Several studies have evaluated the association of miR-146a C/G with head and neck cancer (HNC) susceptibility, and overall cancer risk, but with inconclusive outcomes. To drive a more precise estimation, we carried out this meta-analysis. The literature was searched from MEDLINE (mainly PubMed), Embase, the Cochrane Library, and Google Scholar databases to identify eligible studies. A total of 89 studies were included. The results showed that miR-146a C/G was significantly associated with increased HNC risk in dominant model (I2 =15.6%, Pheterogeneity=0.282, odds ratio (OR) =1.088, 95% confidence interval (CI) =1.002–1.182, P=0.044). However, no cancer risk was detected under all genetic models. By further stratified analysis, we found that rs4919510 mutation contributed to the risk of HNC amongst Asians under homozygote model (I2 =0, Pheterogeneity=0.541, OR =1.189, 95% CI =1.025–1.378, P=0.022), and dominant model (I2 =0, Pheterogeneity=0.959, OR =1.155, 95% CI =1.016–1.312, P=0.028). Simultaneously, in the stratified analysis by source of controls, a significantly increased cancer risk amongst population-based studies was found under homozygote model, dominant model, recessive model, and allele comparison model. However, no significant association was found in the stratified analysis by ethnicity and source of control. The results indicated that miR-146a C/G polymorphism may contribute to the increased HNC susceptibility and could be a promising target to forecast cancer risk for clinical practice. However, no significant association was found in subgroup analysis by ethnicity and source of control. To further confirm these results, well-designed large-scale case–control studies are needed in the future.

Introduction

Cancer, although an age old disease, still poses a formidable challenge to researchers and clinicians. Little is known about its initiation, sustenance, progression and metastasis, and resistance and remission. Due to its morbidity and mortality, cancer is one of the most dreaded diseases and the related fatalities are majorly attributed to delayed diagnosis and treatment. Head and neck cancer (HNC), the sixth most frequent kind of cancer worldwide, is a group of biologically similar cancers that originate from head and neck regions such as oral cavity, pharyngeal cavity, and larynx [1]. Multifactors such as smoking, drinking, betel quid chewing, papilloma virus infection, and exposure to toxic substances are suggested to be the etiological risk factors for HNC [2,3]. Nevertheless, though many individuals are exposed to these external factors, HNC develops only in a small proportion of the exposed people, indicating that intrinsic factors such as genetic polymorphism might play critical roles in its carcinogenic mechanisms.

miRNAs represent a class of evolutionarily conserved, endogenous, single-stranded, non-coding RNA molecules of ~20 nts that regulate gene expression by degrading mRNAs or suppressing translation. miRNAs have been implicated in a wide range of physiologic and pathologic processes, including development, cell differentiation, proliferation, apoptosis, and carcinogenesis [4,5]. Accumulating evidence indicates that the expression of roughly 10–30% of all human genes is regulated by miRNAs [6]. More than half of the known miRNAs are located in cancer-associated genomic regions, and miRNAs are thought to contribute to oncogenesis because they can function either as tumor suppressors or oncogenes [7]. Analyses in human epithelial malignancies have shown that cancers can be distinguished and classified by distinct tumor-specific miRNA signatures [8]. Some of the key dysregulated miRNAs could serve as molecular biomarkers, leading to improved diagnosis and monitoring of cancer treatment response [911].

Single nucleotide polymorphisms (SNPs) are a type of common genetic variations associated with population diversity, disease susceptibility, drug metabolism, and genome evolution [12]. SNPs may affect the expression and function of miRNAs, which could therefore contribute to the susceptibility to cancer occurrence and development [1316]. miR-146a C/G is located in the stem region opposite to the mature miR-146a sequence, which is suspected to have an effect on tumor immune responses and ultimately the development of cancer. In recent years, the polymorphism rs2910164 in miR-146a has attracted wide attention and many studies have been published to explore the association between SNPs of miRNAs and susceptibility to various cancers. But the results were not conclusive and consistent. Since SNPs in miRNAs are closely associated with head and neck cancer (HNC) susceptibility, it is necessary to assess whether these SNP polymorphisms are the risk factors for HNC. It is reported that meta-analysis is a well-established method for combining all the results from the available published information to produce a single estimate for quantitating gene–disease associations more precisely to increase the statistical power [17]. Thus, we performed this meta-analysis of case–control studies to estimate the importance of pre-miR-146a C/G polymorphism for HNC susceptibility.

Materials and methods

Publication search

A comprehensive electronic search was performed to identify articles published up until 12 November 2016 in MEDLINE (mainly PubMed), Embase, the Cochrane Library, and Google Scholar using the following search terms: ‘miR-146a’ or ‘rs2910164’ and ‘head and neck cancer’ or ‘cancer’ or ‘tumor’ or ‘carcinoma’ and ‘polymorphism’ or ‘SNPs’ or ‘variation’. All eligible studies published in English were retrieved, and their bibliographies were checked for additional relevant publications. Review articles and bibliographies of other identified relevant studies were searched by hand to identify any additional eligible studies.

Inclusion and exclusion criteria

Studies included in this meta-analysis had to meet all of the following criteria: (i) case–control study evaluating the association between miR-146a C/G polymorphism and susceptibility to HNC and overall cancer; (ii) sufficient published data for calculating odds ratios (ORs) with corresponding 95% confidence intervals (CIs); (iii) full-text manuscript; and (iv) only the most recent or complete study reporting on the same population of patients was included. Exclusion criteria included: (i) reviews, other meta-analyses, comments, letters, and editorial articles; (ii) not a case–control study; and (iii) no usable data reported.

Data extraction

Information regarding the following aspects was independently retrieved from each study by two reviewers: the first author’s surname, year of publication, country of origin, ethnicity, study design, total number of cases and controls, source of cases and controls, detected sample, genotyping methods, allele and genotype frequencies of cases and controls, and evidence of Hardy–Weinberg equilibrium (HWE) in the controls. In studies including subjects of more than one ethnicity, genotype data were extracted separately for each ethnic group. Data from one publication may contain more than one seperate case-control studies. Any discrepancies between the reviewers were resolved through discussion to reach a consensus.

Statistical analysis

We used crude ORs with 95% CIs to explore the association between miR-146a C/G polymorphism and the risk of HNC and overall cancer. Five genetic variation models were analyzed: homozygote model (CC compared with GG), heterogeneity model (GC compared with CC), dominant model (CC + GC compared with GG), recessive model (CC compared with GC + GG), and allele comparison model (C compared with G). P-value of HWE in control group of each study was calculated by χ2 test and P<0.05 presented a state of disequilibrium [18]. We also performed subgroup analyses by ethnicity and source of control, and heterogeneity was calculated by χ2-based Q-statistic [19]. Both random-effects model (when P-value of heterogeneity was less than 0.05) and fixed-effects model (when P-value of heterogeneity was more than 0.05) were used [20,21]. Sensitivity analyses were performed to verify if our present results were stable. Begg’s funnel plots and Egger’s linear regression tests were used to examine possible publication bias [22,23]. All statistical analyses were performed using Stata software version 11.0 (StataCorp LP, College Station, TX, U.S.A.). All statistical analyses were two-sided, and P-values <0.05 were considered statistically significant.

Results

Characteristics of eligible studies

A total of 721 articles were retrieved after the first search in PubMed, Embase, the Cochrane Library, and Google Scholar. Selection following the specified criteria eliminated 632 studies, leaving 89 individual studies [24103]. The details of the selection process are presented in Figure 1. The publication years of included articles ranged from 2008 to 2016. The distributions of miR-146a C/G genotype in all studies were in accordance with HWE in the control group. No significant differences were found between cases and controls with respect to gender and age distributions. The modified quality scores of all studies ranged from 9 to 16, with 71% (5/7) of the included studies classified as high quality (≥12).The characteristics of all included studies are summarized in Table 1.

