Single nucleotide polymorphisms (SNPs) in miRNA biosynthesis genes DROSHA and DGCR8 were indicated to be correlated with cancer risk. We comprehensively reviewed and analyzed the effect of DROSHA and DGCR8 polymorphisms on cancer risk. Eligible articles were selected according to a series of inclusion and exclusion criteria. Consequently, ten case–control studies (from nine citations) with 4265 cancer cases and 4349 controls were involved in a meta-analysis of seven most prevalent SNPs (rs10719 T/C, rs6877842 G/C, rs2291109 A/T, rs642321 C/T, rs3757 G/A, rs417309 G/A, rs1640299 T/G). Our findings demonstrated that the rs417309 SNP in DGCR8 was significantly associated with an elevated risk of overall cancer in every genetic model. In stratified analysis, correlations of DROSHA rs10719 and rs6877842 SNPs were observed in Asian and laryngeal cancer subgroups, respectively. Moreover, associations of the rs417309 SNP could also be found in numerous subgroups including: Asian and Caucasian population subgroups; laryngeal and breast cancer subgroups; population-based (PB) and hospital-based (HB) subgroups. In conclusion, the DROSHA rs10719, rs6877842 SNPs, and DGCR8 rs417309 SNP play pivotal roles in cancerogenesis and may be potential biomarkers for cancer-forewarning.

Introduction

miRNAs are a type of small non-coding RNAs that play roles at post-transcriptional level by sequence-specific binding to the 3′-UTRs of target mRNAs [1]. During the miRNA maturing processing, primary miRNAs (pri-miRNAs) are first synthesized by RNA II polymerase in nucleus. And then, they are converted into precursor miRNAs (pre-miRNAs) by a Drosha–DGCR8 microprocessor complex which is constituted by DROSHA, an RNase III superfamily member and its cofactor DGCR8 [2]. Next, the pre-miRNAs are exported to cytoplasm and converted into mature miRNAs by DICER. miRNA genes are deemed to function as both oncogenes and tumor suppressors and their expressions have been confirmed to be associated with varieties of cancers [3–5]. Hence, imparied miRNA processing caused by the aberrant expression of miRNA biosynthesis genes DROSHA or DGCR8 can noticeably promote the tumorigenesis [6].

As the most prevalent genetic variation, single nucleotide polymorphisms (SNPs) in DROSHA and DGCR8 genes can affect their structure or expression, resulting in incomplete miRNA processing and in turn influence the expression of target genes, thereby acting as risk factor for diseases such as cancer. Thus far, accumulating studies have been concerned with the association between DROSHA and DGCR8 SNPs and the susceptibility to cancer. However, the findings were inconsistent and there was no systematic analysis for DROSHA and DGCR8 SNPs and cancer risk. In the present study, we comprehensively reviewed the eligible studies and analyzed all available data. Our aim is to explore the association of DROSHA and DGCR8 SNPs with cancer risk, supplying clues to researchers for screening novel cancer biomarkers.

Methods

Retrieval strategy

A detailed literature retrieval was performed by two independent investigators (J.W. and Z.L.) for publications regarding the association between DROSHA and DGCR8 polymorphisms and cancer risk. Relevant publications were selected from PubMed and Web of Science using a combination of the following keywords: ‘DROSHA/drosha ribonuclease III/RNase III/DGCR8/Digeorge syndrome critical region gene 8/Pasha’; ‘SNP/polymorphism/variation/variant’; and ‘tumor/cancer/carcinoma/neoplasm’, up to 1 January 2018.

Inclusion and exclusion criteria

Eligible publications were selected by the following inclusion criteria: (i) a case–control designed study; (ii) regarding the correlation between DROSHA or DGCR8 polymorphisms and cancer risk. Articles meeting the following criteria were excluded: (i) reviews, letters, or editorials; (ii) duplicate records; (iii) unrelated to cancer or DROSHA and DGCR8 polymorphisms; (iv) no available data to extract.

Data extraction

Data extraction was completed by two independent investigators (J.W. and Z.L.). Basic features obtained from each eligible article were as follows: first author’s name, publication year (unpublished collected study year), country, ethnicity, type of cancer, gene, polymorphisms, sample size of cases and controls, genotype distribution, Hardy–Weinberg equilibrium (HWE) in controls, source of control groups (population-based (PB) or hospital-based (HB)), genotyping method, adjusted factors, and quality score. When the article covered multiple stages, data were extracted individually. When the data in eligible articles were unavailable, we tried our best to contact the corresponding authors for original data.

Methodology quality assessment

Quality of the selected studies was assessed by two independent reviewers (H.D. and X.F.) according to a study regarding the method for assigning quality scores, which was mentioned in prior meta-analyses [7,8]. Six items were evaluated in the quality assessment scale: (i) the representativeness of the cases; (ii) the source of controls; (iii) the ascertainment of relevant cancers; (iv) the sample size; (v) the quality control of the genotyping methods; (vi) HWE in controls. The quality scores of eligible studies ranged from 0 to 10. Studies with scores less than 5 and HWE disequilibrium were removed from the subsequent analyses.

Statistical analysis

All statistical analyses in the present study were performed by STATA software, version 11.0 (STATA Corp, College Station, TX, U.S.A.). All statistical tests presented were two-tailed and the P-values<0.05 were regarded as statistically significant, unless highlighted otherwise. And the Bonferroni correction was conducted to justify P-values [40]. The HWE for the genotype frequencies of DROSHA and DGCR8 polymorphisms in controls was computed by χ2 test. The intensity of the correlations between the DROSHA and DGCR8 polymorphisms and the risk of cancer was estimated by odds ratios (ORs) with its corresponding 95% confidence intervals (95% CIs). Between-study heterogeneity was computed by a χ2-based Cochran’s Qtest (significance at P<0.10 and I2 > 50%). We summarized the results by using fixed effect models [9] when the interstudy heterogeneity was absent, otherwise random effect models [10]. Begg’s test and Egger’s linear regression analysis were performed to estimate the publication bias statistically [11,12]. P<0.10 was regarded as statistically significant in both Egger’s and Begg’s test [8,28]. What is more, sensitivity analysis was shown to inspect whether the pooled results were steady after we excluded the outlying studies.

Results

Characteristics of the included studies

As presented in Figure 1, a total of 148 publications were collected through database search after eliminating the duplicate studies. We eliminated 83 records after browsing the titles and abstracts (40 were functional studies; 12 were reviews or meta-analysis; 9 were not case–control studies; 52 were unrelated to DROSHA or DGCR8 SNPs; 8 were unrelated to cancer; 12 were not correlated with cancer risks). What was more, six studies were excluded by calculating (one for the unavailable data; five for the limited study number of DROSHA or DGCR8 polymorphisms). Moreover, the removal of two records from the subsequent analyses was due to the inconformity of their genotype distributions to HWE (PHWE<0.05). Hence, in total, ten case–control studies (from nine citations) containing 4265 cancer cases and 4349 cancer-free controls were involved in our meta-analyses, which were accorded with our inclusion criteria and the evaluation of methodology quality. The characteristics of these involved articles were presented in Table 1 and the frequency distributions of DROSHA and DGCR8 polymorphisms genotype were shown in Table 2. In summary obtained from ten eligible case–control studies, seven SNPs of DROSHA or DGCR8 genes were investigated in the eventual analysis. According to the SNPs selection criteria mentioned in eligible studies [2,14], we found that none of these seven SNPs were in strong linkage disequilibrium (r2 ≥ 0.8). In DROSHA, the analyzed SNPs were rs10719 T/C, rs6877842 G/C, rs2291109 A/T, rs642321 C/T; in DGCR8, the analyzed SNPs are rs3757 G/A, rs417309 G/A, rs1640299 T/G (Figure 2).

The flow chart of identification for studies included in the meta-analysis

A forest plot of the DROSHA and DGCR8 SNPs associated with cancer risk

Figure 2
A forest plot of the DROSHA and DGCR8 SNPs associated with cancer risk

((a) DROSHA rs10719 in the ethnicity subgroup analysis; (b) DROSHA rs6877842 in the cancer type subgroups; (c) DGCR8 rs417309 under allelic model: A compared with G).

Figure 2
A forest plot of the DROSHA and DGCR8 SNPs associated with cancer risk

((a) DROSHA rs10719 in the ethnicity subgroup analysis; (b) DROSHA rs6877842 in the cancer type subgroups; (c) DGCR8 rs417309 under allelic model: A compared with G).

