HBV-related HCC risk in the co-dominant and allelic models (CT compared with CC: P=0.003, OR = 0.75, 95%CI = 0.62–0.91; TT compared with CC: P=0.036, OR = 0.61, 95%CI = 0.39–0.97; T compared with C: P=0.031, OR = 0.80, 95%CI = 0.65–0.98). And hsa-mir-149 rs2292832 was found to be associated with a decreased HBV-related HCC risk in the dominant and recessive models (dominant: P=0.049, OR = 0.28, 95%CI = 0.08–0.99; recessive: P=0.012, OR = 0.28, 95%CI = 0.10–0.75, Table 3 and Figure 3).

Forest plot of ORs for the association of hsa-mir-196a-2 and hsa-mir-149 polymorphism with HCC risks

Figure 3
Forest plot of ORs for the association of hsa-mir-196a-2 and hsa-mir-149 polymorphism with HCC risks

(A) hsa-mir-196a-2 polymorphism stratified by ethnicity in co-dominant model (heterozygote compared with wild-type); (B) hsa-mir-196a-2 polymorphism stratified by ethnicity in co-dominant model (mutation homozygote compared with wild-type); (C) hsa-mir-149 polymorphism in dominant model; (D) hsa-mir-149 polymorphism in recessive model.

Figure 3
Forest plot of ORs for the association of hsa-mir-196a-2 and hsa-mir-149 polymorphism with HCC risks

(A) hsa-mir-196a-2 polymorphism stratified by ethnicity in co-dominant model (heterozygote compared with wild-type); (B) hsa-mir-196a-2 polymorphism stratified by ethnicity in co-dominant model (mutation homozygote compared with wild-type); (C) hsa-mir-149 polymorphism in dominant model; (D) hsa-mir-149 polymorphism in recessive model.