The process of literature research

Figure 1
The process of literature research
Figure 1
The process of literature research
Table 1
Characteristics of all eligible studies
Reference Year Country Ethnicity Cancer type Control source Genotyping method Sample size Case Control 
       Cases Controls GG GC CC GG GC CC 
Horikawa et al. [242008 U.S.A. Caucasian Renal cell cancer PB SNPlex assay 261 235 144 103 14 126 94 15 
Jazdzewski et al.1 [252008 Finland Caucasian PTC PB SNPlex assay 206 274 99 104 150 105 19 
Jazdzewski et al.2 [252008 Poland Caucasian PTC PB SNPlex assay 201 475 115 82 286 163 26 
Jazdzewski et al.3 [252008 U.S.A. Caucasian PTC PB SNPlex assay 201 152 91 101 90 52 10 
Xu et al. [262008 China Asian Liver cancer HB PCR-RFLP 479 504 80 241 158 58 249 197 
Yang et al. [272008 U.S.A. Caucasian Bladder cancer PB SNPlex assay 691 674 414 242 35 385 258 31 
Hoffman et al. [282009 U.S.A. Caucasian Breast cancer PB massARRAY 439 478 234 176 29 273 178 27 
Hu et al. [292009 China Asian Breast cancer HB PCR-RFLP 1009 1093 165 515 329 180 551 362 
Tian et al. [302009 China Asian Lung cancer PB PCR-RFLP 1058 1035 360 510 188 364 502 169 
Catucci et al.1 [312010 Italy Caucasian Breast cancer HB Sequencing 754 1243 409 286 59 650 520 73 
Catucci et al.2 [312010 Germany Caucasian Breast cancer HB Sequencing 805 904 451 304 50 536 318 50 
Guo et al. [322010 China Caucasian ESCC PB SNaPshot 444 468 234 190 20 206 220 42 
Liu et al. [332010 U.S.A. Mixed SCCHN HB PCR-RFLP 1109 1130 630 411 68 655 405 70 
Okubo et al. [342010 Japan Asian Gastric cancer HB PCR-RFLP 552 697 73 243 236 121 322 254 
Pastrello et al. [352010 Italy Caucasian Breast and ovarian cancer PB Sequencing 101 155 60 36 90 59 
Srivastava et al. [362010 India Asian Gall bladder cancer PB PCR-RFLP 230 224 129 90 11 138 81 
Xu et al. [372010 China Asian Prostate cancer HB PCR-RFLP 251 280 68 135 48 54 150 76 
Zeng et al. [382010 China Asian Gastric cancer HB PCR-RFLP 304 304 62 153 89 53 132 119 
Akkiz et al. [392011 Turkey Caucasian Liver cancer HB PCR-RFLP 222 222 137 75 10 144 67 11 
Garcia et al. [402011 French Caucasian Breast cancer PB TaqMan 1130 596 676 388 66 352 220 24 
George et al. [412011 India Asian Prostate cancer PB PCR-RFLP 159 230 79 76 107 116 
Hishida et al. [422011 Japan Asian Gastric cancer HB PCR-RFLP 583 1637 82 271 230 229 775 633 
Mittal et al. [432011 India Asian Bladder cancer PB PCR-RFLP 212 250 127 79 135 108 
Permuth-Wey et al. [442011 U.S.A. Caucasian Glioma PB GoldenGate 593 614 345 198 50 375 214 25 
Vinci et al. [452011 Italy Caucasian NSCLC PB HRMA 101 129 44 48 73 45 11 
Yue et al. [462011 China Asian Cervical cancer HB PCR-RFLP 447 443 118 224 105 87 206 150 
Zhang et al. [472011 China Asian Liver cancer HB PIRA-PCR 925 1593 156 450 319 291 725 577 
Zhou et al. [482011 China Asian CSCC HB PCR-RFLP 226 309 43 113 70 34 159 116 
Ma et al. [492012 China Asian Gastric cancer HB Sequencing 86 42 20 44 14 19 14 
Alshatwi et al. [502012 Saudi Asian Breast cancer PB TaqMan 100 100 50 48 46 51 
Chu et al. [512012 China Asian Oral cancer HB PCR-RFLP 470 425 54 242 174 54 196 175 
Hezova et al. [522012 Czech Caucasian Colorectal HB TaqMan 197 212 115 70 12 124 79 
Kim et al. [532012 Korea Asian Liver cancer PB PCR-RFLP 286 201 27 159 100 24 103 74 
Lung et al. [542012 China Asian Nasopharyngeal carcinoma PB Tm-shift 229 3631 24 88 117 497 1721 1413 
Mihalache et al. [552012 Italy and Germany Caucasian Cholangiocarcinoma HB TaqMan 182 350 118 53 11 211 122 17 
Min et al. [562012 Korea Asian Colorectal HB PCR-RFLP 446 502 62 233 151 69 245 188 
Wang et al. [572012 China Asian Bladder cancer HB TaqMan 1017 1179 369 456 192 340 571 268 
Xiang et al. [582012 China Asian Liver cancer HB PCR-RFLP 100 200 27 45 28 45 100 55 
Zhou et al. [592012 China Asian Liver cancer PB PCR-RFLP 186 483 33 86 67 71 254 158 
Zhou et al. [602012 China Asian Gastric cancer HB TaqMan 1686 1895 578 822 286 551 951 393 
Lv et al. [612013 China Asian Colorectal cancer PB PCR-RFLP 353 540 54 230 47 96 274 143 
Chae et al. [622013 Korea Asian Colorectal cancer PB PCR-RFLP 399 568 61 182 156 121 282 165 
Ma et al. [632013 China Asian TNBC HB massARRAY 192 191 35 94 63 34 93 64 
Ma et al. [642013 China Asian Colorectal cancer HB TaqMan 1147 1203 444 534 169 397 614 192 
Orsos et al. [652013 Hungary Caucasian SCCHN PB PCR-RFLP 468 468 284 168 16 323 136 
Vinci et al. [662013 Italy Caucasian Colorectal cancer PB HRMA 160 178 86 57 17 100 65 13 
Wei et al. [672013 China Asian PTC PB massARRAY 753 760 136 323 294 138 345 277 
Wei et al. [682013 China Asian ESCC HB massARRAY 368 370 67 184 117 67 181 122 
Yamashita et al. [692013 Japan Asian Melanoma PB PCR-RFLP 50 107 35 15 53 51 
Zhang et al. [702013 China Asian HCC PB MassARRAY 997 998 163 503 331 156 475 367 
Ahn et al. [712013 Korea Asian Gastric cancer HB PCR-RFLP 461 447 71 231 159 62 221 164 
Song et al. [722013 China Asian Gastric cancer HB PCR-RFLP 1208 1166 199 586 423 207 615 344 
Wu [732014 China Asian Colorectal cancer HB ASA 175 300 22 59 80 53 120 114 
Chu et al. [742014 China Asian HCC HB PCR-RFLP 188 337 22 82 84 50 145 141 
Cong et al. [752014 China Asian HCC HB PCR-RFLP 206 218 27 85 94 17 84 117 
Dikeakos et al. [762014 Greece Caucasian Gastric cancer HB PCR-RFLP 163 480 13 45 105 24 149 307 
Du et al. [772014 China Asian Renal HB TaqMan assay 353 362 68 167 118 57 190 115 
Hu et al. [782014 China Asian Colorectal HB PCR-RFLP 200 373 34 82 84 44 187 142 
Huang et al. [792014 China Asian Nasopharyngeal HB PCR-RFLP 160 200 23 73 64 36 110 54 
Jeon et al. [802014 Korea Asian Lung PB PCR-RFLP 1091 1096 223 500 368 244 540 312 
Jia et al. [812014 China Asian NSCLC HB PCR-RFLP 400 400 64 182 154 76 200 124 
Kupcinskas et al. [822014 Germany, Lithuania, Latvia Caucasian Gastric HB TaqMan assay 362 347 252 94 16 223 108 16 
Kupcinskas et al. [832014 Lithuania, Latvia Caucasian Colorectal HB TaqMan assay 192 424 140 50 275 134 15 
Mao et al. [842014 China Asian Colorectal PB SNPscan system 547 561 70 291 186 85 271 205 
Nikolić et al. [852014 Serbia Caucasian Prostate HB TaqMan assay 286 199 184 90 12 129 63 
Palmieri et al.1 [862014 Italy Caucasian OSCC HB TaqMan assay 337 88 197 121 19 50 31 
Palmieri et al.2 [862014 Italy Caucasian OSCC HB TaqMan assay 337 206 197 121 19 105 84 17 
Palmieri et al.3 [862014 Italy Caucasian OSCC HB TaqMan assay 337 543 197 121 19 297 206 40 
Parlayan et al.1 [872014 Japan Asian Gastric HB TaqMan assay 160 524 20 79 61 71 237 216 
Parlayan et al.2 [872014 Japan Asian Lung HB TaqMan assay 148 524 25 67 56 71 237 216 
Parlayan et al.3 [872014 Japan Asian Prostate HB TaqMan assay 89 524 11 41 37 71 237 216 
Pu et al. [882014 China Asian Gastric HB PCR-RFLP 197 513 36 96 65 96 274 143 
Qu et al. [892014 China Asian ESCC HB Allele-specific PCR 381 426 62 203 116 75 228 123 
Dikaiakos et al. [902015 Greece Caucasian Colorectal HB PCR-RFLP 157 299 48 101 21 120 158 
Gomez-Lira et al. [912015 Italy Caucasian Melanoma HB PCR-RFLP 224 264 107 100 17 149 105 10 
Qi et al. [922015 China Asian Breast cancer PB PCR-RFLP 321 290 146 132 43 126 144 20 
Zhu et al. [932015 China Asian ESCC HB PCR-RFLP 248 300 82 120 36 99 139 40 
Deng et al. [942015 China Asian Bladder cancer HB PCR-RFLP 159 258 26 73 60 32 154 112 
Li et al. [952015 China Asian HCC HB PCR-RFLP 266 266 151 86 29 166 81 19 
Shen et al. [962015 China Asian ESCC HB SNaPshot multiplex system 1400 2185 220 685 495 345 1060 780 
Yan et al. [972015 China Asian HCC HB PCR-RFLP 274 328 35 145 94 36 169 123 
Yin et al. [982015 China Asian Lung cancer HB PCR-RFLP 575 608 97 280 198 127 313 168 
Xia et al. [992015 China Asian Gastric cancer HB TaqMan assay 1125 1196 192 536 397 199 577 420 
Hashemi et al. [1002016 Iran Caucasian Prostate cancer HB T-ARMS-PCR assay 169 182 25 131 13 24 147 11 
Jiang et al. [1012016 China Asian Gastric cancer HB MassARRAY 898 992 154 441 303 207 457 325 
Miao et al. [1022016 China Asian HNSCC HB Illumina Infinium1 human exome BeadChip 576 1552 497 773 278 154 228 80 
Chen et al.1 [1032016 Taiwan Asian OSCC HB TaqMan assay 512 668 71 241 200 103 293 272 
Chen et al.2 [1032016 Taiwan Asian PSCC HB TaqMan assay 146 668 16 77 53 103 293 272 
