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. 2018 Mar 1;11:1121–1139. doi: 10.2147/OTT.S154211

rs11614913 polymorphism in miRNA-196a2 and cancer risk: an updated meta-analysis

Yuhan Liu 1,*, Anbang He 1,2,*, Baoer Liu 1, Yucheng Zhong 1, Xinhui Liao 1, Jiangeng Yang 1, Jieqing Chen 1, Jianting Wu 1, Hongbing Mei 1,✉
PMCID: PMC5840307  PMID: 29535537

Abstract

Several epidemiological studies have reported that polymorphisms in microRNA-196a2 (miR-196a2) were associated with various cancers. However, the results remained unverified and were inconsistent in different cancers. Therefore, we carried out an updated meta-analysis to elaborate the effects of rs11614913 polymorphism on cancer susceptibility. A total of 84 articles with 35,802 cases and 41,541 controls were included to evaluate the association between the miR-196a2 rs11614913 and cancer risk by pooled odds ratios (ORs) and 95% confidence intervals (CIs). The results showed that miR-196a2 rs11614913 polymorphism is associated with cancer susceptibility, especially in lung cancer (homozygote comparison, OR =0.840, 95% CI =0.734–0.961; recessive model, OR =0.858, 95% CI =0.771–0.955), hepatocellular carcinoma (allelic contrast, OR =0.894, 95% CI =0.800–0.998; homozygote comparison, OR =0.900, 95% CI =0.813–0.997; recessive model, OR =0.800, 95% CI =0.678–0.944), and head and neck cancer (allelic contrast, OR =1.076, 95% CI =1.006–1.152; homozygote comparison, OR =1.214, 95% CI =1.043–1.413). In addition, significant association was found among Asian populations (allele model, OR =0.847, 95% CI =0.899–0.997, P=0.038; homozygote model, OR =0.878, 95% CI =0.788–0.977, P=0.017; recessive model, OR =0.895, 95% CI =0.824–0.972, P=0.008) but not in Caucasians. The updated meta-analysis confirmed the previous results that miR-196a2 rs11614913 polymorphism may serve as a risk factor for patients with cancers.

Keywords: miR-196a2, polymorphisms, cancer risk, meta-analysis

Introduction

The rising morbidity and mortality of cancer has drawn extensive attention worldwide, and finding possible risk factors of tumorigenesis has been a priority task for researchers. Recently, an increasing number of studies have focused on associations between miRNA polymorphisms and cancer susceptibility, which indicated that accumulation of genetic variants may be involved in cancer development, including oral cancer,1 lung cancer,2,3 gastric cancer,4 breast cancer,5 glioma,6 non-small cell lung cancer,7 hepatocellular carcinoma,8,9 gallbladder cancer,10 and head and neck cancer (HNC).11 As the molecular mechanism of cancer remains unclear, further exploration of more accurate cancer treatments and prognosis would be of great importance.

MiRNAs are a class of small non-coding RNAs with 18–25 nucleotides in length, which play as oncogenes or anti-oncogenes in the pathogenesis of tumor by targeting multiple genes.12–14 Studies have shown that almost 10%–30% of all human gene expressions have been regulated by mature miRNAs.15 MiRNAs could modulate related genes implicated in cellular processes, including cell differentiation, growth, apoptosis, and immune response.16–18

Hsa-microRNA-196a2 (miR-196a2), initially discovered by Lagos-Quintana et al,19 has been proven to play important roles in various cancers.20,21 Single nucleotide polymorphisms (SNPs) provide new sources of genetic variation, which contribute to potential molecular mechanisms of cancer development.22 SNPs or mutations in miRNA sequence may transform miRNA expression and/or maturation, related to miRNA function by activating the transcription of the primary transcript, pri-miRNA and pre-miRNA processing, and miRNA–mRNA interactions.23 MiR-196a2 rs11614913, as a definitional miRNA polymorphism,24–26 is crucially associated with cancer risk.23,27 It is located in the 3′-untranslated region of the miR-196a2 precursor.28 Hoffman et al5 also showed that miR-196a2 rs11614913 not only influenced the transcription level of mature miR-196a, but also had a biological effect on target gene production. This updated meta-analysis was performed to explore the association between the hsa-miR-196a2 polymorphism and cancer risk and to further estimate the overall cancer risk by pooling all available data.

Materials and methods

Publication search

Two investigators (LYH, HAB) carried out a systematic review on PubMed, Cochrane Library, and Web of Science, by using (“microRNA-196a2” or “miR-196a2”, or “miR-196-a-2” or “miR-196-2” or “miR-196-a” or “rs11614913”), and (“cancer” or “tumor” or “carcinoma” or “neoplasm” or “malignancy”), and (“polymorphism” or “variation” or “susceptibility”) as the search terms in order to identify potentially eligible studies. We based our dates for literature retrieval from January 2008 to September 2017.

Inclusion and exclusion criteria

Relevant studies had to meet the following inclusion criteria: 1) full-text article; 2) evaluation of a link between miRNA polymorphisms and cancer risks; 3) sufficient data for estimating the odds ratio (OR) with 95% CI and a P-value. Studies containing two or more case-control groups were considered as two or more independent studies. Studies that were, 1) review, letters, and comment articles; 2) not for cancer risk; and 3) duplicate samples or publications, were excluded.

Assessment of study quality

The quality of the study was determined by the Newcastle–Ottawa Scale for cohort studies.

Data extraction

Data extraction from the eligible studies were performed independently by two authors (LYH, HAB), based on the inclusion and exclusion criteria. For each publication, the following data were recorded: first author, date of publication, country of origin, ethnicity, type of tumor, source of control groups, total numbers of cases and controls, and genotyping method.

Statistical analysis

The departure of frequencies of miR-196a2 rs11614913 polymorphisms was assessed under the Hardy–Weinberg equilibrium (HWE) for each publication by adopting the goodness-of-fit test (chi-square or Fisher exact test). The association between the miR-196a2 rs11614913 polymorphisms and the risk of cancer was evaluated by calculating pooled OR together with corresponding 95% CI based on the method published by Woolf.29 Also, a P-value<0.05 was considered statistically significant. In addition, we used stratified meta-regression analyses to explore major causes of heterogeneity among the articles. We respectively examined the association between genetic mutants and cancer risk in allelic contrast (T vs C), homozygote comparisons (TT vs CC), heterozygote comparisons (TC vs CC), recessive model (TT vs TC+CC), and dominant model (TT+TC vs CC). Subgroup analyses were performed by ethnicity (Asian and Caucasian), tumor types (if one tumor type contained less than three individual studies, it was combined into “other cancer” subgroups), and source of control (hospital based and population based).

Q tests30 and I2 tests31 were carried out to test the heterogeneity. I2 values describe the percentage of total variation across studies that are due to heterogeneity rather than chance. I2=0% prompts no heterogeneity observed, with 25% identified as low, 50% as moderate, and 75% as high. If I2 was ≥50% or if the P-value of heterogeneity was <0.05, indicating significant heterogeneity among these articles, a random-effect model was used;32 otherwise, a fixed-effect mode was used.33 Sensitivity analyses were conducted to estimate the stability of the meta-analysis result. We adopted Egger’s test to assess potential publication bias by visual inspection of the Funnel plot. A P-value <0.05 was regarded as an indication of potential publication bias.34 All statistical analyses were performed with the Stata software package version 12.0 (Stata Corporation, College Station, TX, USA).

Results

Study identification

Overall, 84 articles,1–11,26,27,35–100 which were relevant to the search terms, were selected based on the inclusion criteria from PubMed, Cochrane, and Web of Science (Figure 1). These studies with a total of 35,802 cases and 41,541 controls were subjected to further checking. In the present meta-analysis, we excluded 73 articles (36 articles were meta-analysis, 22 articles did not express concern about cancer risk, 11 articles lacked detailed allele frequency data or OR calculation, and four articles were incomplete text). The included study characteristics are provided in Table 1.

