miRNA polymorphisms had potential to be biomarkers for cancer susceptibility and prognosis. The mature miRNA-let-7 family was considered as the most important miRNA for the cancer incidence and progression. Recently, the promising let-7 miRNAs were reported to be associated with various cancers, but the results were inconsistent. We performed a first-reported systematic review with a meta-analysis for the association of let-7 family single nucleotide polymorphisms (SNPs) with cancer risk/prognosis. Ten studies were included with a total of 3878 cancer cases and 4725 controls for the risk study and 1665 cancer patients for the prognosis study in this meta-analysis. In the risk study, the let-7i rs10877887 and let-7a-1/let-7f-1/let-7d rs13293512 were shown no significant association for the overall cancer risk. In the stratified analysis, the rs10877887 variant genotype was significantly associated with a decreased cancer risk in head and neck cancer (TC compared with TT: P=0.017; odds ratio (OR) = 0.81; TC + CC compared with TT: P=0.020; OR = 0.82). In the prognosis study, the let-7i rs10877887 SNP was shown to be associated with a higher risk for cancer prognosis in the dominate model (P=0.004; hazard ratio (HR) = 1.32). The other two SNPs (let-7a-1 rs10739971 and let-7a-2 rs629367) were not found to be associated with cancer survival. None of the let-7 family polymorphisms had potential to be biomarkers for cancer susceptibility but let-7i rs10877887 SNP had potential to be predicting markers for cancer prognosis. In the future, large-sample studies are still needed to verify our findings.

Introduction

From this century, miRNAs were considered as star molecules, instead of ‘trash’, as they worked as a regulatory element for the post-translation of mRNA [1]. miRNAs were also generated from the genome DNA and could transcript and translate into mature miRNA, which was executed in two steps: from pri-miRNA to pre-miRNA, and from pre-miRNA to mature miRNA [2]. As miRNA is small (19–24 nt long) [3], it has the characteristic of stability and thus, has the potential to be the biomarker for the detection in tissues, or even in serum or urine [4]. Other characteristics of miRNA are: first, it could complementarily combine with multiple target sequences and one miRNA could regulate multiple different target genes [1]; second, it has little chance to vary or to mutate [5]. But, if there is a variation in the formation process of miRNA, it could affect the quality and quantity of mature miRNA and even affect hundreds of targeted genes regulated by the changed miRNA [6].

Single nucleotide polymorphisms (SNPs) are the common variation in the genetic polymorphisms and are known as the potential biomarkers for the forecast in cancer risk and predicting the cancer prognosis [7]. Pri-miRNA and pre-miRNA have SNPs which were studied to be associated with cancer risk and prognosis [8,9]. As pri-miRNA is always 500–3000 bp long and pre-miRNA is 60–70 bp long, the existence of pre-miRNA SNPs is limited, and pri-miRNA SNPs are more relative and reported to affect the function of miRNAs [5].

Let-7 family is one of the earliest found miRNAs and composed of ten kinds of miRNAs (let-7a, let-7b, let-7c, let-7d, let-7e, let-7f, let-7g, let-7i, miR-98, and miR-202) [10]. Let-7 family is the most important miRNA acting on carcinogenesis, as Krol et al. found, the pri-miRNA of let-7 family could combine with LIN28 and suppress the splicing procedure of Drosha and Dicer, two important restriction enzymes involved in the maturation process for all miRNAs [11]. In addition, by knocking down the Drosha enzyme to suppress all the miRNA maturation processes comprehensively, Kumar et al. found that the main reason for the activation and promotion of cell’s malignant transformation was the downregulation of let-7 family expression [12]. Thus, let-7 family is essential to suppress the cancer cells’ proliferation, and plays important roles in the carcinogenesis process [13]. The let-7 genetic polymorphisms could have participated in the carcinogenesis process.

The let-7 genetic polymorphisms were reported to be associated with cancer risk and prognosis, but the results were inconsistent. For example, Jing Liu et al. found the let-7i promoter rs10877887 SNP variant C allele could increase cancer risk (odds ratio (OR) = 1.35) [14] while others found the variant C allele could decrease cancer risk [15,16]. Thus, a comprehensive analysis which integrated all individual studies concerning this rs10877887 SNP and all cancer risk/prognosis is still required, as well as all the let-7 family polymorphisms. And until now, a system review or a meta-analysis for the let-7 family polymorphisms was none. These data could expand our understanding of the role of let-7 polymorphisms in human carcinogenesis, which may provide some evidence for future research. Therefore, we systematically reviewed published data and meta-analyzed for let-7 family polymorphisms to give a comprehensive assessment for the associations of let-7 SNPs and cancer risks/prognosis.