Chen et al.3 [1032016 Taiwan Asian OPSCC HB TaqMan assay 658 668 87 318 253 103 293 272 
Reference Year Country Ethnicity Cancer type Control source Genotyping method Sample size Case Control 
       Cases Controls GG GC CC GG GC CC 
Horikawa et al. [242008 U.S.A. Caucasian Renal cell cancer PB SNPlex assay 261 235 144 103 14 126 94 15 
Jazdzewski et al.1 [252008 Finland Caucasian PTC PB SNPlex assay 206 274 99 104 150 105 19 
Jazdzewski et al.2 [252008 Poland Caucasian PTC PB SNPlex assay 201 475 115 82 286 163 26 
Jazdzewski et al.3 [252008 U.S.A. Caucasian PTC PB SNPlex assay 201 152 91 101 90 52 10 
Xu et al. [262008 China Asian Liver cancer HB PCR-RFLP 479 504 80 241 158 58 249 197 
Yang et al. [272008 U.S.A. Caucasian Bladder cancer PB SNPlex assay 691 674 414 242 35 385 258 31 
Hoffman et al. [282009 U.S.A. Caucasian Breast cancer PB massARRAY 439 478 234 176 29 273 178 27 
Hu et al. [292009 China Asian Breast cancer HB PCR-RFLP 1009 1093 165 515 329 180 551 362 
Tian et al. [302009 China Asian Lung cancer PB PCR-RFLP 1058 1035 360 510 188 364 502 169 
Catucci et al.1 [312010 Italy Caucasian Breast cancer HB Sequencing 754 1243 409 286 59 650 520 73 
Catucci et al.2 [312010 Germany Caucasian Breast cancer HB Sequencing 805 904 451 304 50 536 318 50 
Guo et al. [322010 China Caucasian ESCC PB SNaPshot 444 468 234 190 20 206 220 42 
Liu et al. [332010 U.S.A. Mixed SCCHN HB PCR-RFLP 1109 1130 630 411 68 655 405 70 
Okubo et al. [342010 Japan Asian Gastric cancer HB PCR-RFLP 552 697 73 243 236 121 322 254 
Pastrello et al. [352010 Italy Caucasian Breast and ovarian cancer PB Sequencing 101 155 60 36 90 59 
Srivastava et al. [362010 India Asian Gall bladder cancer PB PCR-RFLP 230 224 129 90 11 138 81 
Xu et al. [372010 China Asian Prostate cancer HB PCR-RFLP 251 280 68 135 48 54 150 76 
Zeng et al. [382010 China Asian Gastric cancer HB PCR-RFLP 304 304 62 153 89 53 132 119 
Akkiz et al. [392011 Turkey Caucasian Liver cancer HB PCR-RFLP 222 222 137 75 10 144 67 11 
Garcia et al. [402011 French Caucasian Breast cancer PB TaqMan 1130 596 676 388 66 352 220 24 
George et al. [412011 India Asian Prostate cancer PB PCR-RFLP 159 230 79 76 107 116 
Hishida et al. [422011 Japan Asian Gastric cancer HB PCR-RFLP 583 1637 82 271 230 229 775 633 
Mittal et al. [432011 India Asian Bladder cancer PB PCR-RFLP 212 250 127 79 135 108 
Permuth-Wey et al. [442011 U.S.A. Caucasian Glioma PB GoldenGate 593 614 345 198 50 375 214 25 
Vinci et al. [452011 Italy Caucasian NSCLC PB HRMA 101 129 44 48 73 45 11 
Yue et al. [462011 China Asian Cervical cancer HB PCR-RFLP 447 443 118 224 105 87 206 150 
Zhang et al. [472011 China Asian Liver cancer HB PIRA-PCR 925 1593 156 450 319 291 725 577 
Zhou et al. [482011 China Asian CSCC HB PCR-RFLP 226 309 43 113 70 34 159 116 
Ma et al. [492012 China Asian Gastric cancer HB Sequencing 86 42 20 44 14 19 14 
Alshatwi et al. [502012 Saudi Asian Breast cancer PB TaqMan 100 100 50 48 46 51 
Chu et al. [512012 China Asian Oral cancer HB PCR-RFLP 470 425 54 242 174 54 196 175 
Hezova et al. [522012 Czech Caucasian Colorectal HB TaqMan 197 212 115 70 12 124 79 
Kim et al. [532012 Korea Asian Liver cancer PB PCR-RFLP 286 201 27 159 100 24 103 74 
Lung et al. [542012 China Asian Nasopharyngeal carcinoma PB Tm-shift 229 3631 24 88 117 497 1721 1413 
Mihalache et al. [552012 Italy and Germany Caucasian Cholangiocarcinoma HB TaqMan 182 350 118 53 11 211 122 17 
Min et al. [562012 Korea Asian Colorectal HB PCR-RFLP 446 502 62 233 151 69 245 188 
Wang et al. [572012 China Asian Bladder cancer HB TaqMan 1017 1179 369 456 192 340 571 268 
Xiang et al. [582012 China Asian Liver cancer HB PCR-RFLP 100 200 27 45 28 45 100 55 
Zhou et al. [592012 China Asian Liver cancer PB PCR-RFLP 186 483 33 86 67 71 254 158 
Zhou et al. [602012 China Asian Gastric cancer HB TaqMan 1686 1895 578 822 286 551 951 393 
Lv et al. [612013 China Asian Colorectal cancer PB PCR-RFLP 353 540 54 230 47 96 274 143 
Chae et al. [622013 Korea Asian Colorectal cancer PB PCR-RFLP 399 568 61 182 156 121 282 165 
Ma et al. [632013 China Asian TNBC HB massARRAY 192 191 35 94 63 34 93 64 
Ma et al. [642013 China Asian Colorectal cancer HB TaqMan 1147 1203 444 534 169 397 614 192 
Orsos et al. [652013 Hungary Caucasian SCCHN PB PCR-RFLP 468 468 284 168 16 323 136 
Vinci et al. [662013 Italy Caucasian Colorectal cancer PB HRMA 160 178 86 57 17 100 65 13 
Wei et al. [672013 China Asian PTC PB massARRAY 753 760 136 323 294 138 345 277 
Wei et al. [682013 China Asian ESCC HB massARRAY 368 370 67 184 117 67 181 122 
Yamashita et al. [692013 Japan Asian Melanoma PB PCR-RFLP 50 107 35 15 53 51 
Zhang et al. [702013 China Asian HCC PB MassARRAY 997 998 163 503 331 156 475 367 
Ahn et al. [712013 Korea Asian Gastric cancer HB PCR-RFLP 461 447 71 231 159 62 221 164 
Song et al. [722013 China Asian Gastric cancer HB PCR-RFLP 1208 1166 199 586 423 207 615 344 
Wu [732014 China Asian Colorectal cancer HB ASA 175 300 22 59 80 53 120 114 
Chu et al. [742014 China Asian HCC HB PCR-RFLP 188 337 22 82 84 50 145 141 
Cong et al. [752014 China Asian HCC HB PCR-RFLP 206 218 27 85 94 17 84 117 
Dikeakos et al. [762014 Greece Caucasian Gastric cancer HB PCR-RFLP 163 480 13 45 105 24 149 307 
Du et al. [772014 China Asian Renal HB TaqMan assay 353 362 68 167 118 57 190 115 
Hu et al. [782014 China Asian Colorectal HB PCR-RFLP 200 373 34 82 84 44 187 142 
Huang et al. [792014 China Asian Nasopharyngeal HB PCR-RFLP 160 200 23 73 64 36 110 54 
Jeon et al. [802014 Korea Asian Lung PB PCR-RFLP 1091 1096 223 500 368 244 540 312 
Jia et al. [812014 China Asian NSCLC HB PCR-RFLP 400 400 64 182 154 76 200 124 
Kupcinskas et al. [822014 Germany, Lithuania, Latvia Caucasian Gastric HB TaqMan assay 362 347 252 94 16 223 108 16 
Kupcinskas et al. [832014 Lithuania, Latvia Caucasian Colorectal HB TaqMan assay 192 424 140 50 275 134 15 
Mao et al. [842014 China Asian Colorectal PB SNPscan system 547 561 70 291 186 85 271 205 
Nikolić et al. [852014 Serbia Caucasian Prostate HB TaqMan assay 286 199 184 90 12 129 63 
Palmieri et al.1 [862014 Italy Caucasian OSCC HB TaqMan assay 337 88 197 121 19 50 31 
Palmieri et al.2 [862014 Italy Caucasian OSCC HB TaqMan assay 337 206 197 121 19 105 84 17 
Palmieri et al.3 [862014 Italy Caucasian OSCC HB TaqMan assay 337 543 197 121 19 297 206 40 
Parlayan et al.1 [872014 Japan Asian Gastric HB TaqMan assay 160 524 20 79 61 71 237 216 
Parlayan et al.2 [872014 Japan Asian Lung HB TaqMan assay 148 524 25 67 56 71 237 216 
Parlayan et al.3 [872014 Japan Asian Prostate HB TaqMan assay 89 524 11 41 37 71 237 216 
Pu et al. [882014 China Asian Gastric HB PCR-RFLP 197 513 36 96 65 96 274 143 
Qu et al. [892014 China Asian ESCC HB Allele-specific PCR 381 426 62 203 116 75 228 123 
Dikaiakos et al. [902015 Greece Caucasian Colorectal HB PCR-RFLP 157 299 48 101 21 120 158 
Gomez-Lira et al. [912015 Italy Caucasian Melanoma HB PCR-RFLP 224 264 107 100 17 149 105 10 
Qi et al. [922015 China Asian Breast cancer PB PCR-RFLP 321 290 146 132 43 126 144 20 
Zhu et al. [932015 China Asian ESCC HB PCR-RFLP 248 300 82 120 36 99 139 40 
Deng et al. [942015 China Asian Bladder cancer HB PCR-RFLP 159 258 26 73 60 32 154 112 
Li et al. [952015 China Asian HCC HB PCR-RFLP 266 266 151 86 29 166 81 19 
Shen et al. [962015 China Asian ESCC HB SNaPshot multiplex system 1400 2185 220 685 495 345 1060 780 
Yan et al. [972015 China Asian HCC HB PCR-RFLP 274 328 35 145 94 36 169 123 
Yin et al. [982015 China Asian Lung cancer HB PCR-RFLP 575 608 97 280 198 127 313 168 
Xia et al. [992015 China Asian Gastric cancer HB TaqMan assay 1125 1196 192 536 397 199 577 420 
Hashemi et al. [1002016 Iran Caucasian Prostate cancer HB T-ARMS-PCR assay 169 182 25 131 13 24 147 11 
Jiang et al. [1012016 China Asian Gastric cancer HB MassARRAY 898 992 154 441 303 207 457 325 
Miao et al. [1022016 China Asian HNSCC HB Illumina Infinium1 human exome BeadChip 576 1552 497 773 278 154 228 80 
Chen et al.1 [1032016 Taiwan Asian OSCC HB TaqMan assay 512 668 71 241 200 103 293 272 
Chen et al.2 [1032016 Taiwan Asian PSCC HB TaqMan assay 146 668 16 77 53 103 293 272 
Chen et al.3 [1032016 Taiwan Asian OPSCC HB TaqMan assay 658 668 87 318 253 103 293 272 