Table 3
Meta-analysis of the association between common SNPs and HBV related-HCC risk
Stratification n Heterozygote compared with wild-type n Mutation homozygote compared with wild-type n Dominant model n Recessive model n Allelic model 
  OR (95%CI) P I2 (%)  OR (95%CI) P I2 (%)  OR (95%CI) P I2 (%)  OR (95%CI) P I2 (%)  OR (95%CI) P I2 (%) 
hsa-mir-146a 1.05 0.627 21.9 0.86 0.178 8.8 0.99 0.950 39.2 0.87 0.066 0.0 0.95 0.281 26.3 
rs2910164 G/C  (0.86–1.28)    (0.69–1.07)    (0.82–1.20)    (0.75–1.01)    (0.86–1.05)   
 Asians 0.97 0.813 0.0 0.85 0.161 24.9 0.92 0.434 24.4 0.87 0.067 0.0 0.93 0.144 12.6 
  (0.78–1.22)    (0.68–1.07)    (0.75–1.13)    (0.75–1.01)    (0.83–1.03)   
 Caucasian 1.46 0.105 NA 1.05 0.930 NA 1.40 0.132 NA 0.91 0.862 NA 1.25 0.232 NA 
  (0.92–2.31)    (0.37–2.94)    (0.90–2.18)    (0.33–2.53)    (0.87–1.80)   
hsa-mir-196a-2 0.75 0.003 9.5 0.61 0.036 62.31 0.86 0.444 76.41 0.86 0.429 70.51 0.80 0.031 60.41 
rs11614913 C/T  (0.62–0.91)    (0.39–0.97)    (0.58–1.27)    (0.58–1.26)    (0.65–0.98)   
 Asians 0.76 0.009 38.1 0.70 0.153 62.31 0.94 0.805 80.51 0.97 0.861 68.51 0.85 0.130 58.01 
  (0.62–0.93)    (0.43–1.14)    (0.59–1.50)    (0.66–1.42)    (0.68–1.05)   
 Caucasian 0.70 0.174 NA 0.35 0.007 NA 0.59 0.034 NA 0.42 0.019 NA 0.61 0.006 NA 
  (0.41–1.17)    (0.16–0.75)    (0.36–0.96)    (0.21–0.87)    (0.43–0.87)   
hsa-mir-499 0.81 0.351 52.41 0.85 0.769 68.11 1.08 0.833 85.61 0.90 0.818 55.51 0.90 0.633 76.11 
rs3746444 A/G  (0.52–1.27)    (0.28–2.56)    (0.55–2.12)    (0.36–2.24)    (0.59–1.38)   
hsa-mir-149 0.37 0.059 88.71 0.14 0.071 95.61 0.28 0.049 93.31 0.28 0.012 91.51 0.38 0.057 96.01 
rs2292832 C/T  (0.13–1.04)    (0.02–1.18)    (0.08–0.99)    (0.10-0.75)    (0.14–1.03)   
Stratification n Heterozygote compared with wild-type n Mutation homozygote compared with wild-type n Dominant model n Recessive model n Allelic model 
  OR (95%CI) P I2 (%)  OR (95%CI) P I2 (%)  OR (95%CI) P I2 (%)  OR (95%CI) P I2 (%)  OR (95%CI) P I2 (%) 
hsa-mir-146a 1.05 0.627 21.9 0.86 0.178 8.8 0.99 0.950 39.2 0.87 0.066 0.0 0.95 0.281 26.3 
rs2910164 G/C  (0.86–1.28)    (0.69–1.07)    (0.82–1.20)    (0.75–1.01)    (0.86–1.05)   
 Asians 0.97 0.813 0.0 0.85 0.161 24.9 0.92 0.434 24.4 0.87 0.067 0.0 0.93 0.144 12.6 
  (0.78–1.22)    (0.68–1.07)    (0.75–1.13)    (0.75–1.01)    (0.83–1.03)   
 Caucasian 1.46 0.105 NA 1.05 0.930 NA 1.40 0.132 NA 0.91 0.862 NA 1.25 0.232 NA 
  (0.92–2.31)    (0.37–2.94)    (0.90–2.18)    (0.33–2.53)    (0.87–1.80)   
hsa-mir-196a-2 0.75 0.003 9.5 0.61 0.036 62.31 0.86 0.444 76.41 0.86 0.429 70.51 0.80 0.031 60.41 
rs11614913 C/T  (0.62–0.91)    (0.39–0.97)    (0.58–1.27)    (0.58–1.26)    (0.65–0.98)   
 Asians 0.76 0.009 38.1 0.70 0.153 62.31 0.94 0.805 80.51 0.97 0.861 68.51 0.85 0.130 58.01 
  (0.62–0.93)    (0.43–1.14)    (0.59–1.50)    (0.66–1.42)    (0.68–1.05)   
 Caucasian 0.70 0.174 NA 0.35 0.007 NA 0.59 0.034 NA 0.42 0.019 NA 0.61 0.006 NA 
  (0.41–1.17)    (0.16–0.75)    (0.36–0.96)    (0.21–0.87)    (0.43–0.87)   
hsa-mir-499 0.81 0.351 52.41 0.85 0.769 68.11 1.08 0.833 85.61 0.90 0.818 55.51 0.90 0.633 76.11 
rs3746444 A/G  (0.52–1.27)    (0.28–2.56)    (0.55–2.12)    (0.36–2.24)    (0.59–1.38)   
hsa-mir-149 0.37 0.059 88.71 0.14 0.071 95.61 0.28 0.049 93.31 0.28 0.012 91.51 0.38 0.057 96.01 
rs2292832 C/T  (0.13–1.04)    (0.02–1.18)    (0.08–0.99)    (0.10-0.75)    (0.14–1.03)   

The results were in bold, if P<0.05.

1, means the heterogeneity exists and random-effect model based on DerSimonian and Laird method was used, otherwise, a fixed-effect model based on the Mantel–Haenszel method was employed.

Other miRNA SNPs and HCC risk

The association of some polymorphisms with HCC risk could not be evaluated because of the limited number of studies (such as hsa-mir-101-1 rs7536540 and hsa-let-7i rs10877887). We reviewed these miRNA SNPs that have been studied for HCC cancer risk (Table 4). These may prove informative in the future study of HCC-associated miRNA polymorphism biomarkers.