Table 1
The main features of enrolled studies
Ref. no. Year Country Ethnicity Sample size Source of controls Genotyping method Adjusted factors Quality score Citation 
    Case Control      
2010 Korean Asian 93 93 HB MS NM 5.5 [15
2013 China Asian 878 900 PB Taqman Age and residential area 7.5 [2
2013 China Asian 914 967 PB Taqman Age and residential area 7.5 [2
2013 China Asian 685 730 HB Taqman Age, sex, and smoking status [16
2013 America Caucasian 277 278 PB SNPlex technology Age, sex, ethnicity, and county of residence 7.5 [14
2015 Korean Asian 408 400 HB PCR-RFLP Age, gender, hypertension, diabetes mellitus [24
2015 Polish Caucasian 135 170 HB Taqman NM 7.5 [25
2016 Polish Caucasian 100 100 NM Taqman NM [13
2016 Korean Asian 147 209 HB PCR-RFLP Age, gender, hypertension, diabetes mellitus, drinking status, and smoking [26
10 2017 China Asian 628 502 HB HRM Age, sex, region, smoking status, and drinking status [27
Ref. no. Year Country Ethnicity Sample size Source of controls Genotyping method Adjusted factors Quality score Citation 
    Case Control      
2010 Korean Asian 93 93 HB MS NM 5.5 [15
2013 China Asian 878 900 PB Taqman Age and residential area 7.5 [2
2013 China Asian 914 967 PB Taqman Age and residential area 7.5 [2
2013 China Asian 685 730 HB Taqman Age, sex, and smoking status [16
2013 America Caucasian 277 278 PB SNPlex technology Age, sex, ethnicity, and county of residence 7.5 [14
2015 Korean Asian 408 400 HB PCR-RFLP Age, gender, hypertension, diabetes mellitus [24
2015 Polish Caucasian 135 170 HB Taqman NM 7.5 [25
2016 Polish Caucasian 100 100 NM Taqman NM [13
2016 Korean Asian 147 209 HB PCR-RFLP Age, gender, hypertension, diabetes mellitus, drinking status, and smoking [26
10 2017 China Asian 628 502 HB HRM Age, sex, region, smoking status, and drinking status [27

Abbreviations: HRM, high-resolution melting; MS, sequenome MS-based genotyping assay; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; NM, not mentioned.

Table 2
Genotype frequency distributions of DROSHA and DGCR8 SNPs in included studies
Ref. No. Year Cancer type Gene SNPs1 Sample size Case Control PHWE MAF in controls (Global MAF4Included in meta-analysis 
Case Control Homozygote wild Heterozygote Homozygote variant Homozygote wild Heterozygote Homozygote variant         
2010 Lung cancer DROSHA rs6877842 93 93 81 11 84 0.136 0.054 (0.138) Yes 
    (G > C)            
  Lung cancer DROSHA rs10719 97 97 59 29 52 38 0.987 0.268 (0.483) Yes 
    (T > C)            
  Lung cancer DGCR8 rs3757 94 90 60 27 60 24 0.114 0.200 (0.182) Yes 
    (G > A)            
  Lung cancer DGCR8 rs417309 98 97 90 88 0.632 0.046 (0.043) Yes 
    (G > A)            
  Lung cancer DGCR8 rs1640299 98 97 58 33 52 40 0.444 0.258 (0.381) Yes 
    (T > G)            
2013 Breast cancer DROSHA rs10719 847 878 433 346 68 463 353 62 0.635 0.272 (0.483) Yes 
    (T > C)            
  Breast cancer DROSHA rs17409893 849 885 527 287 35 575 276 34 0.902 0.194 (0.222) No3 
    (A > G)            
  Breast cancer DROSHA rs2291109 858 886 552 273 33 535 306 45 0.884 0.223 (0.061) Yes 
    (A > T)            
  Breast cancer DROSHA rs642321 854 883 212 423 219 231 433 219 0.571 0.493 (0.322) Yes 
    (C > T)            
  Breast cancer DGCR8 rs1640299 849 891 465 330 54 476 357 58 0.412 0.265 (0.381) Yes 
    (T > G)            
  Breast cancer DGCR8 rs417309 860 893 771 89 826 67 0.244 0.038 (0.043) Yes 
    (G > A)            
  Breast cancer DGCR8 rs720012 867 891 225 425 217 240 451 200 0.668 0.478 (0.221) No3 
    (G > A)            
  Breast cancer DGCR8 rs720014 836 880 542 264 30 555 287 38 0.907 0.206 (0.183) No3 
    (T > C)            
2013 Breast cancer DROSHA rs2291109 899 957 563 296 40 625 298 34 0.835 0.191 (0.061) Yes 
    (A > T)            
  Breast cancer DGCR8 rs417309 901 960 830 68 910 49 0.687 0.027 (0.043) Yes 
    (G > A)            
2013 Bladder cancer DROSHA rs2291109 685 730 421 228 36 419 280 31 0.062 0.234 (0.061) Yes 
    (A > T)            
  Bladder cancer DROSHA rs10719 684 727 352 278 54 413 275 39 0.437 0.243 (0.483) Yes 
    (T > C)            
  Bladder cancer DROSHA rs642321 685 730 197 326 162 176 371 183 0.655 0.505 (0.322) Yes 
    (C > T)            
2013 Renal cell carcinoma DROSHA rs10719 252 246 161 75 16 155 76 15 0.177 0.215 (0.483) Yes 
    (T > C)            
  Renal cell carcinoma DROSHA rs6877842 275 278 200 65 10 204 65 0.185 0.149 (0.138) Yes 
    (G > C)            
  Renal cell carcinoma DGCR8 rs3757 276 278 163 102 11 162 102 14 0.688 0.234 (0.182) Yes 
    (G > A)            
  Renal cell carcinoma DGCR8 rs417309 277 278 243 30 243 34 0.87 0.065 (0.043) Yes 
    (G > A)            
  Renal cell carcinoma DGCR8 rs1640299 277 278 61 151 65 75 136 67 0.729 0.486 (0.381) Yes 
    (T > G)            
2015 Colorectal cancer DROSHA rs10719 408 400 224 154 30 211 168 21 0.09 0.263 (0.483) Yes 
    (T > C)            
2015 Laryngeal cancer DROSHA rs6877842 128 170 73 49 76 79 15 0.384 0.321 (0.138) Yes 
    (G > C)            
  Laryngeal cancer DGCR8 rs417309 112 170 67 32 13 116 46 0.227 0.182 (0.043) Yes 
    (G > A)            
  Laryngeal cancer DGCR8 rs1640299 113 170 60 47 61 93 16 0.021 0.368 (0.381) No2 
    (T > G)            
  Laryngeal cancer DGCR8 rs3757 122 170 29 89 36 119 15 <0.001 0.438 (0.182) No2 
    (G > A)            
2016 Laryngeal cancer DROSHA rs6877842 100 100 60 35 44 47 0.476 0.325 (0.138) Yes 
    (G > C)            
  Laryngeal cancer DGCR8 rs417309 100 100 60 28 12 69 27 0.516 0.175 (0.043) Yes 
    (G > A)            
  Laryngeal cancer DGCR8 rs1640299 100 100 52 42 36 55 0.062 0.365 (0.381) Yes 
    (T > G)            
2016 Hepatocellular carcinoma DROSHA rs10719 147 209 81 53 13 110 88 11 0.215 0.263 (0.483) Yes 
    (T > C)            
  Hepatocellular carcinoma DROSHA rs6877842 147 209 138 200 0.75 0.022 (0.138) Yes 
    (G > C)            
10 2017 Gastirc cancer DROSHA rs10719 628 502 314 257 57 248 205 49 0.487 0.302 (0.483) Yes 
    (T > C)            
Ref. No. Year Cancer type Gene SNPs1 Sample size Case Control PHWE MAF in controls (Global MAF4Included in meta-analysis 
Case Control Homozygote wild Heterozygote Homozygote variant Homozygote wild Heterozygote Homozygote variant         
2010 Lung cancer DROSHA rs6877842 93 93 81 11 84 0.136 0.054 (0.138) Yes 
    (G > C)            
  Lung cancer DROSHA rs10719 97 97 59 29 52 38 0.987 0.268 (0.483) Yes 
    (T > C)            
  Lung cancer DGCR8 rs3757 94 90 60 27 60 24 0.114 0.200 (0.182) Yes 
    (G > A)            
  Lung cancer DGCR8 rs417309 98 97 90 88 0.632 0.046 (0.043) Yes 
    (G > A)            
  Lung cancer DGCR8 rs1640299 98 97 58 33 52 40 0.444 0.258 (0.381) Yes 
    (T > G)            
2013 Breast cancer DROSHA rs10719 847 878 433 346 68 463 353 62 0.635 0.272 (0.483) Yes 
    (T > C)            
  Breast cancer DROSHA rs17409893 849 885 527 287 35 575 276 34 0.902 0.194 (0.222) No3 
    (A > G)            
  Breast cancer DROSHA rs2291109 858 886 552 273 33 535 306 45 0.884 0.223 (0.061) Yes 
    (A > T)            
  Breast cancer DROSHA rs642321 854 883 212 423 219 231 433 219 0.571 0.493 (0.322) Yes 
    (C > T)            
  Breast cancer DGCR8 rs1640299 849 891 465 330 54 476 357 58 0.412 0.265 (0.381) Yes 
    (T > G)            
  Breast cancer DGCR8 rs417309 860 893 771 89 826 67 0.244 0.038 (0.043) Yes 
    (G > A)            
  Breast cancer DGCR8 rs720012 867 891 225 425 217 240 451 200 0.668 0.478 (0.221) No3 
    (G > A)            
  Breast cancer DGCR8 rs720014 836 880 542 264 30 555 287 38 0.907 0.206 (0.183) No3 
    (T > C)            
2013 Breast cancer DROSHA rs2291109 899 957 563 296 40 625 298 34 0.835 0.191 (0.061) Yes 
    (A > T)            
  Breast cancer DGCR8 rs417309 901 960 830 68 910 49 0.687 0.027 (0.043) Yes 
    (G > A)            
2013 Bladder cancer DROSHA rs2291109 685 730 421 228 36 419 280 31 0.062 0.234 (0.061) Yes 
    (A > T)            
  Bladder cancer DROSHA rs10719 684 727 352 278 54 413 275 39 0.437 0.243 (0.483) Yes 
    (T > C)            
  Bladder cancer DROSHA rs642321 685 730 197 326 162 176 371 183 0.655 0.505 (0.322) Yes 
    (C > T)            
2013 Renal cell carcinoma DROSHA rs10719 252 246 161 75 16 155 76 15 0.177 0.215 (0.483) Yes 
    (T > C)            
  Renal cell carcinoma DROSHA rs6877842 275 278 200 65 10 204 65 0.185 0.149 (0.138) Yes 
    (G > C)            
  Renal cell carcinoma DGCR8 rs3757 276 278 163 102 11 162 102 14 0.688 0.234 (0.182) Yes 
    (G > A)            
  Renal cell carcinoma DGCR8 rs417309 277 278 243 30 243 34 0.87 0.065 (0.043) Yes 
    (G > A)            
  Renal cell carcinoma DGCR8 rs1640299 277 278 61 151 65 75 136 67 0.729 0.486 (0.381) Yes 
    (T > G)            
2015 Colorectal cancer DROSHA rs10719 408 400 224 154 30 211 168 21 0.09 0.263 (0.483) Yes 
    (T > C)            
2015 Laryngeal cancer DROSHA rs6877842 128 170 73 49 76 79 15 0.384 0.321 (0.138) Yes 
    (G > C)            
  Laryngeal cancer DGCR8 rs417309 112 170 67 32 13 116 46 0.227 0.182 (0.043) Yes 
    (G > A)            
  Laryngeal cancer DGCR8 rs1640299 113 170 60 47 61 93 16 0.021 0.368 (0.381) No2 
    (T > G)            
  Laryngeal cancer DGCR8 rs3757 122 170 29 89 36 119 15 <0.001 0.438 (0.182) No2 
    (G > A)            
2016 Laryngeal cancer DROSHA rs6877842 100 100 60 35 44 47 0.476 0.325 (0.138) Yes 
    (G > C)            
  Laryngeal cancer DGCR8 rs417309 100 100 60 28 12 69 27 0.516 0.175 (0.043) Yes 
    (G > A)            
  Laryngeal cancer DGCR8 rs1640299 100 100 52 42 36 55 0.062 0.365 (0.381) Yes 
    (T > G)            
2016 Hepatocellular carcinoma DROSHA rs10719 147 209 81 53 13 110 88 11 0.215 0.263 (0.483) Yes 
    (T > C)            
  Hepatocellular carcinoma DROSHA rs6877842 147 209 138 200 0.75 0.022 (0.138) Yes 
    (G > C)            
10 2017 Gastirc cancer DROSHA rs10719 628 502 314 257 57 248 205 49 0.487 0.302 (0.483) Yes 
    (T > C)            