Abbreviations: BC, breast cancer; CRC, colorectal cancer; GC, gastric cancer; ESCC,esophageal squamous cell carcinoma; HB, hospital-based; HCC, hepatocellular carcinoma; HNSCC, squamous cell carcinoma of the head and neck; HRMA, high resolution melting analysis; LC, lung cancer; NSCLC, non-small-cell lung carcinoma; OPSCC, squamous cell carcinoma of the oral cavity, oropharynx, and hypopharynx; OSCC, oral squamous cell carcinoma; PB, population-based; Phwe, P-value of HWE; PSCC, squamous cell carcinoma of the oropharynx and hypopharynx; PTC, papillary thyroid cancer; RFLP, restriction fragment length polymorphism; SCCHN, squamous cell carcinoma of head and neck; TNBC, triple negative breast cancer.

1,2,3The superscript values 1, 2 and 3, indicate the number of studies (one, two and three respectively) covered the published article.

miR-146a C/G polymorphism and HNC risk

In the overall analysis, we pooled 13 separate studies to explore the association between miR-146a C/G polymorphism and the risk of HNC under homozygote, heterozygote, recessive, and allele comparison model. There is no significant association between miR-146a C/G polymorphism and the risk of HNC under homozygote model (I2 =21.6%, Pheterogeneity=0.226, OR =1.113, 95% CI =0.980–1.263, P=0.099, Figure 2), heterozygote model (I2 =14.2%, Pheterogeneity=0.301, OR =1.084, 95% CI =0.991–1.186, P=0.079, Figure 3), recessive model (I2 =66.3%, Pheterogeneity<0.01, OR =1.068, 95% CI =0.896–1.272, P=0.465, Figure 4), and allele comparison model (I2 =61%, Pheterogeneity=0.002, OR =1.061, 95% CI =0.966–1.166, P=0.214, Figure 5). Furthermore, we pooled all 14 eligible studies to explore the association between pre-miR-146a C/G polymorphism and the risk of HNC. Significant association was found under dominant model (I2 =15.6%, Pheterogeneity=0.282, OR =1.088, 95% CI =1.002–1.182, P=0.044, Figure 6). In the subgroup analysis by ethnicity, no significant association was found amongst Caucasians under homozygote model (I2 =36.7%, Pheterogeneity=0.177, OR =0.919, 95% CI =0.716–1.180, P=0.509, Table 2), heterozygote model (I2 =52.7%, Pheterogeneity=0.076, OR =1.040, 95% CI =0.922–1.173, P=0.521, Table 2), dominant model (I2 =58.6%, Pheterogeneity=0.034, OR =1.027, 95% CI =0.857–1.232, P=0.772, Table 2), recessive model (I2 =10.9%, Pheterogeneity=0.344, OR =0.919, 95% CI =0.719–1.174, P=0.449, Table 2), and allele comparison model (I2 =69%, Pheterogeneity=0.012, OR =0.981, 95% CI =0.814–1.183, P=0.843, Table 2). Simultaneously, no associations were detected amongst Asians under heterozygote model (I2 =0, Pheterogeneity=0.713, OR =1.142, 95% CI =0.997–1.308, P=0.054, Table 2), recessive model (I2 =76.5, Pheterogeneity<0.01, OR =1.133, 95% CI =0.914–1.404, P=0.254, Table 2), and allele comparison model (I2 =57.6, Pheterogeneity=0.021, OR =1.103, 95% CI =0.988–1.233, P=0.082, Table 2), while slight association was found amongst Asians under homozygote model (I2 =0, Pheterogeneity=0.541, OR =1.189, 95% CI =1.025–1.378, P=0.022, Table 2) and dominant model (I2 =0, Pheterogeneity=0.959, OR =1.155, 95% CI =1.016–1.312, P=0.028, Table 2). In the stratified analysis by source of controls, a significantly increased cancer risk amongst population-based studies was found under homozygote model (I2 =0, Pheterogeneity=0.855, OR =1.668, 95% CI =1.183–2.352, P=0.004, Table 2), dominant model (I2 =0, Pheterogeneity=0.674, OR =1.359, 95% CI =1.095–1.687, P=0.005, Table 2), recessive model (I2 =0, Pheterogeneity=0.874, OR =1.697, 95% CI =1.367–2.107, P<0.001, Table 2), and allele comparison model (I2 =0, Pheterogeneity=0.991, OR =1.394, 95% CI =1.215–1.599, P<0.001, Table 2), while no association was found amongst population-based studies under heterozygote model (I2 =3.5%, Pheterogeneity=0.408, OR =1.219, 95% CI =0.974–1.526, P=0.083, Table 2). Meanwhile, no significant association was found amongst hospital-based studies under homozygote model (I2 =0, Pheterogeneity=0.471, OR =1.113, 95% CI =0.980–1.263, P=0.603, Table 2), heterozygote model (I2 =40.5%, Pheterogeneity=0.186, OR =1.060, 95% CI =0.961–1.169, P=0.248, Table 2), dominant model (I2 =0, Pheterogeneity=0.462, OR =1.047, 95% CI =0.957–1.144, P=0.318, Table 2), recessive model (I2 =26%, Pheterogeneity=0.204, OR =0.941, 95% CI =0.849–1.043, P=0.247, Table 2), and allele comparison model (I2 =19.8%, Pheterogeneity=0.261, OR =0.994, 95% CI =0.935–1.056, P=0.837, Table 2). Results of the meta-analyses are presented in Table 2.

Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under homozygote model)

Figure 2
Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under homozygote model)
Figure 2
Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under homozygote model)

Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under heterozygote model)

Figure 3
Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under heterozygote model)
Figure 3
Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under heterozygote model)

Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under recessive model)

Figure 4
Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under recessive model)
Figure 4
Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under recessive model)

Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under allele comparison model)

Figure 5
Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under allele comparison model)
Figure 5
Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under allele comparison model)

Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under dominant model)

Figure 6
Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under dominant model)
Figure 6
Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under dominant model)
Table 2
Meta-analysis on the association between miR-146a rs2910164 polymorphism and HNC risk
Variables Study number Statistic model Test of heterogeneity Test of association Publication bias 
   P I2 OR (95% CI) P PBegg’s PEgger’s 
Homozygote model 
Total 13 Fixed 0.226 21.6 1.113 (0.980–1.263) 0.099 1.000 0.793 
Ethnicity 
Caucasian Fixed 0.177 36.7 0.919 (0.716–1.180) 0.509   
Asian Fixed 0.541 1.189 (1.025–1.378) 0.022   
Source of control 
Population-based study Fixed 0.855 1.668 (1.183–2.352) 0.004   
Hospital-based study 10 Fixed 0.471 1.113 (0.980–1.263) 0.603   
Heterozygote model 
Total 13 Fixed 0.301 14.2 1.084 (0.991–1.186) 0.079 0.855 0.968 
Ethnicity 
Caucasian Fixed 0.076 52.7 1.040 (0.922–1.173) 0.521   
Asian Fixed 0.713 1.142 (0.997–1.308) 0.054   
Source of control 
Population-based study Fixed 0.408 3.5 1.219 (0.974–1.526) 0.083   
Hospital-based study 10 Fixed 0.186 40.5 1.060 (0.961–1.169) 0.248   
Dominant model 
Total 14 Fixed 0.282 15.6 1.088 (1.002–1.182) 0.044 0.661 0.549 
Ethnicity 
Caucasian Random 0.034 58.6 1.027 (0.857–1.232) 0.772   
Asian Fixed 0.959 1.155 (1.016–1.312) 0.028   
Source of control 
Population-based study  Fixed 0.674 1.359 (1.095–1.687) 0.005   
Hospital-based study  Fixed 0.462 1.047 (0.957–1.144) 0.318   
Recessive model 
Total 13 Random <0.01 66.3 1.068 (0.896–1.272) 0.465 0.76 0.784 
Ethnicity 
Caucasian Fixed 0.344 10.9 0.919 (0.719–1.174) 0.449   
Asian Random <0.01 76.5 1.133 (0.914–1.404) 0.254   
Source of control 
Population-based study Fixed 0.874 1.697 (1.367–2.107) <0.001   
Hospital-based study 10 Fixed 0.204 26 0.941 (0.849–1.043) 0.247   
Allele comparison model 
Total 13 Random 0.002 61 1.061 (0.966–1.166) 0.214 0.855 0.587 
Ethnicity 
Caucasian Random 0.012 69 0.981 (0.814–1.183) 0.843   
Asian Random 0.021 57.6 1.103 (0.988–1.233) 0.082   
Source of control 
Population-based study Fixed 0.991 1.394 (1.215–1.599) <0.001   
Hospital-based study 10 Fixed 0.261 19.8 0.994 (0.935–1.056) 0.837   
Variables Study number Statistic model Test of heterogeneity Test of association Publication bias 
   P I2 OR (95% CI) P PBegg’s PEgger’s 
Homozygote model 
Total 13 Fixed 0.226 21.6 1.113 (0.980–1.263) 0.099 1.000 0.793 
Ethnicity 
Caucasian Fixed 0.177 36.7 0.919 (0.716–1.180) 0.509   
Asian Fixed 0.541 1.189 (1.025–1.378) 0.022   
Source of control 
Population-based study Fixed 0.855 1.668 (1.183–2.352) 0.004   
Hospital-based study 10 Fixed 0.471 1.113 (0.980–1.263) 0.603   
Heterozygote model 
Total 13 Fixed 0.301 14.2 1.084 (0.991–1.186) 0.079 0.855 0.968 
Ethnicity 
Caucasian Fixed 0.076 52.7 1.040 (0.922–1.173) 0.521   
Asian Fixed 0.713 1.142 (0.997–1.308) 0.054   
Source of control 
Population-based study Fixed 0.408 3.5 1.219 (0.974–1.526) 0.083   
Hospital-based study 10 Fixed 0.186 40.5 1.060 (0.961–1.169) 0.248   
Dominant model 
Total 14 Fixed 0.282 15.6 1.088 (1.002–1.182) 0.044 0.661 0.549 
Ethnicity 
Caucasian Random 0.034 58.6 1.027 (0.857–1.232) 0.772   
Asian Fixed 0.959 1.155 (1.016–1.312) 0.028   
Source of control 
Population-based study  Fixed 0.674 1.359 (1.095–1.687) 0.005   
Hospital-based study  Fixed 0.462 1.047 (0.957–1.144) 0.318   
Recessive model 
Total 13 Random <0.01 66.3 1.068 (0.896–1.272) 0.465 0.76 0.784 
Ethnicity 
Caucasian Fixed 0.344 10.9 0.919 (0.719–1.174) 0.449   
Asian Random <0.01 76.5 1.133 (0.914–1.404) 0.254   
Source of control 
Population-based study Fixed 0.874 1.697 (1.367–2.107) <0.001   
Hospital-based study 10 Fixed 0.204 26 0.941 (0.849–1.043) 0.247   
Allele comparison model 
Total 13 Random 0.002 61 1.061 (0.966–1.166) 0.214 0.855 0.587 
Ethnicity 
Caucasian Random 0.012 69 0.981 (0.814–1.183) 0.843   
Asian Random 0.021 57.6 1.103 (0.988–1.233) 0.082   
Source of control 
Population-based study Fixed 0.991 1.394 (1.215–1.599) <0.001   
Hospital-based study 10 Fixed 0.261 19.8 0.994 (0.935–1.056) 0.837   

Values of P<0.05 were considered statistically significant.

miR-146a C/G polymorphism and overall cancer risk

Furthermore, we explored the association between the pre-miR-146a C/G polymorphism and overall cancer risk. We first analyzed the heterogeneity by Q-test and I-squared in any of the genetic models. Significant statistical heterogeneity was identified in the homozygote model (I2 =57.1%, Pheterogneity<0.001), heterozygote model (I2 =55.1%, Pheterogneity<0.001), dominant model (I2 =46.4%, Pheterogneity<0.001), recessive model (I2 =60.9%, Pheterogneity<0.001), and allele comparison model (I2 =58.8%, Pheterogneity<0.001), so that random-effects model was used in all genetic models. Overall, significant association was not identified in all genetic models (homozygote model: OR =1.005, 95% CI =0.931–1.084, P=0901, Figure 7; heterozygote model: OR =1.009, 95% CI =0.951–1.070, P=0.766, Figure 8; dominant model: OR =0.998, 95% CI =0.951–1.047, P=0.932, Figure 9; recessive model: OR =1.005, 95% CI =0.946–1.066, P=0.880, Figure 10, and allele comparison model: OR =0.999, 95% CI =0.965–1.035, P=0.970, Figure 11). Subgroup analysis was performed according to ethnicity. The same result was found, that is, no significant association was detected in all genetic models amongst Caucasians, Asians, and mixed populations. All the results are listed in Table 3.