Table 4
Other SNPs conferring in the studies of HCC risk
Number hsa-mirNA SNP Results Citation 
hsa-mir-646 rs6513497 The variant allele decreased HCC risk [81
hsa-mir-122 rs4309483 The variant allele increased HCC risk in HBV carriers [48
hsa-mir-378 rs1076064 The variant allele decreased HCC risk in HBV carriers [82
hsa-mir-501 rs112489955 The variant allele decreased HCC risk [47
hsa-mir-608 rs4919510 No association [72
hsa-mirNA3152 rs13299349 The variant allele increased HCC risk [83
hsa-mirNA449b rs10061133 The variant allele increased HCC risk [83
hsa-mir-106b-25 rs999885 The variant genotype increased HCC risk in HBV persistent carriers [84
hsa-mir-199a rs74723057 No association [85
10 hsa-mir-301b rs384262 No association [73
11 hsa-mir-423 rs6505162 No association [74
12 hsa-mir-221 rs17084733 No association [78
13 hsa-mir-1269a rs73239138 The variant allele increased HCC risk [86
Number hsa-mirNA SNP Results Citation 
hsa-mir-646 rs6513497 The variant allele decreased HCC risk [81
hsa-mir-122 rs4309483 The variant allele increased HCC risk in HBV carriers [48
hsa-mir-378 rs1076064 The variant allele decreased HCC risk in HBV carriers [82
hsa-mir-501 rs112489955 The variant allele decreased HCC risk [47
hsa-mir-608 rs4919510 No association [72
hsa-mirNA3152 rs13299349 The variant allele increased HCC risk [83
hsa-mirNA449b rs10061133 The variant allele increased HCC risk [83
hsa-mir-106b-25 rs999885 The variant genotype increased HCC risk in HBV persistent carriers [84
hsa-mir-199a rs74723057 No association [85
10 hsa-mir-301b rs384262 No association [73
11 hsa-mir-423 rs6505162 No association [74
12 hsa-mir-221 rs17084733 No association [78
13 hsa-mir-1269a rs73239138 The variant allele increased HCC risk [86

Heterogeneity

Heterogeneity between studies was observed in Table 2. Some comparisons showed slight or moderate heterogeneity between studies. We subsequently conducted sensitivity analyses by estimating sensitivity before and after removal of each study from the analysis (Supplementary Table S1). The most influencing single study was the study conducted by Han et al. [22] for hsa-mir-34b/c rs4938723. However, sensitivity analysis results ranged from insignificant to statistically significant for the allele comparison because the ORs (95%CI) were 0.87 (0.73–1.03) before removal of the study by Han et al. [22] and 0.79 (0.67–0.92) after removal of that study.

Publication bias

We used Begg’s and Egger’s tests to evaluate the potential publication bias of included studies. For hsa-mir-149 rs2292832, a significant P<0.05 was observed in the three genetic models (Table 5), indicating potential publication bias. As reported, this may be due to language bias, a flawed methodological design for smaller studies or a lack of publication of small trials with opposing results [9].