Methods

Publication search

A literature searching was executed systematically and comprehensively by two independent investigators (B.G.W. and Q.X.), up to April 18, 2018. The databases contain PubMed, Web of Science, Embase and Chinese National Knowledge Infrastructure (CNKI) using the following key words: ‘let-7/pri-let-7’, ‘SNP/polymorphisms/variation/variant’, and ‘cancer/carcinoma/tumor/neoplasm’. The major inclusion criteria were the literatures concerning the correlation between let-7 polymorphisms and cancer risks/prognosis. When the literature met the followings: (1) reviews or meta-analysis, (2) duplicate records, (3) study for benign disease compared with controls, (4) unrelated to cancer or let-7 polymorphisms; it was judged as the exclusion criteria.

Data extraction

Two authors (B.G.W. and Q.X.) extracted all the data independently, and finally reached a consensus on all the items. In the risk study, the following items were collected: first author, publication year, ethnicity, cancer type, genotyping method, source of control groups (population-based or hospital-based), total number of controls, and cases, and genotype distributions in controls and cases. In the prognosis study, the following information was extracted from the article: first author, publication year, study population, SNP names, compared genetic model, cancer type, sample size, and hazard ratio (HR) estimation. 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 according to a study regarding the method for assigning quality scores, which was mentioned in prior meta-analysis [17]. Six items were evaluated in the quality assessment scale: (1) the representativeness of the cases; (2) the source of controls; (3) the ascertainment of relevant cancers; (4) the sample size; (5) the quality control of the genotyping methods; (6) and Hardy–Weinberg equilibrium (HWE) in controls. The details see Supplementary Table S1. The quality scores of eligible studies ranged from 0 to 10. Studies with a score less than 5 and HWE disequilibrium were removed from the subsequent analyses.

Trial sequential analysis and false-positive report probability analysis

Trial sequential analysis (TSA) was performed as described by user manual for TSA [18]. In brief, TSA software was downloaded from the website (www.ctu.dk/tsa). After adopting a level of significance of 5% for type I error and of 30% for type II error, the required information size was calculated, and TSA monitoring boundaries were built [19,20].

The false-positive report probability (FPRP) values at different prior probability levels for all significant findings were calculated as published reference studies [21–23]. Briefly, 0.2 was set as FPRP threshold and assigned a prior probability of 0.01 for an association with genotypes under investigation. A FPRP value <0.2 denoted a noteworthy association.

Statistics

The HWE was calculated by the Chi-square test in control groups for genotype frequencies of let-7 polymorphisms. The strength of the association between let-7 polymorphisms and cancer susceptibility was measured by ORs and the relationship between let-7 polymorphisms and cancer prognosis was evaluated by HRs. We calculated the between-study heterogeneity by the Cochran’s Q test and quantified by I2 (a significance level of P<0.10). When heterogeneity did not exist, a fixed-effect model was employed [24]; otherwise, a random-effect model was used [25]. A total of five comparison models were conducted, namely heterozygote comparison (CT compared with TT), homozygote comparison (CC compared with TT), dominant model (CT + CC compared with TT), recessive model (CC compared with CT + TT), and allelic model (C compared with T).

Further, we executed stratification analyses on cancer type, source of controls (population-based and hospital-based study design), and sample size (total samples > 1000 or < 1000). The Begg’s rank correlation and the Egger’s linear regression were evaluated for the publication bias [26,27] (P<0.10 as reached statistically significant). All analyses were performed by STATA software, version 11.0 (STATA Corp., College Station, TX, U.S.A.).