Forest plot of the association between miR-146a rs2910164 polymorphism and overall risk (under homozygote model)

Figure 7
Forest plot of the association between miR-146a rs2910164 polymorphism and overall risk (under homozygote model)
Figure 7
Forest plot of the association between miR-146a rs2910164 polymorphism and overall risk (under homozygote model)

Forest plot of the association between miR-146a rs2910164 polymorphism and overall cancer risk (under heterozygote model)

Figure 8
Forest plot of the association between miR-146a rs2910164 polymorphism and overall cancer risk (under heterozygote model)
Figure 8
Forest plot of the association between miR-146a rs2910164 polymorphism and overall cancer risk (under heterozygote model)

Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under dominant model)

Figure 9
Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under dominant model)
Figure 9
Forest plot of the association between miR-146a rs2910164 polymorphism and HNC risk (under dominant model)

Forest plot of the association between miR-146a rs2910164 polymorphism and overall cancer risk (under recessive model)

Figure 10
Forest plot of the association between miR-146a rs2910164 polymorphism and overall cancer risk (under recessive model)
Figure 10
Forest plot of the association between miR-146a rs2910164 polymorphism and overall cancer risk (under recessive model)

Forest plot of the association between miR-146a rs2910164 polymorphism and overall cancer risk (under allele comparison model)

Figure 11
Forest plot of the association between miR-146a rs2910164 polymorphism and overall cancer risk (under allele comparison model)
Figure 11
Forest plot of the association between miR-146a rs2910164 polymorphism and overall cancer risk (under allele comparison model)
Table 3
Meta-analysis on the association between miR-146a rs2910164 polymorphism and overall cancer risk
Variables Study number Statistic model Test of heterogeneity Test of association Publication bias 
   P I2 OR (95% CI) P PBegg’s PEgger’s 
Homozygote model 
Total 89 Random <0.001 57.1 1.005 (0.931–1.084) 0.901 0.568 0.889 
Ethnicity 
Caucasian 28 Random 0.004 46.9 0.919 (0.716–1.180) 0.756   
Asian 60 Random <0.001 61.4 0.995 (0.915–1.083) 0.913   
Mixed population Random 1.01 (0.711–1.435) 0.956   
Source of control 
Population-based study 29 Random <0.001 54.6 1.134 (0.972–1.323) 0.109   
Hospital-based study 60 Random <0.001 55.4 0.960 (0.882–1.045) 0.347   
Heterozygote model 
Total 89 Random <0.001 55.1 1.009 (0.951–1.070) 0.766 0.918 0.836 
Ethnicity 
Caucasian 28 Random 0.01 42.7 1.072 (0.902–1.273) 0.430   
Asian 60 Random <0.001 59.3 0.994 (0.934–1.057) 0.839   
Mixed population Random 0.957 (0.667–1.373) 0.812   
Source of control 
Population-based study 29 Random <0.001 72.9 1.013 (0.863–1.187) 0.878   
Hospital-based study 60 Random 0.005 35 0.997 (0.944–1.052) 0.906   
Dominant model 
Total 89 Random <0.001 46.4 0.998 (0.951–1.047) 0.932 0.632 0.349 
Ethnicity 
Caucasian 28 Random 0.003 48 1.012 (0.929–1.104) 0.781   
Asian 60 Random <0.001 46.9 0.989 (0.932–1.051) 0.731   
Mixed population Random 1.048 (0.887–1.240) 0.580   
Source of control 
Population-based study 29 Random 0.034 35.1 1.083 (0.983–1.168) 0.420   
Hospital-based study 60 Random <0.001 46.7 0.957 (0.903–1.015) 0.143   
Recessive model 
Total 89 Random <0.001 60.9 1.005 (0.946–1.066) 0.880 0.975 0.817 
Ethnicity 
Caucasian 28 Random 0.034 35.1 1.083 (1.003–1.168) 0.467   
Asian 60 Random <0.001 46.7 0.957 (0.903–1.015) 0.743   
Mixed population Random 0.989 (0.701–1.396) 0.951   
Source of control 
Population-based study 29 Random <0.001 72.3 1.041 (0.895–1.210) 0.605   
Hospital-based study 60 Random <0.001 50.3 0.986 (0.929–1.046) 0.643   
Allele comparison model 
Total 89 Random <0.001 60.8 0.999 (0.965–1.035) 0.970 0.790 0.757 
Ethnicity 
Caucasian 28 Random 0.002 49.8 1.022 (0.954–1.095) 0.542   
Asian 60 Random <0.001 65.1 0.991 (0.950–1.032) 0.655   
Mixed population Random – – 1.030 (0.899–1.181) 0.670   
Source of control 
Population-based study 29 Random <0.001 57.7 1.053 (0.988–1.122) 0.112   
Hospital-based study 60 Random <0.001 60.1 0.977 (0.938–1.017) 0.252   
Variables Study number Statistic model Test of heterogeneity Test of association Publication bias 
   P I2 OR (95% CI) P PBegg’s PEgger’s 
Homozygote model 
Total 89 Random <0.001 57.1 1.005 (0.931–1.084) 0.901 0.568 0.889 
Ethnicity 
Caucasian 28 Random 0.004 46.9 0.919 (0.716–1.180) 0.756   
Asian 60 Random <0.001 61.4 0.995 (0.915–1.083) 0.913   
Mixed population Random 1.01 (0.711–1.435) 0.956   
Source of control 
Population-based study 29 Random <0.001 54.6 1.134 (0.972–1.323) 0.109   
Hospital-based study 60 Random <0.001 55.4 0.960 (0.882–1.045) 0.347   
Heterozygote model 
Total 89 Random <0.001 55.1 1.009 (0.951–1.070) 0.766 0.918 0.836 
Ethnicity 
Caucasian 28 Random 0.01 42.7 1.072 (0.902–1.273) 0.430   
Asian 60 Random <0.001 59.3 0.994 (0.934–1.057) 0.839   
Mixed population Random 0.957 (0.667–1.373) 0.812   
Source of control 
Population-based study 29 Random <0.001 72.9 1.013 (0.863–1.187) 0.878   
Hospital-based study 60 Random 0.005 35 0.997 (0.944–1.052) 0.906   
Dominant model 
Total 89 Random <0.001 46.4 0.998 (0.951–1.047) 0.932 0.632 0.349 
Ethnicity 
Caucasian 28 Random 0.003 48 1.012 (0.929–1.104) 0.781   
Asian 60 Random <0.001 46.9 0.989 (0.932–1.051) 0.731   
Mixed population Random 1.048 (0.887–1.240) 0.580   
Source of control 
Population-based study 29 Random 0.034 35.1 1.083 (0.983–1.168) 0.420   
Hospital-based study 60 Random <0.001 46.7 0.957 (0.903–1.015) 0.143   
Recessive model 
Total 89 Random <0.001 60.9 1.005 (0.946–1.066) 0.880 0.975 0.817 
Ethnicity 
Caucasian 28 Random 0.034 35.1 1.083 (1.003–1.168) 0.467   
Asian 60 Random <0.001 46.7 0.957 (0.903–1.015) 0.743   
Mixed population Random 0.989 (0.701–1.396) 0.951   
Source of control 
Population-based study 29 Random <0.001 72.3 1.041 (0.895–1.210) 0.605   
Hospital-based study 60 Random <0.001 50.3 0.986 (0.929–1.046) 0.643   
Allele comparison model 
Total 89 Random <0.001 60.8 0.999 (0.965–1.035) 0.970 0.790 0.757 
Ethnicity 
Caucasian 28 Random 0.002 49.8 1.022 (0.954–1.095) 0.542   
Asian 60 Random <0.001 65.1 0.991 (0.950–1.032) 0.655   
Mixed population Random – – 1.030 (0.899–1.181) 0.670   
Source of control 
Population-based study 29 Random <0.001 57.7 1.053 (0.988–1.122) 0.112   
Hospital-based study 60 Random <0.001 60.1 0.977 (0.938–1.017) 0.252   

Publication bias

Egger’s test and Begg’s test were used to investigate the publication bias in the literature in all the genetic models. No publication bias was detected by Begg’s and Egger’s tests. The shapes of the funnel plots (not shown) did not identify obvious asymmetry in any of the comparison models, and plot symmetries are evidenced by P-values greater than 0.05. Accordingly, no publication bias was evident in the meta-analysis (Tables 2 and 3).

Sensitivity analysis

We performed sensitivity analysis by sequential omission of individual studies, and the results showed that the significance of the pooled ORs for miR-146a rs2910164 polymorphism was not excessively influenced, suggesting the stability and reliability of the results in the present meta-analysis (not shown).