Table 5
The results of Begg’s and Egger’s tests for the publication bias
Comparison type Begg’s test Egger’s test 
 Z value P-value t value P-value 
hsa-mir-146a rs2910164 G/C 
Heterozygote compared with wild-type −0.64 0.520 0.71 0.490 
Mutation homozygote compared with wild-type 0.05 0.961 −0.47 0.648 
Dominant model −0.54 0.586 0.43 0.673 
Recessive model 1.14 0.255 −1.44 0.173 
Allelic model −0.94 0.347 0.80 0.435 
hsa-mir-196a-2 rs11614913 C/T 
heterozygote compared with wild-type 0.49 0.622 0.38 0.710 
mutation homozygote compared with wild-type −1.15 0.250 1.33 0.209 
Dominant model −0.05 0.956 0.84 0.418 
Recessive model −1.04 0.298 1.30 0.216 
Allelic model 0.60 0.547 −1.08 0.300 
hsa-mir-499 rs3746444 A/G 
Heterozygote compared with wild-type −1.59 0.113 1.78 0.103 
Mutation homozygote compared with wild-type −0.73 0.464 0.17 0.865 
Dominant model −1.22 0.222 1.25 0.237 
Recessive model −0.61 0.542 0.43 0.673 
Allelic model 1.22 0.222 −0.86 0.410 
hsa-mir-149 rs2292832 T/C 
Heterozygote compared with wild-type 0.75 0.453 −1.08 0.331 
Mutation homozygote compared with wild-type 1.95 0.051 −3.08 0.028 
Dominant model 1.05 0.293 −1.26 0.263 
Recessive model 1.65 0.099 −2.80 0.038 
Allelic model −1.95 0.051 2.66 0.045 
hsa-mir-34b/c rs4938723 T/C 
Heterozygote compared with wild-type 1.57 0.117 −1.44 0.387 
Mutation homozygote compared with wild-type 0.52 0.602 −0.21 0.867 
Dominant model 0.52 0.602 −0.99 0.504 
Recessive model 0.52 0.602 −0.04 0.977 
Allelic model −0.52 0.602 0.63 0.641 
Comparison type Begg’s test Egger’s test 
 Z value P-value t value P-value 
hsa-mir-146a rs2910164 G/C 
Heterozygote compared with wild-type −0.64 0.520 0.71 0.490 
Mutation homozygote compared with wild-type 0.05 0.961 −0.47 0.648 
Dominant model −0.54 0.586 0.43 0.673 
Recessive model 1.14 0.255 −1.44 0.173 
Allelic model −0.94 0.347 0.80 0.435 
hsa-mir-196a-2 rs11614913 C/T 
heterozygote compared with wild-type 0.49 0.622 0.38 0.710 
mutation homozygote compared with wild-type −1.15 0.250 1.33 0.209 
Dominant model −0.05 0.956 0.84 0.418 
Recessive model −1.04 0.298 1.30 0.216 
Allelic model 0.60 0.547 −1.08 0.300 
hsa-mir-499 rs3746444 A/G 
Heterozygote compared with wild-type −1.59 0.113 1.78 0.103 
Mutation homozygote compared with wild-type −0.73 0.464 0.17 0.865 
Dominant model −1.22 0.222 1.25 0.237 
Recessive model −0.61 0.542 0.43 0.673 
Allelic model 1.22 0.222 −0.86 0.410 
hsa-mir-149 rs2292832 T/C 
Heterozygote compared with wild-type 0.75 0.453 −1.08 0.331 
Mutation homozygote compared with wild-type 1.95 0.051 −3.08 0.028 
Dominant model 1.05 0.293 −1.26 0.263 
Recessive model 1.65 0.099 −2.80 0.038 
Allelic model −1.95 0.051 2.66 0.045 
hsa-mir-34b/c rs4938723 T/C 
Heterozygote compared with wild-type 1.57 0.117 −1.44 0.387 
Mutation homozygote compared with wild-type 0.52 0.602 −0.21 0.867 
Dominant model 0.52 0.602 −0.99 0.504 
Recessive model 0.52 0.602 −0.04 0.977 
Allelic model −0.52 0.602 0.63 0.641 

The bold numeric means significant as <0.100.

FPRP analyses and trial sequential analysis

We calculated the FPRP values for all observed significant findings in the overall HCC risk. With the assumption of a prior probability of 0.1, the FPRP values in the hsa-mir-146 rs2910164 recessive model for the overall risk and the Asian subgroups, and in the hsa-mir-196a-2 rs11614913 recessive model for the Caucasian subgroup were all <0.20, suggesting that these significant associations were noteworthy (Table 6).