Figure 1.

Figure 1

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The flow diagram of the included and excluded studies.

Table 1.

Characteristics of studies included in the meta-analysis

Author Year Country Ethnicity Cancer type Genotyping method Source of control Case
Control
HWE
TT CT CC TT CT CC
Hu et al7 2008 China Asian LC PCR PB 152 264 140 32 52 23 0.827
Hu et al35 2009 China Asian BRC PCR-RFLP PB 287 483 239 358 517 218 0.207
Tian et al3 2009 China Asian LC PCR-RFLP PB 293 512 253 307 519 209 0.700
Hoffman et al5 2009 USA Caucasian BRC TaqMan HB 71 229 166 36 209 181 0.583
Catucci et al36 2010 Italy Caucasian BRC TaqMan PB 244 842 776 377 1,246 1,116 0.326
Wang et al38 2010 China Asian ESCC PCR PB 48 262 148 111 250 128 0.600
Okubo et al83 2010 Japan Asian GC Gel Pictures HB 166 281 105 372 592 216 0.466
Peng et al4 2010 China Asian GC PCR-RFLP PB 43 94 76 50 107 56 0.936
Srivastava et al10 2010 India Asian GLC PCR-RFLP PB 121 97 21 121 94 15 0.566
Dou et al6 2010 China Asian Glioma PCR-LDR HB 189 343 111 208 305 143 0.119
Li et al9 2010 China Asian HCC PCR-RFLP HB 82 150 78 78 102 42 0.402
Akkiz et al8 2010 Turkey Caucasian HCC PCR-RFLP HB 22 86 77 40 87 58 0.492
Liu et al11 2010 USA Caucasian HNC PCR-RFLP PB 194 565 350 202 545 383 0.737
Kim et al110 2010 Korea Asian LC PCR-RFLP HB 162 305 187 185 300 155 0.126
Catucci et al36 2010 Germany Caucasian BRC MassARRAY PB 216 696 584 157 512 432 0.711
Christensen et al37 2010 USA Caucasian HNC AppliedBiosystems PB 0 302 182 0 367 188 NA
Mittal et al41 2011 India Asian BLC PCR-RFLP PB 5 131 76 14 127 109 0.003
Jedlinski et al40 2011 Australia Caucasian BRC PCR PB 33 86 68 31 82 58 0.830
Zhan et al42 2011 China Asian CRC PCR-RFLP HB 56 128 68 163 267 113 0.849
Zhou et al43 2011 China Asian CSCC PCR-RFLP PB 57 123 46 82 169 58 0.077
Vinci et al111 2011 Italy Caucasian LC TaqMan PB 12 54 35 10 61 58 0.267
Hong et al2 2011 Korea Asian LC TaqMan HB 96 224 86 134 198 96 0.163
George et al39 2011 Italy Caucasian PC PCR-RFLP PB 3 101 55 10 114 106 0.002
Linhares et al45 2012 Brazil Mix BRC TaqMan HB 117 177 94 96 165 127 0.005
Chen et al44 2012 China Asian CRC PCR-LDR HB 35 64 27 107 206 94 0.788
Min et al24 2012 Korea Asian CRC PCR-RFLP HB 125 201 120 148 254 100 0.633
Zhu et al47 2012 China Asian CRC TaqMan HB 130 303 140 172 295 121 0.790
Hezova et al25 2012 Czech Caucasian CRC TaqMan HB 26 89 82 22 103 87 0.291
Zhang et al100 2012 China Asian CRC PCR-RFLP PB 172 204 79 185 197 81 0.026
Ahn et al48 2013 Korea Asian GC PCR-RFLP PB 119 242 100 128 232 87 0.322
Yoon et al46 2012 Korea Asian LC TaqMan PB 99 186 101 24 32 15 0.480
Zhang et al104 2012 China Asian BRC PCR-RFLP PB 133 93 17 148 89 11 0.893
Chu et al87 2012 China Asian HNC PCR-RFLP HB 136 277 57 132 206 87 0.690
Vinci et al113 2013 Italy Caucasian CRC HRMA HB 12 86 62 11 84 83 0.087
Lv et al51 2013 China Asian CRC PCR-RFLP PB 114 223 10 91 331 109 0.000
Umar et al112 2013 India Asian ESCC PCR-RFLP HB 22 121 146 16 122 171 0.330
Wei et al114 2013 China Asian ESCC SNPscanTM HB 106 196 65 113 170 87 0.141
Toraih et al98 2016 Egypt Caucasian OSCC PCR PB 32 93 84 10 35 55 0.221
Wang et al53 2013 China Asian GC TaqMan HB 226 371 152 232 448 220 0.898
Zhang et al55 2013 China Asian HCC MassARRAY HB 294 488 214 328 502 165 0.245
Han et al49 2013 China Asian HCC PCR PB 305 505 207 304 485 220 0.310
Tong et al65 2013 China Asian ALL TaqMan HB 159 308 103 237 307 129 0.434
Pavlakis et al93 2013 Greece Caucasian PCC PCR-RFLP HB 48 33 12 50 58 14 0.647
Pu et al84 2014 China Asian GC PCR-RFLP HB 25 95 39 86 324 101 0.000
Bansal et al56 2014 India Asian BRC PCR-RFLP PB 12 41 68 21 59 85 0.042
Kupcinskas et al62 2014 Lithuania Caucasian CRC PCR HB 27 87 79 54 174 199 0.104
Qu et al64 2014 China Asian ESCC PCR PB 48 207 126 82 211 133 0.918
Wang et al66 2014 China Asian ESCC PCR-LDR PB 162 307 128 154 298 145 0.970
Dikeakos et al58 2014 Greece Caucasian GC PCR-RFLP HB 15 46 102 172 229 79 0.850
Qi et al86 2014 China Asian HCC PCR HB 60 209 45 121 214 71 0.156
Chu et al57 2014 China Asian HCC PCR-RFLP HB 66 81 41 100 167 70 0.986
Parlayan et al115 2014 Japan Asian LC TaqMan HB 38 81 29 146 270 108 0.410
Li et al63 2014 China Asian NPC TaqMan HB 322 489 209 270 518 218 0.301
Du et al59,60 2014 China Asian RCC PCR HB 121 189 43 109 179 74 0.974
Omrani et al85 2014 Iran Asian BRC PCR-RFLP PB 0 25 78 0 18 218 NA
Kou et al91 2014 China Asian HCC PCR HB 37 150 84 103 304 125 0.001
Roy et al94 2014 India Asian HNC AppliedBiosystems HB 46 187 218 38 168 242 0.250
Li et al63 2014 China Asian HNC AppliedBiosystems PB 322 489 209 270 518 218 0.300
Deng et al67 2015 China Asian BLC PCR-RFLP PB 52 66 41 76 166 56 0.040
Qi et al72 2015 China Asian BRC PCR PB 168 119 34 185 88 17 0.141
Dikaiakos et al68 2015 Greece Caucasian CRC PCR-RFLP PB 69 69 19 117 149 33 0.156
Li et al69 2015 China Asian HCC PCR HB 51 131 84 30 123 113 0.689
Li et al69 2015 China Asian NHL PCR-RFLP PB 111 146 61 144 134 42 0.225
Nikolic et al71 2015 Serbia Caucasian PC PCR-RFLP PB 40 161 150 41 147 121 0.728
He et al90 2015 China Asian BRC MassARRAY HB 134 223 93 136 233 81 0.990
Sushma et al97 2015 India Asian OSCC PCR-RFLP PB 68 10 22 81 15 6 0.212
Sodhi et al95 2015 India Asian LC PCR-RFLP PB 19 161 70 8 146 101 0.000
Jiang et al26 2016 China Asian GC PCR HB 300 423 166 290 487 198 0.804
Dai et al74 2016 China Asian BRC MassARRAY HB 98 265 197 144 284 155 0.540
Zhao et al82 2016 China Asian BRC TaqMan PB 33 50 31 25 61 28 0.449
Song et al79 2016 China Asian OC PCR PB 111 247 121 142 203 86 0.385
Shen et al78 2016 China Asian ESCC SNaPshot PB 407 698 295 672 1,121 392 0.043
Li et al75 2016 China Asian GC PCR HB 75 83 24 92 79 11 0.265
Li et al76 2016 China Asian HCC PCR HB 20 64 25 35 52 18 0.861
Xu et al80 2016 China Asian HCC PCR-RFLP HB 56 128 68 163 267 113 0.849
Qiu and Liu77 2016 China Asian HCC PCR PB 61 141 68 70 121 46 0.626
Jiang et al26 2016 China Asian HCC TaqMan PB 159 308 103 237 307 129 0.099
Yin et al81 2016 China Asian LC TaqMan PB 149 298 128 178 297 133 0.664
Zhang et al99 2016 China Asian HCC PCR-RFLP HB 65 85 25 122 138 42 0.770
Sun et al96 2016 China Asian OC PCR HB 39 66 29 77 116 34 0.360
Toraih et al98 2016 Egypt Caucasian HCC PCR PB 11 31 23 17 53 80 0.082
Morales et al92 2016 Chile Mix BRC TaqMan HB 57 191 192 114 351 342 0.121
Gu and Tu88 2016 China Asian GC PCR HB 51 96 39 31 98 57 0.310
Hashemi et al89 2016 Iran Asian GC PCR-RFLP PB 17 88 64 12 93 77 0.021
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Abbreviations: ALL, acute lymphoblastic leukemia; BLC, bladder cancer; BRC, breast cancer; CRC, colorectal cancer; CSCC, cervical cancer; ESCC, esophageal squamous cell carcinoma; GC, gastric cancer; GLC, gallbladder cancer; HB, hospital based; HCC, hepatocellular carcinoma; HNC, head and neck cancer; HRMA, high-resolution melting analysis; HWE, Hardy–Weinberg equilibrium of controls; LC, lung cancer; NHL, non-Hodgkin lymphoma; NPC, nasopharyngeal carcinoma; NA, not available; OC, ovarian cancer; OSCC, oral squamous cell carcinomas; PB, population based; PC, prostate cancer; PCC, pancreatic cancer; PCR, polymerase chain reaction; PCR-LDR, polymerase chain reaction-ligation detection reaction; PCR-RFLP, polymerase chain reaction restriction fragment length polymorphism; RCC, renal cell carcinoma.