Discussion

It is well known that genetic mutations are responsible for cancer occurrence [104]. SNPs, as the most common genetic sequence variation, could affect the function of a series of miRNAs by altering the formation of the primary transcript, miRNA maturation, or miRNA–mRNA interactions [105,106]. Thus, genetic susceptibility to cancer, particularly from SNPs, has been a research focus in the scientific community. Previously, variations of the pre-miR-146a C/G gene have drawn increasing attention in cancer etiologies, and altered expression levels have been observed in inflammatory diseases as well as in cancers [107,108]. The results of the present meta-analysis confirm that miR-146a C/G polymorphism is associated with HNC risk. This risk is significant amongst the individuals with a dominant genotype model. In the stratified analysis by ethnicity, significant analysis was detected amongst Asians under homozygote and dominant model, while no association was found amongst Caucasians under all genetic models. Furthermore, significant association was found in population-based studies under homozygote, dominant, recessive, and allele comparison models. However, no significant association was detected in hospital-based studies under all genetic models. Moreover, no significant association was found between this gene polymorphism and overall cancer risk. Furthermore, in the stratified analyses by ethnicity and source of control, no significant association was detected in the subgroup analyses of source of control.

To the best of our knowledge, the present study is the first and most comprehensive one to date to assess the relationship between miR-146a C/G polymorphism and HNC risk, and the most comprehensive one to date to explore the association between this gene polymorphism and overall cancer risk. Nevertheless, our meta-analysis also has some limitations common to these types of studies. First, relatively large heterogeneity was observed across all the studies involved despite the use of strict criteria for study inclusion and precise data extraction. So, we performed subgroup analyses to explore the possible source of heterogeneity. Second, the majority of subjects included in this meta-analysis were mainly Caucasians and Asians. Thus, the inherent genetic and geographic differences require more data from different ethnic group to increase the statistical power. Third, the low sample size in some of the included studies likely influences the statistical power for evaluating the association between the miR-146a C/G polymorphism and HNC risk, especially in subgroup analyses. Fourth, lack of original data from the reviewed studies limited our further evaluation of potential interactions, considering that gene-to-gene and gene-to-environment interactions might modulate cancer risk. As a result, a more precise analysis stratified by variable host factors could be performed. Last, although the results for publication bias were not statistically significant, publication bias may still exist, because only published studies were included in this meta-analysis.

In conclusion, the meta-analysis presented here indicates that miR-146a C/G polymorphism more is likely contribute to the susceptibility to HNC, and overall cancer risk. Further well-designed studies with large sample size are needed to confirm these findings.

Author contribution

Xiaolei Z. contributed to the study design. D.S. and Xiaoyan Z. contributed to the literature search, data extraction. and the assessment of methodology quality. D.S. contributed to the statistical analysis and drafting of the manuscript. Xiaolei Z. contributed to the revising of the manuscript. All authors approved the final version of manuscript.

Competing interests

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

Funding

The authors declare that there are no sources of funding to be acknowledged.