The results were in bold if P<0.05. Abbreviations: MAF, minor allele frequency; PHWE, the P-value for HWE in control groups.

1, The ancestral alleles were referenced in the NCBI database.

2, Excluded due to the SNP not being in accordance with HWE.

3, Excluded due to the limited number for this locus;

4, The global MAFs were referenced in the NCBI database.

Quantitative data synthesis of seven SNPs in DROSHA and DGCR8 genes

Four SNPs in DROSHA

First, all eligible articles were summarized to evaluate the correlation strength of each DROSHA SNP with the risk of overall cancer. However, these four SNPs (rs10719 T/C, rs6877842 G/C, rs642321 C/T, and rs2291109 A/T) did not manifest any significant associations with cancer risk in any genetic models (Table 3). Due to the existence of interstudy heterogeneity, stratified analyses were performed.

Table 3
Meta-analysis of the association between DROSHA and DGCR8 polymorphisms and cancer risk
  Heterozygote compared with homozygote wild Homozygote variant compared with homozygote wild Dominant model Recessive model Allelic model 
SNPs n P (Pcorr) OR (95% CI) I2 (%) P (Pcorr) OR (95% CI) I2 (%) P (Pcorr) OR (95% CI) I2 (%) P (Pcorr) OR (95% CI) I2 (%) P (Pcorr) OR (95% CI) I2 (%) 
DROSHA rs10719 (T > C) 0.934 1.004 (0.904–1.117) 0.2 0.055 1.214 (0.996–1.480) 0.0 0.502 1.035 (0.936–1.145) 10.5 0.053 1.210 (0.998–1.467) 0.0 0.179 1.056 (0.975–1.145) 
Ethnicity                 
Asian 0.874 1.009 (0.904–1.126) 15.6 0.048 (0.336) 1.230 (1.001–1.511) 0.0 0.449 1.041 (0.938–1.157) 10.5 0.04 (0.336) 1.223 (1.002–1.494) 0.0 0.154 1.062 (0.978–1.154) 
Caucasian 0.796 0.950 (0.645–1.400) NA 0.944 1.027 (0.491–2.148) NA 0.838 0.963 (0.668–1.387) NA 0.907 1.044 (0.504–2.161) NA 0.904 0.981 (0.725–1.329) NA 
Source of controls                 
HB 0.905 0.992 (0.869–1.132) 30.1 0.071 1.257 (0.981–1.610) 0.643 1.030 (0.908–1.169) 27.2 0.061 1.259 (0.989–1.602) 0.25 1.060 (0.0960–1.172) 16.7 
PB 0.767 1.027 (0.861–1.225) 0.396 1.142 (0.822–1.588) 0.0 0.616 1.044 (0.883–1.234) 0.463 1.128 (0.818–1.555) 0.0 0.398 1.049 (0.918-1.199) 
DROSHA rs6877842 (G > C) 0.174 0.841 (0.655–1.079) 39.3 0.101 0.627 (0.358–1.096) 0.0 0.3581 0.839 (0.577–1.220) 50.7 0.218 0.706 (0.406–1.228) 0.0 0.073 0.832 (0.680–1.017) 48.2 
Ethnicity                 
Asian 0.292 1.438 (0.732–2.824) 0.98 1.037 (0.064–16.860) NA 0.303 1.414 (0.731–2.735) 0.0 1.000 (0.062–16.230) NA 0.326 1.372 (0.731–2.576) 
Caucasian 0.059 0.772 (0.590–1.010) 47.1 0.094 0.613 (0.346–1.087) 27.5 0.1251 0.716 (0.467–1.097) 61.1 0.21 0.697 (0.396–1.225) 0.0 0.1231 0.762 (0.540–1.076) 60.1 
Source of controls                 
HB 0.395 0.845 (0.574–1.245) 44.3 0.103 0.460 (0.181–1.169) 0.881 0.953 (0.506–1.793) 52.5 0.194 0.545 (0.218–1.361) 0.155 0.796 (0.581–1.090) 48.0 
PB 0.922 1.020 (0.687–1.514) NA 0.79 1.133 (0.451–2.848) NA 0.862 1.034 (0.710–1.505) NA 0.797 1.128 (0.451–2.820) NA 0.807 1.042 (0.750–1.447) NA 
NM 0.043 0.546 (0.304–0.981) NA 0.129 0.407 (0.128–1.300) NA 0.024 0.524 (0.299–0.919) NA 0.274 0.532 (0.172–1.648) NA 0.026 0.603 (0.387–0.941) NA 
Cancer type                 
Laryngeal cancer 0.008 (0.56) 0.604 (0.417–0.875) 0.022 (0.154) 0.413 (0.193–0.881) 0.0 0.002 (0.014) 0.573 (0.401–0.819) 0.0 0.081 0.518 (0.247–1.084) 0.0 0.002 (0.014) 0.638 (0.481–0.847) 0.0 
Lung cancer 0.469 1.426 (0.546–3.726) NA 0.98 1.037 (0.064–16.860) NA 0.488 1.383 (0.553–3.458) NA 1.000 (0.062–16.230) NA 0.52 1.323 (0.565–3.096) NA 
Hepatocellular carcinoma 0.443 1.449 (0.561–3.744) NA NA NA NA 0.443 1.449 (0.561–3.744) NA NA NA NA 0.45 1.435 (0.563–3.660) NA 
Renal cell carcinoma 0.922 1.020 (0.687–1.514) NA 0.79 1.133 (0.451–2.848) NA 0.862 1.034 (0.710–1.505) NA 0.797 1.128 (0.451–2.820) NA 0.807 1.042 (0.750–1.447) NA 
DROSHA rs642321 (C > T) 0.576 0.918 (0.682–1.237) 0.0 0.6721 0.934 (0.683–1.279) 60.5 0.6031 0.923 (0.681–1.250) 72.2 0.911 0.991 (0.843–1.165) 0.6651 0.965 (0.821–1.134) 62.3 
DROSHA rs2291109 (A > T) 0.3891 0.922 (0.765–1.110) 58.8 0.92 1.014 (0.771–1.333) 44.7 0.4691 0.932 (0.770–1.128) 63.8 0.746 1.046 (0.798–1.371) 37.4 0.6061 0.958 (0.815–1.126) 64.6 
Cancer type                 
Breast cancer 0.8511 0.977 (0.770–1.240) 64.9 0.8991 0.962 (0.530–1.747) 69.2 0.8591 0.975 (0.738–1.289) 76.4 0.9091 0.971 (0.580–1.624) 59.6 0.8711 0.978 (0.752–1.272) 81 
Bladder cancer 0.062 0.810 (0.650–1.011) NA 0.57 1.156 (0.702–1.903) NA 0.12 0.845 (0.683–1.045) NA 0.373 1.251 (0.765–2.046) NA 0.332 0.917 (0.768–1.093) NA 
Source of controls                 
PB 0.8511 0.977 (0.770–1.240) 64.9 0.8991 0.962 (0.530–1.747) 69.2 0.8591 0.975 (0.738–1.289) 76.4 0.9091 0.971 (0.580–1.624) 59.6 0.8711 0.978 (0.752–1.272) 81 
HB 0.062 0.810 (0.650–1.011) NA 0.57 1.156 (0.702–1.903) NA 0.12 0.845 (0.683–1.045) NA 0.373 1.251 (0.765–2.046) NA 0.332 0.917 (0.768–1.093) NA 