In total, there were studies on hepatocellular carcinoma (n=14), breast cancer (n=14), colorectal cancer (n=10), gastric cancer (n=10), lung cancer (n=9), esophageal squamous cell carcinoma (ESCC; n=6), HNC (n=5), bladder cancer (n=2), prostate cancer (n=2), oral squamous cell carcinoma (n=2), epithelial ovarian cancer (n=2), renal cell cancer (n=1), glioma (n=1), pancreatic cancer (n=1), cervical cancer (n=1), nasopharyngeal carcinoma (n=1), gallbladder cancer (n=1), acute lymphoblastic leukemia (n=1), and non-Hodgkin lymphoma (n=1). There were 64 studies of Asians and 18 studies of Caucasians.

Among the genotyping methods used in these studies, 57 studies used polymerase chain reaction (including polymerase chain reaction restriction fragment length polymorphism and polymerase chain reaction-ligation detection reaction), 16 studies used Taqman SNP genotyping assay, and others used MassARRAY and DNA sequencing. The controls of 42 studies mainly came from a hospital-based healthy population matched for gender and age, and 42 studies had population-based controls (PB). The distribution of genotypes in the controls of all of the studies was in agreement with HWE (P>0.05).

Quantitative synthesis

In this meta-analysis, we analyzed the hsa-miR-196a2 rs11614913 polymorphism in 84 comparisons with 35,802 cases and 41,541 controls. All the studies were pooled into the meta-analysis, and the results showed that the hsa-miR-196a2 rs11614913 polymorphism was significantly associated with the risk of cancer in the following genetic models: TT vs CC: OR =0.900, 95% CI =0.813–0.987, P=0.043; TT vs TC+CC: OR =0.918, 95% CI =0.851–0.989, P=0.025.

Then, we performed the subgroup analysis of different specific cancer types, genotypes, control sources, and ethnicities (Table 2). In the different cancer types, close association between rs11614913 and cancer risk was found for lung cancer (homozygote comparison, OR =0.840, 95% CI =0.734–0.961, P=0.011; recessive model, OR =0.858, 95% CI =0.771–0.955, P=0.005), hepatocellular carcinoma (allelic contrast, OR =0.894, 95% CI =0.800–0.998, P=0.047; homozygote comparison, OR =0.900, 95% CI =0.813–0.997, P=0.039; recessive model, OR =0.800, 95% CI =0.678–0.944, P=0.008), and HNC (allelic contrast, OR =1.076, 95% CI =1.006–1.152, P=0.033; homozygote comparison, OR =1.214, 95% CI =1.043–1.413, P=0.012; Figures 2 and 3). However, the association between rs11614913 and breast cancer, ESCC, gastric cancer (GC), or colorectal cancer (CRC) is not statistically significant.

Table 2.