Table 6
FPRP values for the associations between hsa-miRNA polymorphisms and HCC risk
Variables OR (95%CI) P1 Power2 Prior probability 
    0.25 0.1 0.01 0.001 0.0001 
hsa-mir-146 rs2910164         
  Recessive model         
  Overall 0.90 (0.83–0.98) 0.017 0.888 0.054 0.147 0.655 0.950 0.995 
  Asians 0.90 (0.83–0.98) 0.017 0.870 0.055 0.150 0.659 0.951 0.995 
hsa-mir-196a-2 rs11614913         
  Mutation homozygote compared with wild-type         
  Caucasian 0.44 (0.25–0.78) 0.005 0.152 0.090 0.228 0.765 0.970 0.997 
  Recessive model         
  Caucasian 0.47 (0.28–0.79) 0.005 0.726 0.020 0.058 0.405 0.873 0.986 
hsa-mir-34b/c rs4938723         
  Heterozygote compared with wild-type         
  Overall 1.19 (1.03–1.37) 0.016 0.353 0.120 0.290 0.818 0.978 0.998 
Variables OR (95%CI) P1 Power2 Prior probability 
    0.25 0.1 0.01 0.001 0.0001 
hsa-mir-146 rs2910164         
  Recessive model         
  Overall 0.90 (0.83–0.98) 0.017 0.888 0.054 0.147 0.655 0.950 0.995 
  Asians 0.90 (0.83–0.98) 0.017 0.870 0.055 0.150 0.659 0.951 0.995 
hsa-mir-196a-2 rs11614913         
  Mutation homozygote compared with wild-type         
  Caucasian 0.44 (0.25–0.78) 0.005 0.152 0.090 0.228 0.765 0.970 0.997 
  Recessive model         
  Caucasian 0.47 (0.28–0.79) 0.005 0.726 0.020 0.058 0.405 0.873 0.986 
hsa-mir-34b/c rs4938723         
  Heterozygote compared with wild-type         
  Overall 1.19 (1.03–1.37) 0.016 0.353 0.120 0.290 0.818 0.978 0.998 

PB, source of controls is population-based.

1

Chi-square test was adopted to calculate the genotype frequency distributions.

2

Statistical power was calculated using the number of observations in the subgroup and the OR and P-values in this table.

The bold numeric values were considered significant as <0.20.

Amongst the positive results we found, the recessive model for hsa-mir-146a was adopted for the trial sequential analysis to strengthen the robustness of our findings. According to TSA result, the required information size was 15021 subjects to demonstrate the issue (Figure 4). Until now, the cumulative z-curve has not crossed the trial monitoring boundary before reaching the required information size, indicating that the cumulative evidence is insufficient and further trials are necessary.

The required information size to demonstrate the relevance of hsa-mir-146a polymorphism with risk of HCC (recessive model)

Discussion

Until now, there was only one similar meta-analysis published [23] and we had many advantages than theirs. First, the latest update date, we searched until 23 February 2018 and there were 37 studies included in this meta-analysis. Second, we considered the available data for the HBV-related HCC risk and supplied more promising SNP sites for the precaution of HBV-related HCC risk. Third, we listed all the genotypes of the case and control groups and considered the P-value of HWE. There existed two problems for the research state quo: in the studying field of miRNA polymorphisms, (i) the major genotype has not the more frequencies than the minor one, which made the meta results negative. For example, hsa-mir-149 A>G SNP was reported as 13, 36, 139 for AA, AG, GG genotype by Chu et al. [24] and as 210, 49, 12 for AA, AG, GG genotype by Kou et al. [25], while the genotyping method for them was the same. Here, we suppose the reasons for this phenomenon are the geographical and ethnicity cause and the unstable genotyping method. (ii) The Hardy–Weinberg principle was a basic law for the genetic studies. We found several studies did not mention HWE when the PHWE<0.05. In our meta-analysis, we checked the P-value of HWE in the control group and if PHWE<0.05, the SNP should be discarded in further analysis. In addition, we followed main directions from the guidelines for the miRNA terminology [26].

The position of miR-SNPs included pri-, pre-, and/or mature miRNA, and the function of the miR-SNPs depended on its position [27]. The pre-miR-SNPs included hsa-mir-146a rs2910164, hsa-mir-196a-2 rs11614913, hsa-mir-499 rs3746444, hsa-mir-149 rs2292832, and hsa-mir-27a rs895819. Others were all pri-miR-SNPs.

In this disordered reported circumstance, we still found hsa-mir-146a rs2910164 and hsa-mir-34b/c rs4938723 had potential to be biomarkers for the HCC risk in these five common miR-SNPs. First, we found hsa-mir-146a rs2910164 was associated with a decreased risk of HCC. The mature hsa-mir-146a could function for cancer cell proliferation, apoptosis, invasion, and metastasis [28–31]. miR-SNP rs2910164 is a G to C variation located at the +4 base of the passenger strand of hsa-mir-146a-3p. In addition, this SNP decreases the minimum free energy (MFE) from −41.80 kcal/mol for the G allele to −38.80 kcal/mol for the C allele, suggesting a less stable secondary structure for the variant C allele. Jazdzewski et al. [32] reported that the variant (C) genotype shows lower levels of the oncogeneic hsa-mir-146a expression, all the above may be the reasons the variant C had a protective role for HCC risk. Second, we found that hsa-mir-34b/c rs4938723 was associated with an increased HCC risk. This rs4938723 located within the typical CpG island region of pri-hsa-mir-34b/c, and methylation of hsa-mir-34b/c CpG islands were reported to be associated with several cancers [33–35]. The T→C variation of this polymorphism has been predicted to create a GATA-binding site and could affect the transcription factor GATA activity and further affect the mature hsa-mir-34b/c expression [36], which may be the reason for the rs4938723 associated with HCC risk.