DGCR8 rs3757 (G > A) 0.892 1.022 (0.750–1.391) 0.0 0.741 0.894 (0.460–1.737) 0.974 1.005 (0.748–1.350) 0.715 0.885 (0.460–1.704) 0.913 0.986 (0.772–1.260) 0.0 
DGCR8 rs417309 (G > A) 0.012 (0.084) 1.282 (1.057–1.555) 0.0 0.001 (0.007) 3.169 (1.634–6.146) 0.001 0.007) 1.365 (1.131–1.647) 0.001 (0.007) 3.026 (1.574–5.817) 6.90E-05 (4.83E-04) 1.423 (1.196–1.693) 0.0 
Ethnicity                 
Asian 0.004 (0.028) 1.420 (1.115–1.809) 0.0 0.303 3.289 (0.341–31.682) NA 0.003 0.021) 1.435 (1.129–1.825) 0.314 3.204 (0.333–30.856) NA 0.003 (0.021) 1.429 (1.131–1.806) 0.0 
Caucasian 0.699 1.066 (0.772–1.472) 0.0 0.001 (0.007) 3.157 (1.579–6.310) 0.133 1.260 (0.932–1.704) 0.002 (0.014) 3.009 (1.520–5.954) 0.009 (0.063) 1.415 (1.092–1.834) 2.7 
Cancer type                 
laryngeal cancer 0.387 1.199 (0.795–1.810) 0.0 0.003 (0.021) 3.059 (1.474–6.351) 0.05 1.460 (1.000–2.131) 0.004 (0.028) 2.895 (1.410–5.945) 0.003 (0.021) 1.604 (1.176–2.188) 0.0 
Breast cancer 0.003 (0.021) 1.465 (1.141–1.881) 0.0 0.303 3.289 (0.341–31.682) NA 0.002 (0.014) 1.481 (1.155–1.898) 0.314 3.204 (0.333–30.856) NA 0.002 (0.014) 1.473 (1.157–1.876) 0.0 
Lung cancer 0.783 0.869 (0.321–2.355) NA NA NA NA 0.783 0.869 (0.321–2.355) NA NA NA NA 0.788 0.875 (0.330–2.316) NA 
Renal cell carcinoma 0.638 0.882 (0.523–1.487) NA 0.217 4.000 (0.444–36.046) NA 0.91 0.971 (0.587–1.609) NA 0.212 4.059 (0.451–36.544) NA 0.797 1.064 (0.664–1.705) NA 
Source of controls                 
PB 0.012 (0.084) 1.333 (1.065–1.669) 33.7 0.107 3.652 (0.755–17.659) 0.006 (0.042) 1.364 (1.093–1.704) 12.4 0.036 (0.252) 2.659 (1.064–6.643) NA 0.059 1.434 (0.986–2.085) 14.7 
HB 0.648 1.117 (0.694–1.799) 0.0 0.029 (0.203) 2.813 (1.109–7.135) NA 0.243 1.302 (0.836–2.030) 0.108 3.635 (0.752–17.564) 0.003 (0.021) 1.377 (1.111–1.707) 0.0 
NM 0.585 1.193 (0.634–2.243) NA 0.04 3.450 (1.057–11.265) NA 0.184 1.484 (0.828–2.658) NA 0.047 3.273 (1.018–10.523) NA 0.04 1.656 (1.022–2.684) NA 
DGCR8 rs1640299 (T > G) 0.5081 0.895 (0.645–1.243) 60.2 0.988 0.998 (0.751–1.325) 0.494 0.898 (0.659–1.223) 59.1 0.786 0.965 (0.745–1.249) 0.48 0.959 (0.852–1.078) 34.6 
Ethnicity                 
Asian 0.403 0.923 (0.766–1.113) 0.0 0.91 0.979 (0.674–1.420) 0.434 0.931 (0.778–1.114) 0.952 1.011 (0.703–1.455) 0.545 0.957 (0.829–1.104) 0.0 
Caucasian 0.7691 0.870 (0.344–2.202) 85.3 0.7261 0.853 (0.350–2.076) 57.3 0.7131 0.844 (0.342–2.085) 85.6 0.656 0.920 (0.638–1.328) 0.5761 0.864 (0.517–1.443) 77.9 
Source of controls                 
PB 0.6431 1.086 (0.767–1.537) 60.2 0.788 1.043 (0.770–1.412) 0.6941 1.063 (0.785–1.439) 53.5 0.831 0.971 (0.738–1.276) 0.974 0.998 (0.879–1.133) 0.0 
HB 0.32 0.740 (0.408–1.339) NA 0.712 1.255 (0.375–4.197) NA 0.433 0.797 (0.452–1.405) NA 0.565 1.415 (0.433–4.623) NA 0.682 0.908 (0.574-–1.438) NA 
NM 0.033 0.529 (0.295–0.949) NA 0.175 0.462 (0.151–1.410) NA 0.023 0.519 (0.295–0.915) NA 0.424 0.645 (0.221–1.886) NA 0.042 0.643 (0.421–0.984) NA 
  Heterozygote compared with homozygote wild Homozygote variant compared with homozygote wild Dominant model Recessive model Allelic model 
SNPs n P (Pcorr) OR (95% CI) I2 (%) P (Pcorr) OR (95% CI) I2 (%) P (Pcorr) OR (95% CI) I2 (%) P (Pcorr) OR (95% CI) I2 (%) P (Pcorr) OR (95% CI) I2 (%) 
DROSHA rs10719 (T > C) 0.934 1.004 (0.904–1.117) 0.2 0.055 1.214 (0.996–1.480) 0.0 0.502 1.035 (0.936–1.145) 10.5 0.053 1.210 (0.998–1.467) 0.0 0.179 1.056 (0.975–1.145) 
Ethnicity                 
Asian 0.874 1.009 (0.904–1.126) 15.6 0.048 (0.336) 1.230 (1.001–1.511) 0.0 0.449 1.041 (0.938–1.157) 10.5 0.04 (0.336) 1.223 (1.002–1.494) 0.0 0.154 1.062 (0.978–1.154) 
Caucasian 0.796 0.950 (0.645–1.400) NA 0.944 1.027 (0.491–2.148) NA 0.838 0.963 (0.668–1.387) NA 0.907 1.044 (0.504–2.161) NA 0.904 0.981 (0.725–1.329) NA 
Source of controls                 
HB 0.905 0.992 (0.869–1.132) 30.1 0.071 1.257 (0.981–1.610) 0.643 1.030 (0.908–1.169) 27.2 0.061 1.259 (0.989–1.602) 0.25 1.060 (0.0960–1.172) 16.7 
PB 0.767 1.027 (0.861–1.225) 0.396 1.142 (0.822–1.588) 0.0 0.616 1.044 (0.883–1.234) 0.463 1.128 (0.818–1.555) 0.0 0.398 1.049 (0.918-1.199) 
DROSHA rs6877842 (G > C) 0.174 0.841 (0.655–1.079) 39.3 0.101 0.627 (0.358–1.096) 0.0 0.3581 0.839 (0.577–1.220) 50.7 0.218 0.706 (0.406–1.228) 0.0 0.073 0.832 (0.680–1.017) 48.2 
Ethnicity                 
Asian 0.292 1.438 (0.732–2.824) 0.98 1.037 (0.064–16.860) NA 0.303 1.414 (0.731–2.735) 0.0 1.000 (0.062–16.230) NA 0.326 1.372 (0.731–2.576) 
Caucasian 0.059 0.772 (0.590–1.010) 47.1 0.094 0.613 (0.346–1.087) 27.5 0.1251 0.716 (0.467–1.097) 61.1 0.21 0.697 (0.396–1.225) 0.0 0.1231 0.762 (0.540–1.076) 60.1 
Source of controls                 
HB 0.395 0.845 (0.574–1.245) 44.3 0.103 0.460 (0.181–1.169) 0.881 0.953 (0.506–1.793) 52.5 0.194 0.545 (0.218–1.361) 0.155 0.796 (0.581–1.090) 48.0 
PB 0.922 1.020 (0.687–1.514) NA 0.79 1.133 (0.451–2.848) NA 0.862 1.034 (0.710–1.505) NA 0.797 1.128 (0.451–2.820) NA 0.807 1.042 (0.750–1.447) NA 