Meta-analysis of miR-192a rs11614913 polymorphism with cancer risk

rs11614913 na Case/control T vs C
TT vs CC
TC vs CC
OR (95% CI) P-value P–H I2, % OR (95% CI) P-value P–H I2, % OR (95% CI) P-value P–H I2, %
(A)
Total 84 35,802/41,541 0.958 (0.911–1.008) 0.096 0.000 81.30 0.900 (0.813–0.987) 0.043 0.000 78.80 1.005 (0.935–1.079) 0.902 0.000 71.60
Genotyping method
PCR 57 19,301/22,204 0.939 (0.871–1.012) 0.100 0.000 84.50 0.849 (0.732–0.986) 0.032 0.000 81.70 0.987 (0.883–1.102) 0.812 0.000 77.40
Taqman 16 8,565/10,286 1.021 (0.940–1.110) 0.618 0.000 67.40 1.059 (0.894–1.253) 0.507 0.000 65.70 1.053 (0.977–1.134) 0.174 0.410 3.70
Ethnicity
Asian 64 28,337/31,932 0.847 (0.889–0.997) 0.038 0.000 77.00 0.878 (0.788–0.977) 0.017 0.000 76.00 1.012 (0.936–1.095) 0.759 0.000 66.90
Caucasian 18 7,321/8,414 0.997 (0.842–1.181) 0.971 0.000 90.30 0.974 (0.714–1.329) 0.870 0.000 86.10 0.963 (0.785–1.180) 0.714 0.000 83.90
Cancer type
BRC 14 7,760/8,811 0.972 (0.869–1.088) 0.626 0.000 79.70 0.972 (0.869–1.088) 0.341 0.000 72.80 0.979 (0.854–1.121) 0.754 0.001 61.50
CRC 10 2,906/4,150 1.051 (0.867–1.276) 0.611 0.000 86.50 1.051 (0.867–1.276) 0.431 0.000 87.60 1.121 (0.832–1.510) 0.454 0.000 81.10
ESCC 6 3,492/4,376 0.944 (0.816–1.091) 0.435 0.001 76.80 0.944 (0.816–1.091) 0.385 0.000 82.40 1.050 (0.878–1.255) 0.594 0.040 57.20
GC 10 3,723/5,256 0.857 (0.663–1.109) 0.241 0.000 93.80 0.857 (0.663–1.109) 0.276 0.000 91.50 0.778 (0.552–1.098) 0.153 0.000 88.70
HCC 14 4,988/5,962 0.894 (0.800–0.998) 0.047 0.000 72.60 0.900 (0.813–0.997) 0.039 0.000 70.50 0.981 (0.838–1.149) 0.816 0.005 56.30
HNC 5 3,534/3,564 1.076 (1.006–1.152) 0.033 0.285 20.40 1.214 (1.043–1.413) 0.012 0.380 2.50 1.157 (0.922–1.451) 0.209 0.003 75.00
LC 9 2,786/3,191 0.95 (0.854–1.058) 0.354 0.022 55.30 0.840 (0.734–0.961) 0.011 0.025 48.10 0.997 (0.889–1.118) 0.961 0.056 47.20
Design
PB 42 20,691/21,533 0.968 (0.907–1.033) 0.324 0.000 77.20 0.899 (0.777–1.017) 0.087 0.000 74.70 1.018 (0.928–1.117) 0.703 0.000 66.60
HB 42 15,111/20,008 0.945 (0.873–1.024) 0.167 0.000 84.50 0.906 (0.813–0.997) 0.211 0.000 81.90 0.987 (0.882–1.104) 0.822 0.000 75.90
rs11614913 na TT vs TC+CC
TT+TC vs CC
OR (95% CI) P-value P–H I2, % OR (95% CI) P-value P–H I2, %
(B)
Total 84 0.918 (0.851–0.989) 0.025 0.000 75.80 0.974 (0.901–1.052) 0.498 0.000 78.40
Genotyping method
PCR 57 0.880 (0.800–0.9690) 0.009 0.000 73.20 0.949 (0.842–1.069) 0.386 0.000 82.80
Taqman 16 1.000 (0.858–1.166) 0.996 0.000 71.90 1.063 (0.969–1.165) 0.195 0.095 34.10
Ethnicity
Asian 64 0.895 (0.824–0.972) 0.008 0.000 76.50 0.972 (0.8396–1.005) 0.493 0.000 72.90
Caucasian 17 1.015 (0.820–1.256) 0.894 0.000 75.30 0.966 (0.766–1.219) 0.772 0.000 89.30
Cancer type
BRC 14 0.943 (0.815–1.091) 0.429 0.001 64.40 0.967 (0.830–1.126) 0.663 0.000 73.30
CRC 10 1.066 (0.823–1.381) 0.628 0.000 79.00 1.130 (0.826–1.546) 0.444 0.000 84.70
ESCC 6 0.813 (0.610–1.085) 0.160 0.000 81.30 1.000 (0.822–1.216) 0.997 0.008 67.80
GC 10 0.910 (0.697–1.189) 0.489 0.000 83.90 0.763 (0.507–1.148) 0.194 0.000 92.90
HCC 14 0.800 (0.678–0.944) 0.008 0.000 67.40 0.919 (0.776–1.089) 0.332 0.000 66.20
HNC 5 1.205 (0.799–1.817) 0.375 0.000 90.10 1.156 (0.950–1.406) 0.148 0.011 69.10
LC 9 0.858 (0.771–0.955) 0.005 0.158 32.50 0.997 (0.834–1.191) 0.973 0.019 56.20
Design
PB 42 0.924 (0.826–1.034) 0.170 0.000 78.10 0.988 (0.897–1.087) 0.800 0.000 72.40
HB 42 0.912 (0.823–1.010) 0.078 0.000 73.90 0.955 (0.843–1.081) 0.465 0.000 82.70
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Notes: Random-effects model was used when P-value of Q-test for heterogeneity test (P–H) is <0.05; otherwise, fixed-effect model was used. I2: 0%–25%, no heterogeneity; 25%–50%, modest heterogeneity; ≥50%, high heterogeneity.

a

Number of studies involved. Bold figures indicate statistically significant (P<0.05).

Abbreviations: BRC, breast cancer; CRC, colorectal cancer; ESCC, esophageal squamous cell carcinoma; GC, gastric cancer; HB, hospital based; HCC, hepatocellular carcinoma; HNC, head and neck cancer; LC, lung cancer; OR, odds ratio; PB, population based; PCR, polymerase chain reaction; P–H, P-value of heterogeneity test.

Figure 2.

Figure 2

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Forest plots of the association between miR-196a2 rs11614913 polymorphism and cancer risk in different cancer types for homozygote comparison (TT vs CC).

Note: Weights are from random effects analysis.

Abbreviations: BRC, breast cancer; CRC, colorectal cancer; ESCC, esophageal squamous cell carcinoma; GC, gastric cancer; HCC, hepatocellular carcinoma; HNC, head and neck cancer; LC, lung cancer; miR-196a2, microRNA-196a2; OR, odds ratio.

Figure 3.

Figure 3

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Forest plots of the association between miR-196a2 rs11614913 polymorphism and cancer risk in different cancer types for recessive model (TT vs TC+CC).

Note: Weights are from random effects analysis.

Abbreviations: BRC, breast cancer; CRC, colorectal cancer; ESCC, esophageal squamous cell carcinoma; GC, gastric cancer; HCC, hepatocellular carcinoma; HNC, head and neck cancer; LC, lung cancer; miR-196a2, microRNA-196a2; OR, odds ratio.

In ethnic subgroup analysis, a strong association was found between rs11614913 and cancer risk in the allelic contrast (T vs C: OR =0.847, 95% CI =0.899–0.997, P=0.038), the homozygote comparison (TT vs CC: OR =0.878, 95% CI =0.788–0.977, P=0.017), and the recessive model (OR =0.895, 95% CI =0.824–0.972, P=0.008) among Asians, whereas negative results were obtained for Caucasians in all genetic models. Additionally, decreased risk was observed in the polymerase chain reaction (PCR) method for the homozygote comparison (TT vs CC: OR =0.849, 95% CI =0.732–0.986, P=0.032) and the recessive model (TT vs TC+CC: OR =0.880, 95% CI =0.800–0.969, P=0.009), and no significant association of cancer risk was found in Taqman and other methods.

Test of heterogeneity

Among the studies of rs11614913, we found heterogeneity in overall comparisons and subgroup analysis. Moreover, the heterogeneity we evaluated for all genetic models by ethnicity, cancer type, source of controls, as well HWE status was significant. However, we found that heterogeneity could not be explained by the variable ethnicity, cancer type, source of controls, and HWE status (data not shown).

Sensitivity analysis

Sensitivity analysis was conducted to assess the effect by excluding a single study in turn. Sensitivity analysis of the rs11614913 polymorphism in an allelic comparison is presented in Table S1. Overall, we found that no individual study had an influence on the pooled OR. The results demonstrated that the pooled ORs were not materially altered, suggesting the stability of our meta-analysis.

Publication bias

The publication bias of the present meta-analysis was assessed by Begg’s funnel plot and Egger’s test. The funnel plot for the rs11614913 polymorphism in the allelic comparison is presented in Table S2. No evidence of publication bias was noted in Begg’s funnel plot (T vs C [P-value for Begg’s test =0.660], TT vs CC [P-value for Begg’s test =0.971, Figure 4], TC vs CC [P-value for Begg’s test =0.951], TT vs TC+CC [P-value for Begg’s test =0.908, Figure 4], TC+TT vs CC [P-value for Begg’s test =0.592]) and Egger’s test (allele contrast [P=0.923], homozygous model [P=0.822], heterozygous model [P=0.761], recessive model [P=0.899], and dominant model [P=0.401]). The quality of included studies is presented in Table 3.