Results

Characteristics of the studies

After duplicate literatures removed, 172 records in total were using different combinations of the major keywords. First, according to the title or abstracts screening, we excluded 81 articles (amongst them, 67 were function studies and 14 were reviews or meta-analyses). Second, after full-text reading, 81 studies were excluded (73 were not about let-7 polymorphisms but for the let-7 target gene polymorphisms, 7 were not associated with cancer and 1 was not case–control study). Finally, ten studies that met our inclusion criteria were included in our system review and meta-analysis, which consisted of 3837 cancer patients and 4745 controls in the risk study and 1665 cancer patients in the prognosis study (Figure 1). The characteristics of each study in the risk study were shown in Tables 1 and 2, while in the prognosis study, were presented in Table 3. This meta-analysis complied with PROSMA 2009 Checklist, and for details, see Supplementary Table S2. Amongst these ten studies, two SNPs in let-7 family were found in risk study (let-7i rs10877887 and let-7a-1/let-7f-1/let-7d rs13293512) and three SNPs (let-7i rs10877887, let-7a-1 rs10739971, and let-7a-2 rs629367) were found in prognosis study.

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

Figure 1
Studies identified in this meta-analysis based on the criteria for inclusion and exclusion
Figure 1
Studies identified in this meta-analysis based on the criteria for inclusion and exclusion
Table 1
Characteristics of reviewed literatures for the let-7 family polymorphisms
NumberFirst authorYearEthnicityCancer typeGenotyping methodSource of control groupsSample sizemiRNAsQuality scoreCitation
CaseControl
Jing Liu 2018 Asian Cervical squamous cell carcinoma PCR-RFLP HB 331 358 rs10877887; rs13293512 7.5 [14
ZY Sui 2016 Asian Hepatocellular cancer Sequencing HB 89 95 rs10877887 6.0 [34
LQ Shen 2015 Asian Lung adenocarcinoma Sequencing HB 69 75 rs10877887 6.0 [35
Yichao Wang 2015 Asian Papillary thyroid carcinoma PCR-RFLP HB 618 562 rs10877887; rs13293512 8.5 [15
Yu Zhang 2014 Asian Oral cavity cancer Taqman PB 384 731 rs10877887 8.5 [16
Longbiao Zhu 2014 Asian Head and neck cancer Sequencing PB 497 884 rs10877887; rs13293512 8.5 [31
Qian Xu 2014 Asian Gastric cancer PCR-RFLP; Sequencing; MassAssay PB 579 721 rs629367; rs1143770; rs10739971; rs17276588 8.5 [29
Fang Huang 2011 Asian Hepatocellular cancer Taqman HB 1270 1319 rs10877887; rs13293512 7.0 [28
NumberFirst authorYearEthnicityCancer typeGenotyping methodSource of control groupsSample sizemiRNAsQuality scoreCitation
CaseControl
Jing Liu 2018 Asian Cervical squamous cell carcinoma PCR-RFLP HB 331 358 rs10877887; rs13293512 7.5 [14
ZY Sui 2016 Asian Hepatocellular cancer Sequencing HB 89 95 rs10877887 6.0 [34
LQ Shen 2015 Asian Lung adenocarcinoma Sequencing HB 69 75 rs10877887 6.0 [35
Yichao Wang 2015 Asian Papillary thyroid carcinoma PCR-RFLP HB 618 562 rs10877887; rs13293512 8.5 [15
Yu Zhang 2014 Asian Oral cavity cancer Taqman PB 384 731 rs10877887 8.5 [16
Longbiao Zhu 2014 Asian Head and neck cancer Sequencing PB 497 884 rs10877887; rs13293512 8.5 [31
Qian Xu 2014 Asian Gastric cancer PCR-RFLP; Sequencing; MassAssay PB 579 721 rs629367; rs1143770; rs10739971; rs17276588 8.5 [29
Fang Huang 2011 Asian Hepatocellular cancer Taqman HB 1270 1319 rs10877887; rs13293512 7.0 [28

HB, hospital based; PB, population based; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism.