NM 0.043 0.546 (0.304–0.981) NA 0.129 0.407 (0.128–1.300) NA 0.024 0.524 (0.299–0.919) NA 0.274 0.532 (0.172–1.648) NA 0.026 0.603 (0.387–0.941) NA 
Cancer type                 
Laryngeal cancer 0.008 (0.56) 0.604 (0.417–0.875) 0.022 (0.154) 0.413 (0.193–0.881) 0.0 0.002 (0.014) 0.573 (0.401–0.819) 0.0 0.081 0.518 (0.247–1.084) 0.0 0.002 (0.014) 0.638 (0.481–0.847) 0.0 
Lung cancer 0.469 1.426 (0.546–3.726) NA 0.98 1.037 (0.064–16.860) NA 0.488 1.383 (0.553–3.458) NA 1.000 (0.062–16.230) NA 0.52 1.323 (0.565–3.096) NA 
Hepatocellular carcinoma 0.443 1.449 (0.561–3.744) NA NA NA NA 0.443 1.449 (0.561–3.744) NA NA NA NA 0.45 1.435 (0.563–3.660) NA 
Renal cell carcinoma 0.922 1.020 (0.687–1.514) NA 0.79 1.133 (0.451–2.848) NA 0.862 1.034 (0.710–1.505) NA 0.797 1.128 (0.451–2.820) NA 0.807 1.042 (0.750–1.447) NA 
DROSHA rs642321 (C > T) 0.576 0.918 (0.682–1.237) 0.0 0.6721 0.934 (0.683–1.279) 60.5 0.6031 0.923 (0.681–1.250) 72.2 0.911 0.991 (0.843–1.165) 0.6651 0.965 (0.821–1.134) 62.3 
DROSHA rs2291109 (A > T) 0.3891 0.922 (0.765–1.110) 58.8 0.92 1.014 (0.771–1.333) 44.7 0.4691 0.932 (0.770–1.128) 63.8 0.746 1.046 (0.798–1.371) 37.4 0.6061 0.958 (0.815–1.126) 64.6 
Cancer type                 
Breast cancer 0.8511 0.977 (0.770–1.240) 64.9 0.8991 0.962 (0.530–1.747) 69.2 0.8591 0.975 (0.738–1.289) 76.4 0.9091 0.971 (0.580–1.624) 59.6 0.8711 0.978 (0.752–1.272) 81 
Bladder cancer 0.062 0.810 (0.650–1.011) NA 0.57 1.156 (0.702–1.903) NA 0.12 0.845 (0.683–1.045) NA 0.373 1.251 (0.765–2.046) NA 0.332 0.917 (0.768–1.093) NA 
Source of controls                 
PB 0.8511 0.977 (0.770–1.240) 64.9 0.8991 0.962 (0.530–1.747) 69.2 0.8591 0.975 (0.738–1.289) 76.4 0.9091 0.971 (0.580–1.624) 59.6 0.8711 0.978 (0.752–1.272) 81 
HB 0.062 0.810 (0.650–1.011) NA 0.57 1.156 (0.702–1.903) NA 0.12 0.845 (0.683–1.045) NA 0.373 1.251 (0.765–2.046) NA 0.332 0.917 (0.768–1.093) NA 
DGCR8 rs3757 (G > A) 0.892 1.022 (0.750–1.391) 0.0 0.741 0.894 (0.460–1.737) 0.974 1.005 (0.748–1.350) 0.715 0.885 (0.460–1.704) 0.913 0.986 (0.772–1.260) 0.0 
DGCR8 rs417309 (G > A) 0.012 (0.084) 1.282 (1.057–1.555) 0.0 0.001 (0.007) 3.169 (1.634–6.146) 0.001 0.007) 1.365 (1.131–1.647) 0.001 (0.007) 3.026 (1.574–5.817) 6.90E-05 (4.83E-04) 1.423 (1.196–1.693) 0.0 
Ethnicity                 
Asian 0.004 (0.028) 1.420 (1.115–1.809) 0.0 0.303 3.289 (0.341–31.682) NA 0.003 0.021) 1.435 (1.129–1.825) 0.314 3.204 (0.333–30.856) NA 0.003 (0.021) 1.429 (1.131–1.806) 0.0 
Caucasian 0.699 1.066 (0.772–1.472) 0.0 0.001 (0.007) 3.157 (1.579–6.310) 0.133 1.260 (0.932–1.704) 0.002 (0.014) 3.009 (1.520–5.954) 0.009 (0.063) 1.415 (1.092–1.834) 2.7 
Cancer type                 
laryngeal cancer 0.387 1.199 (0.795–1.810) 0.0 0.003 (0.021) 3.059 (1.474–6.351) 0.05 1.460 (1.000–2.131) 0.004 (0.028) 2.895 (1.410–5.945) 0.003 (0.021) 1.604 (1.176–2.188) 0.0 
Breast cancer 0.003 (0.021) 1.465 (1.141–1.881) 0.0 0.303 3.289 (0.341–31.682) NA 0.002 (0.014) 1.481 (1.155–1.898) 0.314 3.204 (0.333–30.856) NA 0.002 (0.014) 1.473 (1.157–1.876) 0.0 
Lung cancer 0.783 0.869 (0.321–2.355) NA NA NA NA 0.783 0.869 (0.321–2.355) NA NA NA NA 0.788 0.875 (0.330–2.316) NA 
Renal cell carcinoma 0.638 0.882 (0.523–1.487) NA 0.217 4.000 (0.444–36.046) NA 0.91 0.971 (0.587–1.609) NA 0.212 4.059 (0.451–36.544) NA 0.797 1.064 (0.664–1.705) NA 
Source of controls                 
PB 0.012 (0.084) 1.333 (1.065–1.669) 33.7 0.107 3.652 (0.755–17.659) 0.006 (0.042) 1.364 (1.093–1.704) 12.4 0.036 (0.252) 2.659 (1.064–6.643) NA 0.059 1.434 (0.986–2.085) 14.7 
HB 0.648 1.117 (0.694–1.799) 0.0 0.029 (0.203) 2.813 (1.109–7.135) NA 0.243 1.302 (0.836–2.030) 0.108 3.635 (0.752–17.564) 0.003 (0.021) 1.377 (1.111–1.707) 0.0 
NM 0.585 1.193 (0.634–2.243) NA 0.04 3.450 (1.057–11.265) NA 0.184 1.484 (0.828–2.658) NA 0.047 3.273 (1.018–10.523) NA 0.04 1.656 (1.022–2.684) NA 
DGCR8 rs1640299 (T > G) 0.5081 0.895 (0.645–1.243) 60.2 0.988 0.998 (0.751–1.325) 0.494 0.898 (0.659–1.223) 59.1 0.786 0.965 (0.745–1.249) 0.48 0.959 (0.852–1.078) 34.6 
Ethnicity                 
Asian 0.403 0.923 (0.766–1.113) 0.0 0.91 0.979 (0.674–1.420) 0.434 0.931 (0.778–1.114) 0.952 1.011 (0.703–1.455) 0.545 0.957 (0.829–1.104) 0.0 
Caucasian 0.7691 0.870 (0.344–2.202) 85.3 0.7261 0.853 (0.350–2.076) 57.3 0.7131 0.844 (0.342–2.085) 85.6 0.656 0.920 (0.638–1.328) 0.5761 0.864 (0.517–1.443) 77.9 
Source of controls                 
PB 0.6431 1.086 (0.767–1.537) 60.2 0.788 1.043 (0.770–1.412) 0.6941 1.063 (0.785–1.439) 53.5 0.831 0.971 (0.738–1.276) 0.974 0.998 (0.879–1.133) 0.0 
HB 0.32 0.740 (0.408–1.339) NA 0.712 1.255 (0.375–4.197) NA 0.433 0.797 (0.452–1.405) NA 0.565 1.415 (0.433–4.623) NA 0.682 0.908 (0.574-–1.438) NA 
NM 0.033 0.529 (0.295–0.949) NA 0.175 0.462 (0.151–1.410) NA 0.023 0.519 (0.295–0.915) NA 0.424 0.645 (0.221–1.886) NA 0.042 0.643 (0.421–0.984) NA 