Figure 4.

Figure 4

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Begg’s funnel plot for publication bias of miR-196a2 rs11614913 polymorphism and cancer risk by homozygote comparison and recessive model.

Notes: Each point represents a separate study for the indicated association. LogES represents natural logarithm of OR. Horizontal line means magnitude of the effect. Funnel plot with pseudo 95% confidence limits was used.

Abbreviations: miR-196a2, microRNA-196a2; OR, odds ratio.

Table 3.

Methodological quality of the included studies according to the Newcastle–Ottawa scale

Author Adequacy of case definition Representativeness of the cases Selection of controls Definition of controls Comparability of cases/controls Ascertainment of exposure Same method of ascertainment Non-response rate
Hu et al7 * * * * ** * * NA
Hu et al35 * * NA * ** * * NA
Tian et al3 * * NA * * * * NA
Hoffman et al5 * * * * * * * NA
Catucci et al36 * * NA * ** NA * NA
Wang et al38 * * NA * ** * * NA
Okubo et al83 * * * * ** * * NA
Peng et al4 * * NA * ** NA * NA
Srivastava et al10 * * NA * ** * * NA
Dou et al6 * * NA NA * NA * NA
Li et al9 * * * * ** NA * NA
Akkiz et al8 * * NA * ** NA * NA
Liu et al11 * * NA * * * * NA
Kim et al110 * * NA NA * * * NA
Catucci et al36 * * * * ** * * NA
Christensen et al37 * * NA * ** * * NA
Mittal et al41 * * NA * ** * * NA
Jedlinski et al40 * * * * ** NA * NA
Zhan et al42 * * NA * * NA * NA
Zhou et al43 * * NA * ** NA * NA
Vinci et al111 * * NA * ** * * NA
Hong et al2 * * NA * * * * NA
George et al39 * * NA * ** * * NA
Linhares et al45 * * NA * ** * * NA
Chen et al44 * * NA * ** NA * NA
Min et al24 * * NA * ** * * NA
Zhu et al47 * * NA * ** * * NA
Hezova et al25 * * NA * ** NA * NA
Zhang et al100 * * * * ** * * NA
Ahn et al48 * * NA * ** * * NA
Yoon et al46 * * NA * ** * * NA
Zhang et al104 * * * * ** NA * NA
Chu et al87 * * NA * ** NA * NA
Vinci et al113 * * * * ** NA * NA
Lv et al51 * * * * ** NA * NA
Umar et al112 * * NA NA ** * * NA
Wei et al114 * * NA * ** * * NA
Toraih et al98 * * NA * ** * * NA
Wang et al53 * * NA * ** NA * NA
Zhang et al55 * * NA NA ** NA * NA
Han et al49 * * * * ** * * NA
Tong et al65 * * NA * ** * * NA
Pavlakis et al93 * * NA * ** * * NA
Pu et al84 * * * * ** NA * NA
Bansal et al56 * * NA * ** * * NA
Kupcinskas et al62 * * * * ** * * NA
Qu et al64 * * NA NA ** * * NA
Wang et al66 * * NA * ** * * NA
Dikeakos et al58 * * NA * ** * * NA
Qi et al86 * * NA * ** NA * NA
Chu et al57 * * * * * * * NA
Parlayan et al115 * * * * ** * * NA
Li et al63 * * NA * ** * * NA
Du et al59,60 * * NA * * NA * NA
Omrani et al85 * * NA * ** * * NA
Kou et al91 * * * * ** * * NA
Roy et al94 * * NA * ** * * NA
Li et al63 * * NA * ** NA * NA
Deng et al67 * * * * ** NA * NA
Qi et al72 * * NA * ** NA * NA
Dikaiakos et al68 * * * * * * * NA
Li et al69 * * NA NA ** * * NA
Li et al69 * * NA NA ** * * NA
Nikolic et al71 * * * * ** * * NA
He et al90 * * NA NA ** NA * NA
Sushma et al97 * * NA * ** * * NA
Sodhi et al95 * * * * ** * * NA
Jiang et al26 * * NA * ** * * NA
Dai et al74 * * NA * ** NA * NA
Zhao et al82 * * NA * ** * * NA
Song et al79 * * * * * NA * NA
Shen et al78 * * NA * ** * * NA
Li et al75 * * NA * ** NA * NA
Li et al76 * * NA * * * * NA
Xu et al80 * * NA NA * * * NA
Qiu and Liu77 * * * * * * * NA
Jiang et al26 * * * * ** * * NA
Yin et al81 * * NA * * * * NA
Zhang et al99 * * * * ** NA * NA
Sun et al96 * * * * * * * NA
Toraih et al98 * * NA * ** NA * NA
Morales et al92 * * NA * ** * * NA
Gu and Tu88 * * NA * * * * NA
Hashemi et al89 * * NA * ** * * NA
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Notes: This table identified “high”quality choices with a “*”. A study can be awarded a maximum of one “*” for each numbered item within the selection and exposure categories. A maximum of two “**” can be given for comparability.

Abbreviation: NA, not available.

Discussion

MiRNAs are reported as critical posttranscriptional regulators in gene expression and are involved in various diseases. The associations between miR-196a2 rs11614913 polymorphism and susceptibility to different cancers are widely explored. Guo et al101 found that the C allele had the effect of increasing cancer risk in gastric cancer, and Ma et al102 found that TT could decrease the risk of colorectal cancer. Moreover, Wang et al103 and Zhang et al104 showed that the rs11614913 polymorphism has no association with the risk of hepatocellular carcinoma. However, the regulatory effects of miRNA in carcinogenesis remain unclear. Therefore, we performed this updated meta-analysis to explore the molecular mechanisms of the genetic associations between miRNA and SNPs with cancer risk.

MiR-196a2 is composed of two distinct mature miRNAs (miR-196a-3P and miR-196a-5P), which are processed from the same stem loop;105 thus, the potential targets of miR-196a could be influenced by its altered expression patterns. SNPs in miRNAs could potentially affect the processing or target selection of miRNAs,106,107 which is identified as a key factor in oncogenesis, and contributes to regulate the translation or degradation of messenger RNA (mRNA).23 Hoffman et al5 found that the expression of mature miR-196a2 was increased 9.3-fold in cells transfected with pre-miR-196a2-C but upregulated only by 4.4-fold with pre-miR-196a2-T, and that the C allele of rs11614913 increased mature miR-196a2 levels in lung cancer7 and CRC42 tissues. Xu et al108 have shown that miR-196a2 rs11614913 CC is associated with significantly increased expression of mature miR-196a (lower cycle threshold corresponding to a higher expression) in cardiac tissue specimens of congenital heart disease, and the increased miR-196a expression could further decrease mRNA target of HOXB8. These results indicated that the rs11614913 polymorphism may affect the processing of the pre-miRNA to its mature form.

Several meta-analyses have been performed to analyse the SNP of this miRNA that is associated with the cancer risk.104,109 In our present work, we screened out all the studies published to date and included more papers and cancer types than the previously published meta-analyses. For example, Kang et al109 conducted a meta-analysis encompassing the rs11614913 polymorphism in miR-196a2 and cancer risks, which suggested that the rs11614913 polymorphism may contribute to decreased susceptibility to liver cancer (allele model, homozygous model, dominant model, and heterozygous model) and lung cancer (allele model, homozygous model, and recessive model); however, this was not duplicated in our meta-analysis. In this study, we concluded that the rs11614913 polymorphism conferred a decreased susceptibility to lung cancer (homozygote comparison, recessive model) and hepatocellular carcinoma (allelic contrast, homozygote comparison, recessive model) or an increased susceptibility to HNC (allelic contrast, homozygote comparison). Our study had a larger sample size than the previous ones, which might influence the results. In addition, the previous meta-analyses did not evaluate the quality of the included studies.