The etiology of HBV-related HCC was not caused by one particular driver mutation but involved several oncogenic pathways [37,38]. It included TP53 pathway [39], Wnt signaling [37], cell cycle [40,41], oxidative stress [39,42], epigenetic regulator [40], and so on. Thus, many miRNAs play important role for these oncogenic pathways in HBV-related HCC [43,44]. We found in this meta-analysis, hsa-mir-196a-2 rs11614913 and hsa-mir-149 rs2292832 were associated with decreased HBV-related HCC risks. However, there is no report about the hsa-mir-196a-2 and hsa-mir-149 involved in the process of HBV-related HCC. Some other miRNAs like hsa-mir-125 were found to be associated with HBV-related HCC [45]. The results we found could be a clue for the particular miRNA involved in the pathogenic process and it also need to be verified in the future studies.

Some promising miR-SNPs were summarized in Table 5. Several SNPs were associated with HCC risk and related functional studies were also reported. For example, Long et al. [46] screened 48 pre-miRNA SNPs and found only hsa-mir-1268a rs28599926 affected HCC risk. And this polymorphism was associated not only with higher portal vein tumor risk and tumor dedifferentiation, but also with increasing the mutation risk of TP53 gene and modifying the targetted ADAMTS4 gene expression [46]. Several miR-SNPs were also found to affect the miRNA or gene expression, like hsa-mir-501 SNP and hsa-mir-122 SNP [47,48]. These are all the potential functional polymorphism biomarkers for the future HCC studies.

Advantages and limitations

This meta-analysis still had several limitations. First, only studies written in English and Chinese were searched in our analysis, while reports in other languages or some other ongoing studies were not available. Second, the pooled sample size was relatively limited and thus limited for the subgroup analysis. More studies are still required to pool together to make the analysis more reliable.

Summary and future directions

In summary, we found hsa-mir-146a rs2910164 was associated with a decreased HCC risk in the recessive model. While hsa-mir-34b/c rs4938723 was related with an increased HCC risk in the co-dominant. When analyzing the HBV-related HCC risk, hsa-mir-196a-2 rs11614913 was associated with a decreased HBV-related HCC risk in the co-dominant and allelic models, and hsa-mir-149 rs2292832 was found to be associated with a decreased HBV-related HCC risk in the dominant and recessive models. In conclusion, hsa-mir-146a rs2910164 and hsa-mir-34b/c rs4938723 could be biomarkers for the HCC risk while hsa-mir-196a-2 rs11614913 and hsa-mir-149 rs2292832 had potential to be biomarkers for HBV-related HCC risk.

Competing interests

The authors declare that there are no competing interests associated with the manuscript.

Author contribution

Q.X. designed the present study. B.-g.W. and Q.X. extracted the data and analyzed the data. B.-g.W. and L.-y.J. wrote the manuscript. Q.X. revised the manuscript.

Funding

This work was supported partly by the Natural Science Foundation of Liaoning Province in China [grant number 20170541001]; and the Fund for Scientific Research of The First Hospital of China Medical University [grant number FSFH201713].

Abbreviations

     
  • CI

    confidence interval

  •  
  • FPRP

    false-positive report probability

  •  
  • HBV

    Hepatitis B virus

  •  
  • HCC

    hepatocellular cancer

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • OR

    odds ratio

  •  
  • SNP

    single nucleotide polymorphism

  •  
  • pri-miRNA

    primary-microRNA

  •  
  • pre-miRNA

    precursor-microRNA

  •  
  • TSA

    trial sequential analysis

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Supplementary data