The results are in bold if P<0.05. Abbreviation: Pcorr, P-values after Bonferroni correction.

1, P was calculated by random model.

In subgroup analyses, rs10719 and rs6877842 SNPs were analyzed in ‘ethnicity’ subgroup; the rs6877842 and rs2291109 SNPs were analyzed in ‘cancer type’ subgroup; rs10719, rs6877842, and rs2291109 SNPs were analyzed in ’source of controls’ subgroup. For rs10719 T/C SNP, its homozygote variant genotype and recessive models were correlated with an elevated cancer risk in Asian (CC compared with TT: OR = 1.230, 95% CI = 1.001–1.511, P=0.048; CC + CT compared with TT: OR = 1.223, 95% CI = 1.002–1.494, P=0.048, Table 3). For rs6877842 G/C SNP, its heterozygote model had strong correlation with a reduced risk of laryngeal cancer (CG compared with GG: OR = 0.413, 95% CI = 0.193–0.881, P=0.022, Table 3) and its homozygote variant genotype, dominant and allelic models had moderate associations with a descending risk of laryngeal cancer (CC compared with GG: OR = 0.604, 95% CI = 0.417–0.875, P=0.008; CC compared with CG + GG: OR = 0.573, 95% CI = 0.401–0.819, P=0.002; C compared with G: OR = 0.638, 95% CI = 0.481–0.847, P=0.002, Table 3). For rs2291109 polymorphism, however, the correlations with cancer risk were not elucidated in any stratified analyses.

Three SNPs in DGCR8

We evaluated the correlation strength of the polymorphisms in DROSHA gene with cancer risk, based on the entire population. The rs417309 G/A SNP was demonstrated to be associated with an increased risk of cancer. Strong associations of rs417309 were found in homozygote variant genotype and recessive models (AA compared with GG: OR = 3.169, 95% CI = 1.634–6.146, P=0.001; AA + AG compared with GG: OR = 3.026, 95% CI = 1.574–5.817, P=0.001). Correlations of rs417309 could also be found in other three models (AG compared with GG: OR = 1.282, 95% CI = 1.057–1.555, P=0.012; AA compared with AG + GG: OR = 1.365, 95% CI = 1.131–1.647, P=0.001; A compared with G: OR = 1.423, 95% CI = 1.196–1.693, P<0.001, Table 3). Associations of the rs3757 G/A and rs1640299 T/G SNPs with cancer risk were not illustrated in primary analyses.

In stratified analyses, rs417309 and rs1640299 SNPs were analyzed in ‘ethnicity’ and ‘source of controls’ subgroups; the rs417309 SNP was also analyzed in ‘cancer type’ subgroup. For rs417309 G/A SNP, its associations were observed in every subgroup (including: Asian population, Caucasian population; laryngeal cancer, breast cancer; PB, HB). Amongst all significant associations in subgroup analyses, only strong associations were reported below. In Caucasian subgroup, strong correlations were indicated in homozygote variant genotype and recessive models (AA compared with GG: OR = 3.169, 95% CI = 1.634–6.146, P=0.001; AA + AG compared with GG: OR = 3.026, 95% CI = 1.574–5.817, P=0.001). In laryngeal cancer subgroup, strong relationships were observed in homozygote variant genotype and recessive models (AA compared with GG: OR = 3.169, 95% CI = 1.634–6.146, P=0.001; AA + AG compared with GG: OR = 3.026, 95% CI = 1.574–5.817, P=0.001), When the control groups were PB, the recessive type (AA + AG) showed a strong relationship with an increased risk of cancer, compared with the wild-type GG (OR = 1.604, 95% CI = 1.176–2.188, P=0.003). When the controls groups were HB, the homozygote variant model of rs417309 presented a strong correlation with cancer risk (AA compared with GG: OR = 2.813, 95% CI = 1.109–7.135, P=0.029, Table 3). For rs1640299 T/G polymorphism, however, no significant relationship was found in any subgroup analyses.

Sensitivity analysis

Sensitivity analyses were conducted to calculate the effect of individual study on the merged findings by evaluating the sensitivity before and after eliminating each study from our meta-analysis (Supplementary Table S1). For rs417309 SNP, it was no longer statistically significant after we removed the study conducted by Jiang et al. (Supplementary Table S1) [2].

Publication bias

Begg’s and Egger’s tests were performed to evaluate the potential publication bias. The publication bias was revealed in the heterozygote genotype and the dominant models of rs10719 SNP in both Begg’s and Egger’s tests, for P<0.1 (Table 4). This might be due to the language bias, the lack of publications with opposing results, and/or the inflated estimates caused by a deficient methodological design in smaller studies [1].