According to the procedure of seeking for the source of heterogeneity, we performed subgroup studies according to cancer type, ethnicity, and source of control. A strong association was found between rs11614913 and cancer risk in lung cancers, hepatocellular carcinoma, and HNC, but not in breast cancer, gastric cancer, ESCC, or CRC, which was not similar to the findings of previous studies.101–103,109 The present meta-analysis showed that homozygote TT had the effect of decreasing the risk of lung cancer or hepatocellular carcinoma compared with that of CC homozygote or C allele carriers. We conducted another subgroup analysis by population to determine the association between these miRNA polymorphisms and tumorigenesis. The results suggested that individuals with alterative T allele could decrease cancer susceptibility in Asians but not in Caucasians, indicating that the difference of ethnic background and the living environment may also be a risk factor.

To determine the hsa-miR-196a2 rs11614913 polymorphism, PCR, Taqman, and other methods have been adopted. We found that the hsa-miR-196a2 rs11614913 polymorphism significantly decreased cancer risk in homozygous models and the recessive model when using the PCR method, but this result was not shown when selecting Taqman and other methods. Therefore, more effort may be necessary for further progress in SNP analysis. We found sources of heterogeneity in the studies from cancer type and ethnicity suggesting cancer and population playing important roles. When detecting the source of control, we observed significant associations in population-based and hospital-based controls. This may be due to the included studies matching age, gender, and residential area to control selection bias.

Nevertheless, several defects of this meta-analysis should be emphasized. Firstly, although we strictly screened articles and precisely extracted the data, the differences in the selection of subjects could not be eliminated. Secondly, in our meta-analysis, only Asian and Caucasian ethnicities were included, and the impact of the differences in racial descent should not be ignored. Thirdly, potential language bias could not be avoided due to limitation of studies published in English or Chinese. Therefore, it is not possible to avoid potential publication bias in this meta-analysis.

In summary, miR-196a2 rs11614913 polymorphism may contribute to the development of cancer, especially in lung cancer, hepatocellular carcinoma, and HNC. It might be useful as a candidate marker for the diagnosis of these cancers, and could also be a potential protective factor for cancer risks in Asians. Furthermore, more significant studies and investigations with larger populations focusing on cancer types or ethnicities should be performed to confirm the results.

Supplementary materials

Table S1.

Details of the sensitivity analyses of the association between rs11614913 polymorphism and cancer risk homozygous model (TT vs CC) and recessive model (TT vs TC+CC).

Comparison Study omitted Estimate (95% Conf Interval)
Lower CI Upper CI
TT vs CC Hu et al7 0.902 0.814 0.999
Hu et al35 0.904 0.815 1.002
Tian et al3 0.902 0.814 1.001
Hoffman et al5 0.890 0.805 0.985
Catucci et al36 0.900 0.811 1.000
Wang et al38 0.911 0.824 1.008
Okubo et al83 0.900 0.812 0.998
Peng et al4 0.904 0.816 1.002
Srivastava et al10 0.903 0.815 1.000
Dou et al6 0.897 0.809 0.994
Li et al9 0.906 0.818 1.003
Akkiz et al8 0.908 0.820 1.005
Liu et al11 0.898 0.810 0.997
Kim et al101 0.904 0.815 1.002
Catucci et al36 0.899 0.810 0.997
Christensen et al37 0.900 0.813 0.997
Mittal et al41 0.904 0.816 1.001
Jedlinski et al40 0.900 0.813 0.998
Zhan et al42 0.906 0.818 1.004
Zhou et al43 0.901 0.813 0.998
Vinci et al102 0.895 0.809 0.992
Hong et al2 0.902 0.814 1.000
George et al39 0.902 0.815 0.999
Linhares et al45 0.893 0.806 0.988
Chen et al44 0.898 0.811 0.995
Min et al24 0.904 0.815 1.002
Zhu et al47 0.905 0.816 1.003
Hezova et al25 0.897 0.810 0.994
Zhang et al100 0.900 0.812 0.998
Yoon et al46 0.904 0.816 1.001
Zhang et al99 0.904 0.816 1.001
Chu et al87 0.894 0.807 0.990
Vinci et al105 0.897 0.810 0.994
Ahn et al103 0.902 0.814 1.000
Lv et al51 0.878 0.798 0.965
Umar et al104 0.895 0.808 0.992
Wei et al106 0.896 0.809 0.993
Wang et al53 0.894 0.807 0.990
Zhang et al55 0.904 0.816 1.003
Han et al49 0.898 0.810 0.996
Pavlakis et al93 0.899 0.812 0.996
Tong et al65 0.901 0.813 1.000
Pu et al84 0.902 0.814 1.000
Bansal et al56 0.902 0.815 1.000
Kupcinskas et al62 0.897 0.809 0.994
Qu et al64 0.905 0.817 1.003
Wang et al66 0.897 0.809 0.994
Dikeakos et al58 0.925 0.843 1.015
Qi et al86 0.902 0.814 1.000
Chu et al57 0.898 0.810 0.995
Parlayan et al107 0.900 0.812 0.997
Li et al63 0.896 0.808 0.993
Du et al59 0.892 0.806 0.987
Omrani et al85 0.900 0.813 0.997
Kou et al91 0.907 0.819 1.004
Roy et al94 0.896 0.809 0.993
Li et al63 0.896 0.808 0.993
Deng et al67 0.900 0.812 0.997
Qi et al72 0.907 0.819 1.005
Dikaiakos et al68 0.899 0.812 0.996
Li et al69 0.890 0.805 0.985
Li et al69 0.907 0.819 1.004
Nikolic et al71 0.902 0.814 1.000
He et al90 0.901 0.813 0.999
Sushma et al97 0.909 0.821 1.006
Sodhi et al95 0.891 0.806 0.986
Jiang et al26 0.896 0.808 0.993
Toraih et al98 0.894 0.807 0.990
Dai et al74 0.908 0.820 1.005
Zhao et al82 0.898 0.811 0.995
Song et al79 0.907 0.819 1.004
Shen et al78 0.902 0.813 1.002
Li et al75 0.907 0.820 1.005
Li et al76 0.906 0.819 1.004
Xu et al80 0.906 0.818 1.004
Qiu et al77 0.905 0.817 1.003
Jiang et al26 0.901 0.813 1.000
Yin et al81 0.901 0.813 0.999
Zhang et al99 0.901 0.813 0.998
Sun et al96 0.904 0.817 1.002
Toraih et al98 0.894 0.808 0.990
Morales et al92 0.901 0.812 0.999
Gu et al88 0.891 0.805 0.986
Hashemi et al89 0.896 0.809 0.992
Combined2–10,25,26,35–107 0.900 0.813 0.997
TT vs TC+CC Hu et al7 0.918 0.851 0.991
Hu et al35 0.920 0.852 0.993
Tian et al3 0.918 0.850 0.991
Hoffman et al5 0.910 0.844 0.980
Catucci et al36 0.917 0.849 0.991
Wang et al38 0.928 0.862 0.999
Okubo et al83 0.917 0.850 0.991
Peng et al4 0.919 0.852 0.991
Srivastava et al10 0.918 0.850 0.990
Dou et al6 0.918 0.850 0.991
Li et al9 0.922 0.854 0.994
Akkiz et al8 0.923 0.856 0.995
Liu et al11 0.917 0.849 0.990
Kim et al101 0.920 0.852 0.992
Catucci et al36 0.916 0.849 0.989
Christensen et al37 0.918 0.851 0.989
Mittal et al41 0.921 0.854 0.993
Jedlinski et al40 0.917 0.850 0.989
Zhan et al42 0.922 0.854 0.994
Zhou et al43 0.918 0.850 0.990
Vinci et al102 0.915 0.849 0.987
Hong et al2 0.922 0.854 0.994
George et al39 0.920 0.853 0.992
Linhares et al45 0.913 0.847 0.985
Chen et al44 0.916 0.849 0.988
Min et al24 0.918 0.850 0.990
Zhu et al47 0.921 0.854 0.994
Hezova et al25 0.915 0.848 0.987
Zhang et al100 0.918 0.850 0.991
Yoon et al46 0.920 0.853 0.993
Zhang et al99 0.919 0.852 0.992
Chu et al87 0.918 0.851 0.991
Vinci et al105 0.919 0.851 0.991
Ahn et al103 0.916 0.850 0.988
Lv et al51 0.905 0.842 0.974
Umar et al104 0.914 0.848 0.986
Wei et al106 0.918 0.850 0.990
Wang et al53 0.913 0.846 0.985
Zhang et al55 0.919 0.851 0.992
Han et al49 0.917 0.849 0.990
Pavlakis et al93 0.921 0.854 0.994
Tong et al65 0.913 0.847 0.985
Pu et al84 0.918 0.851 0.990
Bansal et al56 0.919 0.852 0.991
Kupcinskas et al62 0.916 0.849 0.988
Qu et al64 0.923 0.855 0.995
Wang et al66 0.916 0.848 0.988
Dikeakos et al58 0.931 0.866 1.001
Qi et al86 0.924 0.857 0.996
Chu et al57 0.914 0.847 0.986
Parlayan et al107 0.918 0.851 0.990
Li et al63 0.913 0.846 0.985
Du et al59 0.914 0.847 0.986
Omrani et al85 0.918 0.851 0.989
Kou et al91 0.921 0.854 0.994
Roy et al94 0.915 0.848 0.987
Li et al63 0.906 0.845 0.971
Deng et al67 0.913 0.847 0.985
Qi et al72 0.923 0.856 0.995
Dikaiakos et al68 0.914 0.848 0.987
Li et al69 0.911 0.845 0.982
Li et al69 0.922 0.855 0.995
Nikolic et al71 0.919 0.852 0.991
He et al90 0.917 0.850 0.990
Sushma et al97 0.921 0.855 0.994
Sodhi et al95 0.913 0.847 0.984
Jiang et al26 0.914 0.847 0.986
Toraih et al98 0.914 0.848 0.986
Dai et al74 0.922 0.855 0.995
Zhao et al82 0.914 0.848 0.986
Song et al79 0.923 0.856 0.995
Shen et al78 0.918 0.849 0.992
Li et al75 0.921 0.854 0.993
Li et al76 0.923 0.856 0.995
Xu et al80 0.922 0.854 0.994
Qiu et al77 0.921 0.854 0.993
Jiang et al26 0.921 0.854 0.994
Yin et al81 0.919 0.851 0.992
Zhang et al99 0.918 0.851 0.991
Sun et al96 0.919 0.852 0.992
Toraih et al98 0.915 0.848 0.986
Morales et al92 0.918 0.851 0.991
Gu et al88 0.911 0.845 0.982
Hashemi et al89 0.915 0.848 0.986
Combined2–10,25,26,35–107 0.918 0.851 0.989
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Table S2.