Table 2
The detailed data for the let-7 family meta-analysis
First authormiRNAsYearCancer typeSource of control groupsSample sizeCaseControlP of HWE
CaseControlTTTCCCTTTCCC
Jing Liu rs10877887 2018 Cervical squamous cell carcinoma HB 331 358 140 131 60 169 155 34 0.860 
ZY Sui rs10877887 2016 Hepatocellular cancer HB 89 95 25 64 64 55 40 40 0.482 
LQ Shen rs10877887 2015 Lung adenocarcinoma HB 69 75 20 44 34 37 0.552 
Yichao Wang rs10877887 2015 Papillary thyroid carcinoma HB 618 562 325 224 69 262 248 52 0.541 
Yu Zhang rs10877887 2014 Oral cavity cancer PB 384 731 172 165 41 291 343 82 0.205 
Fang Huang rs10877887 2011 Hepatocellular cancer HB 1261 1319 542 564 155 581 585 153 0.756 
Longbiao Zhu rs10877887 2014 Head and neck cancer PB 497 884 227 213 57 361 422 101 0.179 
Jing Liu rs13293512 2018 Cervical squamous cell carcinoma HB 331 358 97 163 71 105 186 67 0.340 
Yichao Wang rs13293512 2015 Papillary thyroid carcinoma HB 618 562 165 333 120 158 300 104 0.066 
Fang Huang rs13293512 2011 Hepatocellular cancer HB 1270 1291 406 611 253 427 638 226 0.642 
Longbiao Zhu rs13293512 2014 Head and neck cancer PB 492 893 157 257 78 270 439 184 0.821 
First authormiRNAsYearCancer typeSource of control groupsSample sizeCaseControlP of HWE
CaseControlTTTCCCTTTCCC
Jing Liu rs10877887 2018 Cervical squamous cell carcinoma HB 331 358 140 131 60 169 155 34 0.860 
ZY Sui rs10877887 2016 Hepatocellular cancer HB 89 95 25 64 64 55 40 40 0.482 
LQ Shen rs10877887 2015 Lung adenocarcinoma HB 69 75 20 44 34 37 0.552 
Yichao Wang rs10877887 2015 Papillary thyroid carcinoma HB 618 562 325 224 69 262 248 52 0.541 
Yu Zhang rs10877887 2014 Oral cavity cancer PB 384 731 172 165 41 291 343 82 0.205 
Fang Huang rs10877887 2011 Hepatocellular cancer HB 1261 1319 542 564 155 581 585 153 0.756 
Longbiao Zhu rs10877887 2014 Head and neck cancer PB 497 884 227 213 57 361 422 101 0.179 
Jing Liu rs13293512 2018 Cervical squamous cell carcinoma HB 331 358 97 163 71 105 186 67 0.340 
Yichao Wang rs13293512 2015 Papillary thyroid carcinoma HB 618 562 165 333 120 158 300 104 0.066 
Fang Huang rs13293512 2011 Hepatocellular cancer HB 1270 1291 406 611 253 427 638 226 0.642 
Longbiao Zhu rs13293512 2014 Head and neck cancer PB 492 893 157 257 78 270 439 184 0.821 
Table 3
The characteristics of miRNA SNPs in the prognosis study
Author namePublication yearStudy populationmiRNA-SNPsModelCancer typeSample sizeOutcomeHR95% upper95% lowerCitation
Kyung Min Shin 2016 Korea rs1143770 CT + TT compared with CC Non-small-cell lung cancer 761 OS 0.52 0.79 0.34 [36
Kyung Min Shin 2016 Korea rs629367 CC compared with AA Non-small-cell lung cancer 761 OS 0.92 1.89 0.45 [36
Kyung Min Shin 2016 Korea rs10739971 GA + AA compared with GG Non-small-cell lung cancer 761 OS 1.03 1.42 0.75 [36
Kyung Min Shin 2016 Korea rs17276588 GA + AA compared with GG Non-small-cell lung cancer 761 OS 1.06 1.31 0.86 [36
ZY Sui 2016 China rs10877887 TT compared with CT + CC Hepatocellular cancer 89 OS 0.68 0.94 0.52 [34
Kaipeng Xie 2013 China rs10877887 CT + CC compared with TT Hepatocellular cancer 331 OS 1.23 1.58 0.96 [36
Kaipeng Xie 2013 China rs13293512 CT + CC compared with TT Hepatocellular cancer 331 OS 0.93 1.22 0.71 [36
Ying Li 2015 China rs10739971 GA + AA compared with GG Gastric cancer 334 OS 1.32 4.8 0.36 [37
Qian Xu 2014 China rs629367 CC compared with AA Gastric cancer 150 OS 4.8 12.6 1.6 [29
Author namePublication yearStudy populationmiRNA-SNPsModelCancer typeSample sizeOutcomeHR95% upper95% lowerCitation
Kyung Min Shin 2016 Korea rs1143770 CT + TT compared with CC Non-small-cell lung cancer 761 OS 0.52 0.79 0.34 [36
Kyung Min Shin 2016 Korea rs629367 CC compared with AA Non-small-cell lung cancer 761 OS 0.92 1.89 0.45 [36
Kyung Min Shin 2016 Korea rs10739971 GA + AA compared with GG Non-small-cell lung cancer 761 OS 1.03 1.42 0.75 [36
Kyung Min Shin 2016 Korea rs17276588 GA + AA compared with GG Non-small-cell lung cancer 761 OS 1.06 1.31 0.86 [36
ZY Sui 2016 China rs10877887 TT compared with CT + CC Hepatocellular cancer 89 OS 0.68 0.94 0.52 [34
Kaipeng Xie 2013 China rs10877887 CT + CC compared with TT Hepatocellular cancer 331 OS 1.23 1.58 0.96 [36
Kaipeng Xie 2013 China rs13293512 CT + CC compared with TT Hepatocellular cancer 331 OS 0.93 1.22 0.71 [36
Ying Li 2015 China rs10739971 GA + AA compared with GG Gastric cancer 334 OS 1.32 4.8 0.36 [37
Qian Xu 2014 China rs629367 CC compared with AA Gastric cancer 150 OS 4.8 12.6 1.6 [29

