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.
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
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 . | . | . |
1 | 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] |
2 | Hikmet Akkız | 2011 | Turkish | Caucasian | HB | PCR-RFLP | hsa-mir-499 | 222 | 222 | 45 | 87 | 90 | 47 | 93 | 82 | 0.950 | [50] | |||
3 | Hikmet Akkız | 2011 | Turkish | Caucasian | HB | PCR-RFLP | hsa-mir-146a | 222 | 222 | 137 | 75 | 10 | 144 | 67 | 11 | 75 | 51 | 6 | 0.384 | [51] |
4 | 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 | 9 | 281 | 55 | 1 | 46 | 27 | 0.321 | ||||||||
Real-time PCR | hsa-mir-149 | 188 | 337 | 13 | 36 | 139 | 27 | 64 | 246 | 19 | 54 | <0.001 | ||||||||
5 | 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] |
6 | 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 | |||||||||||
7 | 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 | 3 | 120 | 74 | 7 | 91 | 34 | 2 | 0.278 | ||||||||
hsa-mir-149 | 159 | 201 | 14 | 64 | 81 | 21 | 97 | 83 | 68 | 49 | 10 | 0.345 | ||||||||
8 | 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 | |||||||||||
9 | 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 | 7 | 123 | 48 | 14 | 54 | 14 | 3 | 0.120 | ||||||||
15 | Eman A. Toraih | 2016 | Egypt | Caucasian | PB | Real-time PCR | hsa-mir-196a-2 | 60 | 150 | 25 | 32 | 3 | 80 | 53 | 17 | 0.082 | [61] | |||
hsa-mir-499 | 60 | 150 | 28 | 23 | 9 | 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 | 7 | 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 | 4 | 371 | 100 | 12 | 0.100 | |||||||||||
25 | Hong-Zhi Zou | 2013 | China | Asian | HB | PCR-RFLP | hsa-mir-499 | 185 | 204 | 136 | 44 | 5 | 139 | 52 | 13 | 54 | 14 | 3 | 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 | 0 | 165 | 149 | 3 | 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 | 2 | 301 | 101 | 4 | 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 | 9 | 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 | 1 | 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 . | . | . |
1 | 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] |
2 | Hikmet Akkız | 2011 | Turkish | Caucasian | HB | PCR-RFLP | hsa-mir-499 | 222 | 222 | 45 | 87 | 90 | 47 | 93 | 82 | 0.950 | [50] | |||
3 | Hikmet Akkız | 2011 | Turkish | Caucasian | HB | PCR-RFLP | hsa-mir-146a | 222 | 222 | 137 | 75 | 10 | 144 | 67 | 11 | 75 | 51 | 6 | 0.384 | [51] |
4 | 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 | 9 | 281 | 55 | 1 | 46 | 27 | 0.321 | ||||||||
Real-time PCR | hsa-mir-149 | 188 | 337 | 13 | 36 | 139 | 27 | 64 | 246 | 19 | 54 | <0.001 | ||||||||
5 | 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] |
6 | 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 | |||||||||||
7 | 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 | 3 | 120 | 74 | 7 | 91 | 34 | 2 | 0.278 | ||||||||
hsa-mir-149 | 159 | 201 | 14 | 64 | 81 | 21 | 97 | 83 | 68 | 49 | 10 | 0.345 | ||||||||
8 | 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 | |||||||||||
9 | 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 | 7 | 123 | 48 | 14 | 54 | 14 | 3 | 0.120 | ||||||||
15 | Eman A. Toraih | 2016 | Egypt | Caucasian | PB | Real-time PCR | hsa-mir-196a-2 | 60 | 150 | 25 | 32 | 3 | 80 | 53 | 17 | 0.082 | [61] | |||
hsa-mir-499 | 60 | 150 | 28 | 23 | 9 | 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 | 7 | 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 | 4 | 371 | 100 | 12 | 0.100 | |||||||||||
25 | Hong-Zhi Zou | 2013 | China | Asian | HB | PCR-RFLP | hsa-mir-499 | 185 | 204 | 136 | 44 | 5 | 139 | 52 | 13 | 54 | 14 | 3 | 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 | 0 | 165 | 149 | 3 | 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 | 2 | 301 | 101 | 4 | 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 | 9 | 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 | 1 | 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
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 | 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 | 2 | 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 | 2 | 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 | 7 | 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 | 3 | 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 | 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 | 2 | 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 | 2 | 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 | 7 | 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 | 3 | 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