P-values of Begg’s and Egger’s test for the polymorphism rs11614913

Polymorphism Comparison Subgroup Begg’s test
(P>z)		 Egger’s test
(P>t)
rs11614913 T vs C Overall 0.660 0.923
Taqman 0.368 0.723
PCR 0.640 0.859
Asian 0.946 0.854
Caucasian 0.147 0.969
HB 0.509 0.386
PB 0.251 0.579
TT vs CC Overall 0.971 0.822
Taqman 0.719 0.606
PCR 0.832 0.762
Asian 0.578 0.758
Caucasian 0.163 0.971
HB 0.721 0.489
PB 0.666 0.880
TC vs CC Overall 0.951 0.761
Taqman 0.418 0.289
PCR 0.839 0.933
Asian 0.991 0.546
Caucasian 0.902 0.767
HB 0.721 0.601
PB 0.965 0.453
TT+TC vs CC Overall 0.592 0.401
Taqman 0.418 0.613
PCR 0.734 0.598
Asian 0.986 0.185
Caucasian 0.300 0.770
HB 0.737 0.543
PB 0.584 0.593
TT vs TC+CC Overall 0.908 0.899
Taqman 0.719 0.440
PCR 0.912 0.917
Asian 0.795 0.688
Caucasian 0.537 0.857
HB 0.673 0.503
PB 0.914 0.508
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Abbreviations: HB, hospital based; PB, population based; PCR, polymerase chain reaction.

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Acknowledgments

This review was supported by Health Care 3F Project of Shenzhen (Peking University First Hospital-The Second People’s Hospital of Shenzhen, Academician Yinglu Guo’s Team), the Shenzhen Key Medical Discipline Fund, Special Support Funds of Shenzhen for Introduced High-Level Medical Team, Shenzhen Foundation of Science and Technology (JCYJ20150330102720182), and Shenzhen Health and Family Planning Commission Scientific Research Project (201506026, 201601025, and 201606019).

Footnotes

Disclosure

The authors report no conflicts of interest in this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1.

Details of the sensitivity analyses of the association between rs11614913 polymorphism and cancer risk homozygous model (TT vs CC) and recessive model (TT vs TC+CC).