Abbreviations

     
  • CI

    confidence interval

  •  
  • HNC

    head and neck cancer

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • OR

    odds ratio

  •  
  • SNP

    single nucleotide polymorphism

References

References
1
Specenier
P.
and
Vermorken
J.B.
(
2013
)
Cetuximab: its unique place in head and neck cancer treatment
.
Biologics
7
,
77
90
[PubMed]
2
Liao
C.T.
,
Wallace
C.G.
,
Lee
L.Y.
et al
(
2014
)
Clinical evidence of field cancerization in patients with oral cavity cancer in a betel quid chewing area
.
Oral Oncol.
50
,
721
731
[PubMed]
3
Machiels
J.P.
,
Lambrecht
M.
,
Hanin
F.X.
et al
(
2014
)
Advances in the management of squamous cell carcinoma of the head and neck
.
F1000Prime Rep.
6
,
44
[PubMed]
4
Bartel
D.P.
(
2004
)
MicroRNAs: genomics, biogenesis, mechanism, and function
.
Cell
116
,
281
297
[PubMed]
5
Esquela-Kerscher
A.
and
Slack
F.J.
(
2006
)
Oncomirs – microRNAs with a role in cancer
.
Nat. Rev. Cancer
6
,
259
269
[PubMed]
6
Berezikov
E.
,
Guryev
V.
,
van de Belt
J.
et al
(
2005
)
Phylogenetic shadowing and computational identification of human microRNA genes
.
Cell
120
,
21
24
[PubMed]
7
Chen
C.Z.
(
2005
)
MicroRNAs as oncogenes and tumor suppressors
.
N. Engl. J. Med.
353
,
1768
1771
[PubMed]
8
Hui
A.
,
How
C.
,
Ito
E.
et al
(
2011
)
Micro-RNAs as diagnostic or prognostic markers in human epithelial malignancies
.
BMC Cancer
11
,
500
[PubMed]
9
Qu
K.Z.
,
Zhang
K.
,
Li
H.
et al
(
2011
)
Circulating microRNAs as biomarkers for hepatocellular carcinoma
.
J. Clin. Gastroenterol.
45
,
355
360
[PubMed]
10
Yoon
S.O.
,
Chun
S.M.
,
Han
E.H.
et al
(
2011
)
Deregulated expression of microRNA-221 with the potential for prognostic biomarkers in surgically resected hepatocellular carcinoma
.
Hum. Pathol.
42
,
1391
1400
[PubMed]
11
Ji
J.
,
Shi
J.
,
Budhu
A.
et al
(
2009
)
MicroRNA expression, survival, and response to interferon in liver cancer
.
N. Engl. J. Med.
361
,
1437
1447
[PubMed]
12
Shastry
B.S.
(
2009
)
SNPs: impact on gene function and phenotype
.
Methods Mol. Biol.
578
,
3
22
[PubMed]
13
Hu
Z.
,
Liang
J.
,
Wang
Z.
et al
(
2009
)
Common genetic variants in premicroRNAs were associated with increased risk of breast cancer in Chinese women
.
Hum. Mutat.
30
,
79
84
[PubMed]
14
Hu
Z.
,
Chen
J.
,
Tian
T.
et al
(
2008
)
Genetic variants of miRNA sequences and non-small cell lung cancer survival
.
J. Clin. Invest.
118
,
2600
2608
[PubMed]
15
Slaby
O.
,
Bienertova-Vasku
J.
,
Svoboda
M.
et al
(
2012
)
Genetic polymorphisms and microRNAs: new direction in molecular epidemiology of solid cancer
.
J. Cell. Mol. Med.
16
,
8
21
[PubMed]
16
Landau
D.A.
and
Slack
F.J.
(
2011
)
MicroRNAs in mutagenesis, genomic instability, and DNA repair
.
Semin. Oncol.
38
,
743
751
[PubMed]
17
Ntais
C.
,
Polycarpou
A.
and
Ioannidis
J.P.
(
2004
)
Meta-analysis of the association of the cathepsin D Ala224Val gene polymorphism with the risk of Alzheimer’s disease: a HuGE gene-disease association review
.
Am. J. Epidemiol.
159
,
527
536
[PubMed]
18
Wigginton
J.E.
,
Cutler
D.J.
and
Abecasis
G.R.
(
2005
)
A note on exact tests of Hardy-Weinberg equilibrium
.
Am. J. Hum. Genet.
76
,
887
893
[PubMed]
19
Cochran
W.G.
(
1954
)
The combination of estimates from different experiments
.
Biometrics
10
,
101
129
20
DerSimonian
R.
and
Laird
N.
(
1986
)
Meta-analysis in clinical trials
.
Control. Clin. Trials
7
,
177
188
[PubMed]
21
Mantel
N.
and
Haenszel
W.
(
1959
)
Statistical aspects of the analysis of data from retrospective studies of disease
.
J. Natl. Cancer Inst.
22
,
719
748
[PubMed]
22
Begg
C.B.
and
Mazumdar
M.
(
1994
)
Operating characteristics of a rank correlation test for publication bias
.
Biometrics
50
,
1088
1101
[PubMed]
23
Egger
M.
,
Davey Smith
G.
,
Schneider
M.
et al
(
1997
)
Bias in meta-analysis detected by a simple, graphical test
.
BMJ
315
,
629
634
[PubMed]
24
Horikawa
Y.
,
Wood
C.G.
,
Yang
H.
et al
(
2008
)
Single nucleotide polymorphisms of microRNA machinery genes modify the risk of renal cell carcinoma
.
Clin. Cancer Res.
14
,
7956
7962
[PubMed]
25
Jazdzewski
K.
,
Murray
E.L.
,
Franssila
K.
et al
(
2008
)
Common SNP in pre-miR-146a decreases mature miR expression and predisposes to papillary thyroid carcinoma
.
Proc. Natl. Acad. Sci. U.S.A.
105
,
7269
7274
[PubMed]
26
Xu
T.
,
Zhu
Y.
,
Wei
Q.K.
et al
(
2008
)
A functional polymorphism in the miR-146a gene is associated with the risk for hepatocellular carcinoma
.
Carcinogenesis
29
,
2126
2131
[PubMed]
27
Yang
H.
,
Dinney
C.P.
,
Ye
Y.
et al
(
2008
)
Evaluation of genetic variants in microRNA-related genes and risk of bladder cancer
.
Cancer Res.
68
,
2530
2537
[PubMed]
28
Hoffman
A.E.
,
Zheng
T.
,
Yi
C.
et al
(
2009
)
microRNA miR-196a-2 and breast cancer: a genetic and epigenetic association study and functional analysis
.
Cancer Res.
69
,
5970
5977
[PubMed]
29
Hu
Z.
,
Liang
J.
,
Wang
Z.
et al
(
2009
)
Common genetic variants in pre-microRNAs were associated with increased risk of breast cancer in Chinese women
.
Hum. Mutat.
30
,
79
84
[PubMed]
30
Tian
T.
,
Shu
Y.
,
Chen
J.
et al
(
2009
)
A functional genetic variant in microRNA-196a2 is associated with increased susceptibility of lung cancer in Chinese
.
Cancer Epidemiol. Biomarkers Prev.
18
,
1183
1187
[PubMed]
31
Catucci
I.
,
Yang
R.
,
Verderio
P.
et al
(
2010
)
Evaluation of SNPs in miR-146a, miR196a2 and miR-499 as low-penetrance alleles in German and Italian familial breast cancer cases
.
Hum. Mutat.
31
,
E1052
E1057
[PubMed]
32
Guo
H.
,
Wang
K.
,
Xiong
G.
et al
(
2010
)
A functional variant in microRNA-146a is associated with risk of esophageal squamous cell carcinoma in Chinese Han
.
Fam. Cancer
9
,
599
603
[PubMed]
33
Liu
Z.
,
Li
G.
,
Wei
S.
et al
(
2010
)
Genetic variants in selected pre-microRNA genes and the risk of squamous cell carcinoma of the head and neck
.
Cancer
116
,
4753
4760
[PubMed]
34
Okubo
M.
,
Tahara
T.
,
Shibata
T.
et al
(
2010
)
Association between common genetic variants in pre-microRNAs and gastric cancer risk in Japanese population
.
Helicobacter
15
,
524
531
[PubMed]
35
Pastrello
C.
,
Polesel
J.
,
Della Puppa
L.
et al
(
2010
)
Association between hsa-miR146a genotype and tumor age-of-onset in BRCA1/BRCA2-negative familial breast and ovarian cancer patients
.
Carcinogenesis
31
,
2124
2126
[PubMed]
36
Srivastava
K.
,
Srivastava
A.
and
Mittal
B.
(
2010
)
Common genetic variants in pre-microRNAs and risk of gallbladder cancer in North Indian population
.
J. Hum. Genet.
55
,
495
499
[PubMed]
37
Xu
B.
,
Feng
N.H.
,
Li
P.C.
et al
(
2010
)
A functional polymorphism in Pre-miR-146a gene is associated with prostate cancer risk and mature miR-146a expression in vivo
.
Prostate
70
,
467
472
[PubMed]
38
Zeng
Y.
,
Sun
Q.M.
,
Liu
N.N.
et al
(
2010
)
Correlation between pre-miR-146a C/G polymorphism and gastric cancer risk in Chinese population
.
World J. Gastroenterol.
16
,
3578
3583
[PubMed]
39
Akkız
H.
,
Bayram
S.
,
Bekar
A.
et al
(
2011
)
No association of pre-microRNA-146a rs2910164 polymorphism and risk of hepatocellular carcinoma development in Turkish population: a case-control study
.
Gene
486
,
104
109
[PubMed]
40
Garcia
A.I.
,
Cox
D.G.
,
Barjhoux
L.
et al
(
2011
)
The rs2910164: G>C SNP in the MIR146A gene is not associated with breast cancer risk in BRCA1 and BRCA2 mutation carriers
.
Hum. Mutat.
32
,
1004
1007
[PubMed]
41
George
G.P.
,
Gangwar
R.
,
Mandal
R.K.
et al
(
2011
)
Genetic variation in microRNA genes and prostate cancer risk in North Indian population
.
Mol. Biol. Rep.
38
,
1609
1615
[PubMed]
42
Hishida
A.
,
Matsuo
K.
and
Goto
Y.
(
2011
)
Combined effect of miR-146a rs2910164 G/C polymorphism and Toll-like receptor 4 +3725 G/C polymorphism on the risk of severe gastric atrophy in Japanese
.
Dig. Dis. Sci.
56
,
1131
1137
[PubMed]
43
Mittal
R.D.
,
Gangwar
R.
,
George
G.P.
et al
(
2011
)
Investigative role of pre-microRNAs in bladder cancer patients: a case-control study in North India
.
DNA Cell Biol.
30
,
401
406
[PubMed]
44
Permuth-Wey
J.
,
Thompson
R.C.
,
Burton Nabors
L.
et al
(
2011
)
A functional polymorphism in the pre-miR-146a gene is associated with risk and prognosis in adult glioma
.
J. Neurooncol.
105
,
639
646
[PubMed]
45
Vinci
S.
,
Gelmini
S.
,
Pratesi
N.
et al
(
2011
)
Genetic variants in miR-146a, miR-149, miR-196a2, miR-499 and their influence on relative expression in lung cancers
.
Clin. Chem. Lab. Med.
49
,
2073
2080
[PubMed]
46
Yue
C.
,
Wang
M.
,
Ding
B.
et al
(
2011
)
Polymorphism of the pre-miR-146a is associated with risk of cervical cancer in a Chinese population
.
Gynecol. Oncol.
122
,
33
37
[PubMed]
47
Zhang
X.W.
,
Pan
S.D.
,
Feng
Y.L.
et al
(
2011
)
Relationship between genetic polymorphism in microRNAs precursor and genetic predisposition of hepatocellular carcinoma
.
Zhonghua Yu Fang Yi Xue Za Zhi
45
,
239
243
[PubMed]
48
Zhou
B.
,
Wang
K.
,
Wang
Y.
et al
(
2011
)
Common genetic polymorphisms in pre-microRNAs and risk of cervical squamous cell carcinoma
.
Mol. Carcinog.
50
,
499
505
[PubMed]
49
Ma
L.
,
Zhu
L.J.
,
Gu
D.Y.
et al
(
2013
)
A genetic variant in miR-146a modifies colorectal cancer susceptibility in a Chinese population
.
Arch. Toxicol.
87
,
825
833
[PubMed]
50
Alshatwi
A.A.
,
Shafi
G.
,
Hasan
T.N.
et al
(
2012
)
Differential expression profile and genetic variants of microRNAs sequences in breast cancer patients
.
PLoS ONE
7
,
e30049
[PubMed]
51
Chu
Y.H.
,
Tzeng
S.L.
,
Lin
C.W.
et al
(
2012
)
Impacts of microRNA gene polymorphisms on the susceptibility of environmental factors leading to carcinogenesis in oral cancer
.
PLoS ONE
7
,
e39777
[PubMed]
52
Hezova
R.
,
Kovarikova
A.
,
Bienertova-Vasku
J.
et al
(
2012
)
Evaluation of SNPs in miR-196-a2, miR-27a and miR-146a as risk factors of colorectal cancer
.
World J. Gastroenterol.
18
,
2827
2831
[PubMed]
53
Kim
W.H.
,
Min
K.T.
,
Jeon
Y.J.
et al
(
2012
)
Association study of mi-croRNA polymorphisms with hepatocellular carcinoma in Korean population
.
Gene
504
,
92
97
[PubMed]
54
Lung
R.W.
,
Wang
X.
,
Tong
J.H.
et al
(
2013
)
A single nucleotide polymorphism in microRNA-146a is associated with the risk for nasopharyngeal carcinoma
.
Mol. Carcinog.
52
,
E28
E38
[PubMed]
55
Mihalache
F.
,
Hoblinger
A.
,
Acalovschi
M.
et al
(
2012
)
A common variant in the precursor miR-146a sequence does not predispose to cholangiocarcinoma in a large European cohort
.
Hepatobiliary Pancreat. Dis. Int.
11
,
412
417
[PubMed]
56
Min
K.T.
,
Kim
J.W.
,
Jeon
Y.J.
et al
(
2012
)
Association of the miR-146aC>G, 149C>T, 196a2C>T, and 499A>G polymorphisms with colorectal cancer in the Korean population