Table 4
The results of Begg’s and Egger’s tests for the publication bias
 Begg’s test Egger’s test 
Comparison type Z-value P-value t-value P-value 
DROSHA rs10719 (T > C)     
Heterozygote compared with homozygote wild −2.250 0.024 −3.030 0.029 
Homozygote variant compared with homozygote wild 0.450 0.652 0.300 0.774 
Dominant model −1.950 0.051 −2.340 0.066 
Recessive model 0.560 0.573 1.100 0.332 
Allelic model −0.750 0.453 −1.330 0.241 
DROSHA rs6877842 (G > C)     
Heterozygote compared with homozygote wild −0.490 0.624 0.620 0.581 
Homozygote variant compared with homozygote wild 0.000 1.000 0.020 0.988 
Dominant model 0.000 1.000 0.500 0.650 
Recessive model 0.000 1.000 0.030 0.976 
Allelic model 0.000 1.000 0.740 0.511 
DROSHA rs642321 (C > T)     
Heterozygote compared with homozygote wild −1.000 0.317 NA NA 
Homozygote variant compared with homozygote wild −1.000 0.317 NA NA 
Dominant model −1.000 0.317 NA NA 
Recessive model −1.000 0.317 NA NA 
Allelic model −1.000 0.317 NA NA 
DROSHA rs2291109 (A > T)     
Heterozygote compared with homozygote wild −1.570 0.117 −1.270 0.426 
Homozygote variant compared with homozygote wild 0.520 0.602 0.560 0.673 
Dominant model −0.520 0.602 −0.830 0.558 
Recessive model 0.520 0.602 0.870 0.545 
Allelic model −0.520 0.602 −0.430 0.741 
DGCR8 rs3757 (G > A)     
Heterozygote compared with homozygote wild 1.000 0.317 NA NA 
Homozygote variant compared with homozygote wild 1.000 0.317 NA NA 
Dominant model 1.000 0.317 NA NA 
Recessive model 1.000 0.317 NA NA 
Allelic model 1.000 0.317 NA NA 
DGCR8 rs417309 (G > A)     
Heterozygote compared with homozygote wild −0.940 0.348 −2.150 0.098 
Homozygote variant compared with homozygote wild 0.680 0.497 1.460 0.282 
Dominant model −1.320 0.188 −1.460 0.219 
Recessive model 0.680 0.497 1.710 0.230 
Allelic model −0.190 0.851 −1.320 0.258 
DGCR8 rs1640299 (T > G)     
Heterozygote compared with homozygote wild −0.680 0.497 −0.540 0.644 
Homozygote variant compared with homozygote wild 0.000 1.000 −0.560 0.635 
Dominant model −0.680 0.497 −0.560 0.634 
Recessive model 0.000 1.000 −0.070 0.949 
Allelic model −0.680 0.497 −0.880 0.471 
 Begg’s test Egger’s test 
Comparison type Z-value P-value t-value P-value 
DROSHA rs10719 (T > C)     
Heterozygote compared with homozygote wild −2.250 0.024 −3.030 0.029 
Homozygote variant compared with homozygote wild 0.450 0.652 0.300 0.774 
Dominant model −1.950 0.051 −2.340 0.066 
Recessive model 0.560 0.573 1.100 0.332 
Allelic model −0.750 0.453 −1.330 0.241 
DROSHA rs6877842 (G > C)     
Heterozygote compared with homozygote wild −0.490 0.624 0.620 0.581 
Homozygote variant compared with homozygote wild 0.000 1.000 0.020 0.988 
Dominant model 0.000 1.000 0.500 0.650 
Recessive model 0.000 1.000 0.030 0.976 
Allelic model 0.000 1.000 0.740 0.511 
DROSHA rs642321 (C > T)     
Heterozygote compared with homozygote wild −1.000 0.317 NA NA 
Homozygote variant compared with homozygote wild −1.000 0.317 NA NA 
Dominant model −1.000 0.317 NA NA 
Recessive model −1.000 0.317 NA NA 
Allelic model −1.000 0.317 NA NA 
DROSHA rs2291109 (A > T)     
Heterozygote compared with homozygote wild −1.570 0.117 −1.270 0.426 
Homozygote variant compared with homozygote wild 0.520 0.602 0.560 0.673 
Dominant model −0.520 0.602 −0.830 0.558 
Recessive model 0.520 0.602 0.870 0.545 
Allelic model −0.520 0.602 −0.430 0.741 
DGCR8 rs3757 (G > A)     
Heterozygote compared with homozygote wild 1.000 0.317 NA NA 
Homozygote variant compared with homozygote wild 1.000 0.317 NA NA 
Dominant model 1.000 0.317 NA NA 
Recessive model 1.000 0.317 NA NA 
Allelic model 1.000 0.317 NA NA 
DGCR8 rs417309 (G > A)     
Heterozygote compared with homozygote wild −0.940 0.348 −2.150 0.098 
Homozygote variant compared with homozygote wild 0.680 0.497 1.460 0.282 
Dominant model −1.320 0.188 −1.460 0.219 
Recessive model 0.680 0.497 1.710 0.230 
Allelic model −0.190 0.851 −1.320 0.258 
DGCR8 rs1640299 (T > G)     
Heterozygote compared with homozygote wild −0.680 0.497 −0.540 0.644 
Homozygote variant compared with homozygote wild 0.000 1.000 −0.560 0.635 
Dominant model −0.680 0.497 −0.560 0.634 
Recessive model 0.000 1.000 −0.070 0.949 
Allelic model −0.680 0.497 −0.880 0.471 

The results are in bold if P<0.1. Abbreviation: NA, not available.

Discussion

In the present study, total seven SNPs in DROSHA and DGCR8 genes were comprehensively reviewed and analyzed to estimate their associations with the risk of overall cancer. Of these seven SNPs, four (rs6877842, rs642321, rs2291109, rs3757) were analyzed for the first time. Our findings indicated that rs417309 SNP of DGCR8 might facilitate the cancerogenesis. Moreover, correlations with cancer risk could also be observed in stratified analyses of DROSHA rs10719, rs6877842 SNPs and DGCR8 rs417309 SNP. No associations were revealed amongst other studied SNPs.

Polymorphisms in DROSHA

As an RNase III superfamily member, DROSHA initiates miRNA processing by converting pri-miRNA into pre-miRNA. Current studies have indicated the role of DROSHA on the development of several sorts of cancers such as laryngeal, bladder, lung, and so on [13–16]. And mounting studies have focussed on the correlations of DROSHA polymorphisms with cancer risk. Based on our analyses, the significant associations with cancer risk could be observed in rs10719 and rs6877842 SNPs.

Regarding rs10719 T/C polymorphism, we presented significant associations between rs10719 SNP (CC or TC + CC genotypes) and cancer risk in Asian population. Located in the DROSHA 3′-UTR region, the T to C substitution of rs10719 disrupted an hsa-miR-27b binding site, which was identified by luciferase reported gene assays, leading to an overexpression of DROSHA gene at the post-transcriptional level [16]. The overexpression of DROSHA caused by rs10719-C allele was elucidated to facilitate the proliferation and inhibit apoptosis of cancer cells [17–19], which was in-line with our meta-analysis findings. The meta-analysis of rs10719 analyzed six case–control studies, five of which, however, were inconsistent with our study. From our viewpoint, this phenomenon might be owing to the limitation of sample size, diversity of cancer type, and/or complexity of environmental factors. Hence, further investigations that concentrate on rs10719 SNP are extremely needed to obtain more credible results.

As for rs6877842 G/C polymorphism, strong/moderate correlations with the laryngeal cancer could be observed in every genetic model except recessive model and the rs687742-C allele manifested a protective effect on laryngeal cancer. Located in the promoter region of DROSHA, the rs6877842 SNP might influence the expression level of DROSHA by altering the transcription factor binding sites, which was forecasted by a bioinformatics website ‘https://snpinfo.niehs.nih.gov/’, thus inhibiting the laryngeal cancer development. Our study analyzed five case–control studies on different cancers: laryngeal (two), lung (one), hepatocellular (one), and renal cell carcinoma (one). Interestingly, the association of rs6877842 SNP could only be observed in laryngeal cancer subgroup, rather than in the overall cancer analysis. And the between-study heterogeneity was absent after we conducted the ‘cancer type’ subgroup. Thus, it is reasonable to suggest that the effect of rs6877842 SNP on overall cancer susceptibility could be masked by the existence of heterogeneity deriving from different types of cancer. Further investigations on this SNP are in demand to verify our speculation.

Polymorphisms in DGCR8

The Drosha–DGCR8 microprocessor complex could mediate the biogenesis from pri-miRNA to pre-miRNA, whereas neither Drosha or recombinant DGCR8 alone is active in this processing, suggesting that both the proteins are indispensable in miRNA maturing processing [20]. DGCR8 are also referred to as Pasha, stabilizes Drosha by protein–protein, and takes charge of recognizing ssRNA and dsRNA structures [21]. Studies have revealed the up-regulation of DGCR8 expression in various cancers [22,23]. And accumulating researches have focussed on the associations between the DGCR8 polymorphisms and cancer risk.

The rs417309 G/A polymorphism was the most extensively investigated one amongst DGCR8 SNPs, and the rs417309-A allele was strongly associated with an elevated cancer susceptibility. Based on the bioinformatics website prediction ‘https://snpinfo.niehs.nih.gov/’, rs417909 SNP was located at miRNA-binding sites (miR-106b and miR-579) in 3′-UTR region of DGCR8. The risk allele rs417309-A could elevate DGCR8 expression level, probably through interrupting miRNA binding [2], thus facilitating the cancer development. The meta-analysis of rs417309 SNP involved six case–control studies. Two of them, however, showed no association with cancer risk. Thus, further investigations concerned with rs417309 SNP remain in strong demand for identifying this potential cancer biomarker.