Comparison Study omitted Estimate (95% Conf Interval)
Lower CI Upper CI
TT vs CC Hu et al7 0.902 0.814 0.999
Hu et al35 0.904 0.815 1.002
Tian et al3 0.902 0.814 1.001
Hoffman et al5 0.890 0.805 0.985
Catucci et al36 0.900 0.811 1.000
Wang et al38 0.911 0.824 1.008
Okubo et al83 0.900 0.812 0.998
Peng et al4 0.904 0.816 1.002
Srivastava et al10 0.903 0.815 1.000
Dou et al6 0.897 0.809 0.994
Li et al9 0.906 0.818 1.003
Akkiz et al8 0.908 0.820 1.005
Liu et al11 0.898 0.810 0.997
Kim et al101 0.904 0.815 1.002
Catucci et al36 0.899 0.810 0.997
Christensen et al37 0.900 0.813 0.997
Mittal et al41 0.904 0.816 1.001
Jedlinski et al40 0.900 0.813 0.998
Zhan et al42 0.906 0.818 1.004
Zhou et al43 0.901 0.813 0.998
Vinci et al102 0.895 0.809 0.992
Hong et al2 0.902 0.814 1.000
George et al39 0.902 0.815 0.999
Linhares et al45 0.893 0.806 0.988
Chen et al44 0.898 0.811 0.995
Min et al24 0.904 0.815 1.002
Zhu et al47 0.905 0.816 1.003
Hezova et al25 0.897 0.810 0.994
Zhang et al100 0.900 0.812 0.998
Yoon et al46 0.904 0.816 1.001
Zhang et al99 0.904 0.816 1.001
Chu et al87 0.894 0.807 0.990
Vinci et al105 0.897 0.810 0.994
Ahn et al103 0.902 0.814 1.000
Lv et al51 0.878 0.798 0.965
Umar et al104 0.895 0.808 0.992
Wei et al106 0.896 0.809 0.993
Wang et al53 0.894 0.807 0.990
Zhang et al55 0.904 0.816 1.003
Han et al49 0.898 0.810 0.996
Pavlakis et al93 0.899 0.812 0.996
Tong et al65 0.901 0.813 1.000
Pu et al84 0.902 0.814 1.000
Bansal et al56 0.902 0.815 1.000
Kupcinskas et al62 0.897 0.809 0.994
Qu et al64 0.905 0.817 1.003
Wang et al66 0.897 0.809 0.994
Dikeakos et al58 0.925 0.843 1.015
Qi et al86 0.902 0.814 1.000
Chu et al57 0.898 0.810 0.995
Parlayan et al107 0.900 0.812 0.997
Li et al63 0.896 0.808 0.993
Du et al59 0.892 0.806 0.987
Omrani et al85 0.900 0.813 0.997
Kou et al91 0.907 0.819 1.004
Roy et al94 0.896 0.809 0.993
Li et al63 0.896 0.808 0.993
Deng et al67 0.900 0.812 0.997
Qi et al72 0.907 0.819 1.005
Dikaiakos et al68 0.899 0.812 0.996
Li et al69 0.890 0.805 0.985
Li et al69 0.907 0.819 1.004
Nikolic et al71 0.902 0.814 1.000
He et al90 0.901 0.813 0.999
Sushma et al97 0.909 0.821 1.006
Sodhi et al95 0.891 0.806 0.986
Jiang et al26 0.896 0.808 0.993
Toraih et al98 0.894 0.807 0.990
Dai et al74 0.908 0.820 1.005
Zhao et al82 0.898 0.811 0.995
Song et al79 0.907 0.819 1.004
Shen et al78 0.902 0.813 1.002
Li et al75 0.907 0.820 1.005
Li et al76 0.906 0.819 1.004
Xu et al80 0.906 0.818 1.004
Qiu et al77 0.905 0.817 1.003
Jiang et al26 0.901 0.813 1.000
Yin et al81 0.901 0.813 0.999
Zhang et al99 0.901 0.813 0.998
Sun et al96 0.904 0.817 1.002
Toraih et al98 0.894 0.808 0.990
Morales et al92 0.901 0.812 0.999
Gu et al88 0.891 0.805 0.986
Hashemi et al89 0.896 0.809 0.992
Combined2–10,25,26,35–107 0.900 0.813 0.997
TT vs TC+CC Hu et al7 0.918 0.851 0.991
Hu et al35 0.920 0.852 0.993
Tian et al3 0.918 0.850 0.991
Hoffman et al5 0.910 0.844 0.980
Catucci et al36 0.917 0.849 0.991
Wang et al38 0.928 0.862 0.999
Okubo et al83 0.917 0.850 0.991
Peng et al4 0.919 0.852 0.991
Srivastava et al10 0.918 0.850 0.990
Dou et al6 0.918 0.850 0.991
Li et al9 0.922 0.854 0.994
Akkiz et al8 0.923 0.856 0.995
Liu et al11 0.917 0.849 0.990
Kim et al101 0.920 0.852 0.992
Catucci et al36 0.916 0.849 0.989
Christensen et al37 0.918 0.851 0.989
Mittal et al41 0.921 0.854 0.993
Jedlinski et al40 0.917 0.850 0.989
Zhan et al42 0.922 0.854 0.994
Zhou et al43 0.918 0.850 0.990
Vinci et al102 0.915 0.849 0.987
Hong et al2 0.922 0.854 0.994
George et al39 0.920 0.853 0.992
Linhares et al45 0.913 0.847 0.985
Chen et al44 0.916 0.849 0.988
Min et al24 0.918 0.850 0.990
Zhu et al47 0.921 0.854 0.994
Hezova et al25 0.915 0.848 0.987
Zhang et al100 0.918 0.850 0.991
Yoon et al46 0.920 0.853 0.993
Zhang et al99 0.919 0.852 0.992
Chu et al87 0.918 0.851 0.991
Vinci et al105 0.919 0.851 0.991
Ahn et al103 0.916 0.850 0.988
Lv et al51 0.905 0.842 0.974
Umar et al104 0.914 0.848 0.986
Wei et al106 0.918 0.850 0.990
Wang et al53 0.913 0.846 0.985
Zhang et al55 0.919 0.851 0.992
Han et al49 0.917 0.849 0.990
Pavlakis et al93 0.921 0.854 0.994
Tong et al65 0.913 0.847 0.985
Pu et al84 0.918 0.851 0.990
Bansal et al56 0.919 0.852 0.991
Kupcinskas et al62 0.916 0.849 0.988
Qu et al64 0.923 0.855 0.995
Wang et al66 0.916 0.848 0.988
Dikeakos et al58 0.931 0.866 1.001
Qi et al86 0.924 0.857 0.996
Chu et al57 0.914 0.847 0.986
Parlayan et al107 0.918 0.851 0.990
Li et al63 0.913 0.846 0.985
Du et al59 0.914 0.847 0.986
Omrani et al85 0.918 0.851 0.989
Kou et al91 0.921 0.854 0.994
Roy et al94 0.915 0.848 0.987
Li et al63 0.906 0.845 0.971
Deng et al67 0.913 0.847 0.985
Qi et al72 0.923 0.856 0.995
Dikaiakos et al68 0.914 0.848 0.987
Li et al69 0.911 0.845 0.982
Li et al69 0.922 0.855 0.995
Nikolic et al71 0.919 0.852 0.991
He et al90 0.917 0.850 0.990
Sushma et al97 0.921 0.855 0.994
Sodhi et al95 0.913 0.847 0.984
Jiang et al26 0.914 0.847 0.986
Toraih et al98 0.914 0.848 0.986
Dai et al74 0.922 0.855 0.995
Zhao et al82 0.914 0.848 0.986
Song et al79 0.923 0.856 0.995
Shen et al78 0.918 0.849 0.992
Li et al75 0.921 0.854 0.993
Li et al76 0.923 0.856 0.995
Xu et al80 0.922 0.854 0.994
Qiu et al77 0.921 0.854 0.993
Jiang et al26 0.921 0.854 0.994
Yin et al81 0.919 0.851 0.992
Zhang et al99 0.918 0.851 0.991
Sun et al96 0.919 0.852 0.992
Toraih et al98 0.915 0.848 0.986
Morales et al92 0.918 0.851 0.991
Gu et al88 0.911 0.845 0.982
Hashemi et al89 0.915 0.848 0.986
Combined2–10,25,26,35–107 0.918 0.851 0.989
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Table S2.

P-values of Begg’s and Egger’s test for the polymorphism rs11614913

Polymorphism Comparison Subgroup Begg’s test
(P>z)		 Egger’s test
(P>t)
rs11614913 T vs C Overall 0.660 0.923
Taqman 0.368 0.723
PCR 0.640 0.859
Asian 0.946 0.854
Caucasian 0.147 0.969
HB 0.509 0.386
PB 0.251 0.579
TT vs CC Overall 0.971 0.822
Taqman 0.719 0.606
PCR 0.832 0.762
Asian 0.578 0.758
Caucasian 0.163 0.971
HB 0.721 0.489
PB 0.666 0.880
TC vs CC Overall 0.951 0.761
Taqman 0.418 0.289
PCR 0.839 0.933
Asian 0.991 0.546
Caucasian 0.902 0.767
HB 0.721 0.601
PB 0.965 0.453
TT+TC vs CC Overall 0.592 0.401
Taqman 0.418 0.613
PCR 0.734 0.598
Asian 0.986 0.185
Caucasian 0.300 0.770
HB 0.737 0.543
PB 0.584 0.593
TT vs TC+CC Overall 0.908 0.899
Taqman 0.719 0.440
PCR 0.912 0.917
Asian 0.795 0.688
Caucasian 0.537 0.857
HB 0.673 0.503
PB 0.914 0.508
Open in a new tab

Abbreviations: HB, hospital based; PB, population based; PCR, polymerase chain reaction.

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