.
Mol. Carcinog.
51
,
E65
E73
[PubMed]
57
Wang
M.
,
Chu
H.
,
Li
P.
et al
(
2012
)
Genetic variants in miRNAs predict bladder cancer risk and recurrence
.
Cancer Res.
72
,
6173
6182
[PubMed]
58
Xiang
Y.
,
Fan
S.
,
Cao
J.
et al
(
2012
)
Association of the microRNA-499 variants with susceptibility to hepatocellular carcinoma in a Chinese population
.
Mol. Biol. Rep.
39
,
7019
7023
[PubMed]
59
Zhou
J.
,
Lv
R.
,
Song
X.
et al
(
2012
)
Association between two genetic variants in miRNA and primary liver cancer risk in the Chinese population
.
DNA Cell Biol.
31
,
524
530
[PubMed]
60
Zhou
F.
,
Zhu
H.
,
Luo
D.
et al
(
2012
)
A functional polymorphism in Pre-miR-146a is associated with susceptibility to gastric cancer in a Chinese population
.
DNA Cell Biol.
31
,
1290
1295
[PubMed]
61
Lv
M.
,
Dong
W.
,
Li
L.
et al
(
2013
)
Association between genetic variants in pre-miRNA and colorectal cancer risk in a Chinese population
.
J. Cancer Res. Clin. Oncol.
139
,
1405
1410
[PubMed]
62
Chae
Y.S.
,
Kim
J.G.
,
Lee
S.J.
et al
(
2013
)
A miR-146a polymorphism (rs2910164) predicts risk of and survival from colorectal cancer
.
Anticancer Res.
33
,
3233
3239
[PubMed]
63
Ma
F.
,
Zhang
P.
,
Lin
D.X.
et al
(
2013
)
There is no association between microRNA gene polymorphisms and risk of triple negative breast cancer in a Chinese Han population
.
PLoS ONE
8
,
e60195
[PubMed]
64
Ma
L.
,
Zhu
L.
,
Gu
D.
et al
(
2013
)
A genetic variant in miR-146a modifies colorectal cancer susceptibility in a Chinese population
.
Arch. Toxicol.
87
,
825
833
[PubMed]
65
Orsos
Z.
,
Szanyi
I.
,
Csejtei
A.
et al
(
2013
)
Association of pre-miR-146a rs2910164 polymorphism with the risk of head and neck cancer
.
Anticancer Res.
33
,
341
346
[PubMed]
66
Vinci
S.
,
Gelmini
S.
,
Mancini
I.
et al
(
2013
)
Genetic and epigenetic factors in regulation of microRNA in colorectal cancers
.
Methods
59
,
138
146
[PubMed]
67
Wei
W.J.
,
Wang
Y.L.
,
Li
D.S.
et al
(
2013
)
Association between the rs2910164 polymorphism in pre-Mir-146a sequence and thyroid carcinogenesis
.
PLoS ONE
8
,
e56638
[PubMed]
68
Wei
J.
,
Zheng
L.
,
Liu
S.
et al
(
2013
)
MiR-196a2 rs11614913 T>C polymorphism and risk of esophageal cancer in a Chinese population
.
Hum. Immunol.
74
,
1199
1205
[PubMed]
69
Yamashita
J.
,
Iwakiri
T.
,
Fukushima
S.
et al
(
2013
)
The rs2910164 G/C polymorphism in microRNA-146a is associated with the incidence of malignant melanoma
.
Melanoma Res.
23
,
13
20
[PubMed]
70
Zhang
J.
,
Wang
R.
,
Ma
Y.Y.
et al
(
2013
)
Association between single nucleotide polymorphisms in miRNA196a-2 and miRNA146a and susceptibility to hepatocellular carcinoma in a Chinese population
.
Asian Pac. J. Cancer Prev.
14
,
6427
6431
[PubMed]
71
Ahn
D.H.
,
Rah
H.
,
Choi
Y.K.
et al
(
2013
)
Association of the miR-146aC>G, miR-149T>C, miR-196a2T>C, and miR-499A>G polymorphisms with gastric cancer risk and survival in the Korean population
.
Mol. Carcinog.
52
(
Suppl. 1
),
E39
E51
[PubMed]
72
Song
M.Y.
,
Su
H.J.
,
Zhang
L.
et al
(
2013
)
Genetic polymorphisms of mR-146a and miR-27a, H. pylori infection, and risk of gastric lesions in a Chinese population
.
PLoS ONE
8
,
e61250
[PubMed]
73
Wu
R.R.
(
2014
)
The Association of miR-SNP with the Susceptibility of Colorectal Cancer and Response to Chemotherapy
,
Soochow University
,
Suzhou, China
74
Chu
Y.H.
,
Hsieh
M.J.
,
Chiou
H.L.
et al
(
2014
)
MicroRNA gene polymorphisms and environmental factors increase patient susceptibility to hepatocellular carcinoma
.
PLoS ONE
9
,
e89930
[PubMed]
75
Cong
N.
,
Chen
H.
,
Bu
W.Z.
et al
(
2014
)
miR-146a G>C polymorphisms and risk of hepatocellular carcinoma in a Chinese population
.
Tumour Biol.
35
,
5669
5673
[PubMed]
76
Dikeakos
P.
,
Theodoropoulos
G.
,
Rizos
S.
et al
(
2014
)
Association of the miR-146aC>G, miR-149T>C, and miR-196a2T>C polymorphisms with gastric cancer risk and survival in the Greek population
.
Mol. Biol. Rep.
41
,
1075
1080
[PubMed]
77
Du
M.
,
Lu
D.
,
Wang
Q.
et al
(
2014
)
Genetic variations in microRNAs and the risk and survival of renal cell cancer
.
Carcinogenesis
35
,
1629
1635
[PubMed]
78
Hu
X.
,
Li
L.
,
Shang
M.
et al
(
2014
)
Association between microRNA genetic variants and susceptibility to colorectal cancer in Chinese population
.
Tumour Biol.
35
,
2151
2156
[PubMed]
79
Huang
G.L.
,
Chen
M.L.
,
Li
Y.Z.
et al
(
2014
)
Association of miR-146a gene polymorphism with risk of nasopharyngeal carcinoma in the central-southern Chinese population
.
J. Hum. Genet.
59
,
141
144
[PubMed]
80
Jeon
H.S.
,
Lee
Y.H.
,
Lee
S.Y.
et al
(
2014
)
A common polymorphism in premicroRNA-146a is associated with lung cancer risk in a Korean population
.
Gene
534
,
66
71
[PubMed]
81
Jia
Y.
,
Zang
A.
,
Shang
Y.
et al
(
2014
)
Micro-RNA-146a rs2910164 polymorphism is associated with susceptibility to non-small cell lung cancer in the Chinese population
.
Med. Oncol.
31
,
194
[PubMed]
82
Kupcinskas
J.
,
Wex
T.
,
Link
A.
et al
(
2014
)
Gene polymorphisms of micrornas in Helicobacter pylori-induced high risk atrophic gastritis and gastric cancer
.
PLoS ONE
9
,
e87467
[PubMed]
83
Kupcinskas
J.
,
Bruzaite
I.
,
Juzenas
S.
et al
(
2014
)
Lack of association between miR-27a, miR-146a, miR-196a-2, miR-492 and miR-608 gene polymorphisms and colorectal cancer
.
Sci. Rep.
4
,
5993
[PubMed]
84
Mao
Y.
,
Li
Y.
,
Jing
F.
et al
(
2014
)
Association of a genetic variant in microRNA-146a with risk of colorectal cancer: a population-based case-control study
.
Tumour Biol.
35
,
6961
6967
[PubMed]
85
Nikolić
Z.Z.
,
SavićPavićević
D.L.j.
,
Vukotić
V.D.
et al
(
2014
)
Association between genetic variant in hsa-miR-146a gene and prostate cancer progression: evidence from Serbian population
.
Cancer Causes Control.
25
,
1571
1575
[PubMed]
86
Palmieri
A.
,
Carinci
F.
,
Martinelli
M.
et al
(
2014
)
Role of the MIR146A polymorphism in the origin and progression of oral squamous cell carcinoma
.
Eur. J. Oral Sci.
122
,
198
201
[PubMed]
87
Parlayan
C.
,
Ikeda
S.
,
Sato
N.
et al
(
2014
)
Association analysis of single nucleotide polymorphisms in miR-146a and miR-196a2 on the prevalence of cancer in elderly Japanese: a case-control study
.
Asian Pac. J. Cancer Prev.
15
,
2101
2107
[PubMed]
88
Pu
J.Y.
,
Dong
W.
,
Zhang
L.
,
Liang
W.B.
,
Yang
Y.
and
Lv
M.L.
(
2014
)
No association between single nucleotide polymorphisms in pre-mirnas and the risk of gastric cancer in Chinese population
.
Iran J. Basic Med. Sci.
17
,
128
133
[PubMed]
89
Qu
Y.
,
Qu
H.
,
Luo
M.
et al
(
2014
)
MicroRNAs related polymorphisms and genetic susceptibility to esophageal squamous cell carcinoma
.
Mol. Genet. Genomics
289
,
1123
1130
[PubMed]
90
Dikaiakos
P.
,
Gazouli
M.
,
Rizos
S.
et al
(
2015
)
Evaluation of genetic variants in miRNAs in patients with colorectal cancer
.
Cancer Biomark.
15
,
157
162
[PubMed]
91
Gomez-Lira
M.
,
Ferronato
S.
,
Orlandi
E.
et al
(
2015
)
Association of microRNA 146a polymorphism rs2910164 and the risk of melanoma in an Italian population
.
Exp. Dermatol.
24
,
794
795
[PubMed]
92
Qi
P.
,
Wang
L.
,
Zhou
B.
et al
(
2015
)
Associations of miRNA polymorphisms and expression levels with breast cancer risk in the Chinese population
.
Genet. Mol. Res.
14
,
6289
6296
[PubMed]
93
Zhu
J.
,
Yang
L.
,
You
W.
et al
(
2015
)
Genetic variation in miR-100 rs1834306 is associated with decreased risk for esophageal squamous cell carcinoma in Kazakh patients in northwest China
.
Int. J. Clin. Exp. Pathol.
8
,
7332
7340
[PubMed]
94
Deng
S.
,
Wang
W.
,
Li
X.
et al
(
2015
)
Common genetic polymorphisms in pre-microRNAs and risk of bladder cancer
.
World J. Surg. Oncol.
13
,
297
[PubMed]
95
Li
X.
,
Li
K.
and
Wu
Z.
(
2015
)
Association of four common SNPs in microRNA polymorphisms with the risk of hepatocellular carcinoma
.
Int. J. Clin. Exp. Pathol.
8
,
9560
9566
[PubMed]
96
Shen
F.
,
Chen
J.
,
Guo
S.
et al
(
2016
)
Genetic variants in miR-196a2 and miR-499 are associated with susceptibility to esophageal squamous cell carcinoma in Chinese Han population
.
Tumour Biol.
37
,
4777
4784
[PubMed]
97
Yan
P.
,
Xia
M.
,
Gao
F.
et al
(
2015
)
Predictive role of miR-146a rs2910164 (C>G), miR-149 rs2292832 (T>C), miR-196a2 rs11614913 (T>C) and miR-499 rs3746444 (T>C) in the development of hepatocellular carcinoma
.
Int. J. Clin. Exp. Pathol.
8
,
15177
15183
[PubMed]
98
Yin
Z.
,
Cui
Z.
,
Ren
Y.
et al
(
2016
)
Association between polymorphisms in pre-miRNA genes and risk of lung cancer in a Chinese non-smoking female population
.
Lung Cancer
94
,
15
21
[PubMed]
99
Xia
Z.G.
,
Yin
H.F.
,
Long
Y.
et al
(
2016
)
Genetic variant of miR-146a rs2910164 C>G and gastric cancer susceptibility
.
Oncotarget
7
,
34316
34321
[PubMed]
100
Hashemi
M.
,
Moradi
N.
,
Ziaee
S.A.
et al
(
2016
)
Association between single nucleotide polymorphism in miR-499, miR-196a2, miR-146a and miR-149 and prostate cancer risk in a sample of Iranian population
.
J. Adv. Res.
7
,
491
498
[PubMed]
101
Jiang
J.
,
Jia
Z.F.
,
Cao
D.H.
et al
(
2016
)
Association of the miR-146a rs2910164 polymorphism with gastric cancer susceptibility and prognosis
.
Future Oncol.
12
,
2215
2226
[PubMed]
102
Miao
L.
,
Wang
L.
,
Zhu
L.
et al
(
2016
)
Association of microRNA polymorphisms with the risk of head and neck squamous cell carcinoma in a Chinese population: a case-control study
.
Chin J. Cancer
35
,
77
[PubMed]
103
Chen
H.C.
,
Tseng
Y.K.
,
Chi
C.C.
et al
(
2016
)
Genetic variants in microRNA-146a (C>G) and microRNA-1269b (G>C) are associated with the decreased risk of oral premalignant lesions, oral cancer, and pharyngeal cancer
.
Arch. Oral Biol.
72
,
21
32
[PubMed]
104
Frixa
T.
,
Donzelli
S.
and
Blandino
G.
(
2015
)
Oncogenic microRNAs: key players in malignant transformation
.
Cancers (Basel)
7
,
2466
2485
[PubMed]
105
Ryan
B.M.
,
Robles
A.I.
and
Harris
C.C.
(
2010
)
Genetic variation in microRNA networks: the implications for cancer research
.
Nat. Rev. Cancer
10
,
389
402
[PubMed]
106
Kang
Z.
,
Li
Y.
,
He
X.
et al
(
2014
)
Quantitative assessment of the association between miR-196a2 rs11614913 polymorphism and cancer risk: evidence based on 45,816 subjects
.
Tumor Biol.
35
,
6271
6282
107
Perry
M.M.
,
Moschos
S.A.
,
Williams
A.E.
et al
(
2008
)
Rapid changes in microRNA-146a expression negatively regulate the IL-1beta-induced inflammatory response in human lung alveolar epithelial cells
.
J. Immunol.
180
,
5689
5698
[PubMed]
108
Reis
L.O.
,
Pereira
T.C.
,
Lopes-Cendes
I.
et al
(
2010
)
MicroRNAs: a new paradigm on molecular urological oncology
.
Urology
76
,
521
527
[PubMed]
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