Limitations in our meta-analysis must be recognized. First, only eligible articles published in English were incorporated in our study, which might result in certain publication bias. Second, studies of DROSHA and DGCR8 polymorphisms on cancer predisposition field remain emerging, which resulted in limited number of the relevant investigations. Third, we did not analyze the association of polymorphisms in other miRNA-machinery genes, which were listed in Table 5 including: DICER1, XPO5, RAN, TARBP2, AGO2, HIWI, GEMIN3, and GEMIN4. Because their study number was limited or because they have already been analyzed in other meta-analyses.

Table 5
Reviews of the other miRNA-machinery gene polymorphisms studied in regard to cancer risk
Gene SNP Position Cancer type Citation 
XPO5 rs11077 (A > G) 3′-UTR EC, BC, CRC, TC, RCC, bladder, and larynx cancer [2,14,15,25,29–34
RAN rs14035 (C > T) 3′-UTR RCC, LC, CRC, EC, GC, OC, and larynx cancer [14,15,24,25,29,32–35
 rs3803012 (A > G) 3′-UTR BC, HNC, CC, and HCC [2,36–38
 rs3809142 (C > T) Upstream BC [2
 rs7301722 (C > A) Upstream BC [2
 rs7958223 (C > A) Intron BC [31
 rs10848236 (G > A) Intron BC [31
DICER1 rs1057035 (T > C) 3′-UTR BC [2,31
 rs3742330 (A > G) 3′UTR PC, LC, and larynx cancer [15,25,39
 rs2282265 (A > G) Intron BC [31
 rs13078 (T > A) 3′-UTR LC and larynx [15,25
TARBP2 rs784567 (A > G) 5′-UTR PC and larynx [25
 rs2280448 (C > T) 5′-UTR BC [31
AGO1 rs595055 (G > A) Intron BC [31
 rs11263833 (T > G) Intron BC [31
 rs636832 (A > G) Intron LC [15
 rs595961 (G > A) Intron LC and RCC [14,15
AGO2 rs4961280 (A > C) Upstream PC [39
 rs77216619 (G/T) Intron BC [2
 rs78796470 (C > T) Intron BC [2
 rs2292779 (G > C) Intron BC [31
 rs3864659 (A > C) Intron BC [31
 rs7016981 (T > C) Intron BC [31
 rs7824304 (C > T) Intron BC [31
 rs11786030 (A > G) 3′-UTR BC [31
HIWI rs10773771 (T > C) 3′-UTR BC [2
 rs4759659 (G > A) Intron BC [31
 rs7963072 (G > A) Intron BC [31
 rs1106042 (G > A) Exon (K527R) BC and LC [15,31
 rs11060845 (G > T) Intron BC [31
GEMIN3 rs197414 (C > A) Exon (S693R) PC, BC, and LC [15
 rs197388 (T > A) Upstream LC [15
 rs197412 (T > C) Exon (T636I) LC, RCC, and OC [14,15,35
 rs11584657 (C > T) Upstream BC [2
 rs17504173 (A > G) 3′-UTR BC [2
 rs197413 (G > A) Exon (V642V) BC [31
 rs17569368 (A > T) Intron BC [31
GEMIN4 rs7813 (C > T) Exon (C1033R) PC,BC, LC, and RCC [14,15,31
 rs3744741 (C > T) Exon (R684Q) BC and LC [2,15,31
 rs4968104 (T > A) Exon (V593E) BC and LC [2,15,31
 rs2251689 (G > A) Upstream BC [2
 rs2740348 (C > G) Exon (E450Q) BC and LC [15
 rs910924 (C > T) Upstream LC [15
 rs910925 (G > C) Exon (G579A) LC [15
 rs1062923 (T > C) Exon (T739I) LC [15
 rs2740349 (A > G) Exon (N929D) BC [31
FMR1 rs25704 (T > C) 3′-UTR BC [31
 rs28900 (A > C) Intron BC [31
 rs971000 (C > T) Intron BC [31
Gene SNP Position Cancer type Citation 
XPO5 rs11077 (A > G) 3′-UTR EC, BC, CRC, TC, RCC, bladder, and larynx cancer [2,14,15,25,29–34
RAN rs14035 (C > T) 3′-UTR RCC, LC, CRC, EC, GC, OC, and larynx cancer [14,15,24,25,29,32–35
 rs3803012 (A > G) 3′-UTR BC, HNC, CC, and HCC [2,36–38
 rs3809142 (C > T) Upstream BC [2
 rs7301722 (C > A) Upstream BC [2
 rs7958223 (C > A) Intron BC [31
 rs10848236 (G > A) Intron BC [31
DICER1 rs1057035 (T > C) 3′-UTR BC [2,31
 rs3742330 (A > G) 3′UTR PC, LC, and larynx cancer [15,25,39
 rs2282265 (A > G) Intron BC [31
 rs13078 (T > A) 3′-UTR LC and larynx [15,25
TARBP2 rs784567 (A > G) 5′-UTR PC and larynx [25
 rs2280448 (C > T) 5′-UTR BC [31
AGO1 rs595055 (G > A) Intron BC [31
 rs11263833 (T > G) Intron BC [31
 rs636832 (A > G) Intron LC [15
 rs595961 (G > A) Intron LC and RCC [14,15
AGO2 rs4961280 (A > C) Upstream PC [39
 rs77216619 (G/T) Intron BC [2
 rs78796470 (C > T) Intron BC [2
 rs2292779 (G > C) Intron BC [31
 rs3864659 (A > C) Intron BC [31
 rs7016981 (T > C) Intron BC [31
 rs7824304 (C > T) Intron BC [31
 rs11786030 (A > G) 3′-UTR BC [31
HIWI rs10773771 (T > C) 3′-UTR BC [2
 rs4759659 (G > A) Intron BC [31
 rs7963072 (G > A) Intron BC [31
 rs1106042 (G > A) Exon (K527R) BC and LC [15,31
 rs11060845 (G > T) Intron BC [31
GEMIN3 rs197414 (C > A) Exon (S693R) PC, BC, and LC [15
 rs197388 (T > A) Upstream LC [15
 rs197412 (T > C) Exon (T636I) LC, RCC, and OC [14,15,35
 rs11584657 (C > T) Upstream BC [2
 rs17504173 (A > G) 3′-UTR BC [2
 rs197413 (G > A) Exon (V642V) BC [31
 rs17569368 (A > T) Intron BC [31
GEMIN4 rs7813 (C > T) Exon (C1033R) PC,BC, LC, and RCC [14,15,31
 rs3744741 (C > T) Exon (R684Q) BC and LC [2,15,31
 rs4968104 (T > A) Exon (V593E) BC and LC [2,15,31
 rs2251689 (G > A) Upstream BC [2
 rs2740348 (C > G) Exon (E450Q) BC and LC [15
 rs910924 (C > T) Upstream LC [15
 rs910925 (G > C) Exon (G579A) LC [15
 rs1062923 (T > C) Exon (T739I) LC [15
 rs2740349 (A > G) Exon (N929D) BC [31
FMR1 rs25704 (T > C) 3′-UTR BC [31
 rs28900 (A > C) Intron BC [31
 rs971000 (C > T) Intron BC [31

Abbreviations: BC, breast cancer; CC, cervical cancer; CRC, colorectal cancer; EC, esophageal cancer; GC, gastric cancer; HCC, hepatocellular carcinoma; HNC, head and neck cancer; LC, lung cancer; OC, oral cancer; PC, prostate cancer; RCC, renal cell carcinoma; TC, thyroid cancer.

In summary, we performed a systematic review on the association between DROSHA and DGCR8 polymorphisms and risk of cancer. Meanwhile, all available data were utilized to achieve a meta-analysis for seven prevalent SNPs. Three of them (DROSHA rs10719, rs6877842, and DGCR8 rs417309) were revealed to be associated with risk of cancer in whole population or some particular subgroups. Our study generalized the status quo of the current studies on cancer-related polymorphisms in DROSHA and DGCR8 genes, supplying investigators with novel clues for identifying new biomarkers with cancer-forewarning function.

Author contribution

M.S. conceived and designed the study. J.W. and Z.L. were responsible for the data extraction. H.D. and X.F. were responsible for the quality assessment. J.W. and M.S. wrote the manuscript, and M.S. revised the manuscript.

Competing interests

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

Funding

The authors confirm that there is no funding to be acknowledged.

Abbreviations

     
  • HB

    hospital-based

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • OR

    odds ratio

  •  
  • PB

    population-based

  •  
  • pre-miRNA

    precursor miRNA

  •  
  • pri-miRNA

    primary miRNA

  •  
  • SNP

    single nucleotide polymorphism

  •  
  • 95% CI

    95% confidence interval

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This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

Supplementary data