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 | 6 | 1.05 | 0.627 | 21.9 | 6 | 0.86 | 0.178 | 8.8 | 6 | 0.99 | 0.950 | 39.2 | 7 | 0.87 | 0.066 | 0.0 | 7 | 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 | 5 | 0.97 | 0.813 | 0.0 | 5 | 0.85 | 0.161 | 24.9 | 5 | 0.92 | 0.434 | 24.4 | 6 | 0.87 | 0.067 | 0.0 | 6 | 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 | 1.46 | 0.105 | NA | 1 | 1.05 | 0.930 | NA | 1 | 1.40 | 0.132 | NA | 1 | 0.91 | 0.862 | NA | 1 | 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 | 4 | 0.75 | 0.003 | 9.5 | 4 | 0.61 | 0.036 | 62.31 | 5 | 0.86 | 0.444 | 76.41 | 5 | 0.86 | 0.429 | 70.51 | 4 | 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 | 3 | 0.76 | 0.009 | 38.1 | 3 | 0.70 | 0.153 | 62.31 | 4 | 0.94 | 0.805 | 80.51 | 4 | 0.97 | 0.861 | 68.51 | 3 | 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 | 1 | 0.70 | 0.174 | NA | 1 | 0.35 | 0.007 | NA | 1 | 0.59 | 0.034 | NA | 1 | 0.42 | 0.019 | NA | 1 | 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 | 4 | 0.81 | 0.351 | 52.41 | 4 | 0.85 | 0.769 | 68.11 | 5 | 1.08 | 0.833 | 85.61 | 4 | 0.90 | 0.818 | 55.51 | 5 | 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 | 3 | 0.37 | 0.059 | 88.71 | 3 | 0.14 | 0.071 | 95.61 | 3 | 0.28 | 0.049 | 93.31 | 4 | 0.28 | 0.012 | 91.51 | 3 | 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 | 6 | 1.05 | 0.627 | 21.9 | 6 | 0.86 | 0.178 | 8.8 | 6 | 0.99 | 0.950 | 39.2 | 7 | 0.87 | 0.066 | 0.0 | 7 | 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 | 5 | 0.97 | 0.813 | 0.0 | 5 | 0.85 | 0.161 | 24.9 | 5 | 0.92 | 0.434 | 24.4 | 6 | 0.87 | 0.067 | 0.0 | 6 | 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 | 1.46 | 0.105 | NA | 1 | 1.05 | 0.930 | NA | 1 | 1.40 | 0.132 | NA | 1 | 0.91 | 0.862 | NA | 1 | 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 | 4 | 0.75 | 0.003 | 9.5 | 4 | 0.61 | 0.036 | 62.31 | 5 | 0.86 | 0.444 | 76.41 | 5 | 0.86 | 0.429 | 70.51 | 4 | 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 | 3 | 0.76 | 0.009 | 38.1 | 3 | 0.70 | 0.153 | 62.31 | 4 | 0.94 | 0.805 | 80.51 | 4 | 0.97 | 0.861 | 68.51 | 3 | 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 | 1 | 0.70 | 0.174 | NA | 1 | 0.35 | 0.007 | NA | 1 | 0.59 | 0.034 | NA | 1 | 0.42 | 0.019 | NA | 1 | 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 | 4 | 0.81 | 0.351 | 52.41 | 4 | 0.85 | 0.769 | 68.11 | 5 | 1.08 | 0.833 | 85.61 | 4 | 0.90 | 0.818 | 55.51 | 5 | 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 | 3 | 0.37 | 0.059 | 88.71 | 3 | 0.14 | 0.071 | 95.61 | 3 | 0.28 | 0.049 | 93.31 | 4 | 0.28 | 0.012 | 91.51 | 3 | 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.
Number . | hsa-mirNA . | SNP . | Results . | Citation . |
---|---|---|---|---|
1 | hsa-mir-646 | rs6513497 | The variant allele decreased HCC risk | [81] |
2 | hsa-mir-122 | rs4309483 | The variant allele increased HCC risk in HBV carriers | [48] |
3 | hsa-mir-378 | rs1076064 | The variant allele decreased HCC risk in HBV carriers | [82] |
4 | hsa-mir-501 | rs112489955 | The variant allele decreased HCC risk | [47] |
5 | hsa-mir-608 | rs4919510 | No association | [72] |
6 | hsa-mirNA3152 | rs13299349 | The variant allele increased HCC risk | [83] |
7 | hsa-mirNA449b | rs10061133 | The variant allele increased HCC risk | [83] |
8 | hsa-mir-106b-25 | rs999885 | The variant genotype increased HCC risk in HBV persistent carriers | [84] |
9 | 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 . |
---|---|---|---|---|
1 | hsa-mir-646 | rs6513497 | The variant allele decreased HCC risk | [81] |
2 | hsa-mir-122 | rs4309483 | The variant allele increased HCC risk in HBV carriers | [48] |
3 | hsa-mir-378 | rs1076064 | The variant allele decreased HCC risk in HBV carriers | [82] |
4 | hsa-mir-501 | rs112489955 | The variant allele decreased HCC risk | [47] |
5 | hsa-mir-608 | rs4919510 | No association | [72] |
6 | hsa-mirNA3152 | rs13299349 | The variant allele increased HCC risk | [83] |
7 | hsa-mirNA449b | rs10061133 | The variant allele increased HCC risk | [83] |
8 | hsa-mir-106b-25 | rs999885 | The variant genotype increased HCC risk in HBV persistent carriers | [84] |
9 | 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].
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).
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.
Chi-square test was adopted to calculate the genotype frequency distributions.
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].