OS, overall survival.

In the risk study, all studies were matched for age; however, only seven studies were matched for sex; the other one did not need sex matching. The controls of five studies were HB, while others were PB; genotyping methods included PCR-RFLP, qPCR and sequencing. All genotypes were checked for quality control and were consistent with HWE. And according to the methodology quality assessment, the studies with a score less than 5 would be removed from the subsequent analyses. All the studies were above a score of 6.0 and recruited into the following analyses.

Quantitative synthesis for the association of SNPs and cancer susceptibility

For the let-7i rs10877887 SNP, the dominate model could collect seven studies while other genetic model could collect six studies. In all the five genetic models, none was shown a significant association between let-7i rs10877887 SNP and overall cancer risk except the recessive model. In the recessive model, when compared with let-7i rs10877887 TT + TC genotype, the variant CC genotype was nearly associated with the overall cancer risk, and the P-value reached 0.066 (OR = 1.15; 95% confidence interval (CI) = 0.99–1.33). For the other SNP rs13293512, no association was found between the SNP and overall cancer risk (Table 4).

Table 4
Pooled ORs and 95% CIs of stratified meta-analysis for the risk study
StratificationGenotypeNOR (95% CI)ZP-valueModelI2(%)
rs10877887        
All cancers        
 TC compared with TT 0.91 (0.76–1.09) 1.04 0.300 60.7 
 CC compared with TT 1.13 (0.87–1.46) 0.93 0.351 54.3 
 TC + CC compared with TT 1.10 (0.86–1.40) 0.77 0.443 80.9 
 CC compared with TT + TC 1.15 (0.99–1.33) 1.84 0.066 45.1 
 C compared with T 1.02 (0.89–1.16) 0.28 0.783 65.4 
Cancer type        
  Hepatocellular cancer        
 CC compared with TT + TC 1.85 (0.56–6.06) 1.01 0.312 92.9 
  Head and neck cancer        
 TC compared with TT 0.81 (0.68–0.96) 2.39 0.017 0.0 
 CC compared with TT 0.88 (0.66–1.15) 0.95 0.341 0.0 
 TC + CC compared with TT 0.82 (0.70–0.97) 2.33 0.020 0.0 
 CC compared with TT + TC 0.98 (0.75–1.27) 0.18 0.857 0.0 
 C compared with T 0.89 (0.76–1.06) 1.80 0.072 0.0 
Source of controls        
  HB        
 TC compared with TT 1.00 (0.76–1.31) 0.02 0.982 70.5 
 CC compared with TT 1.33 (0.94–1.90) 1.59 0.111 57.6 
 TC + CC compared with TT 0.82 (0.70–0.97) 1.55 0.122 84.2 
 CC compared with TT + TC 1.35 (0.97–1.88) 1.76 0.079 56.4 
 C compared with T 1.11 (0.92–1.33) 1.11 0.269 68.5 
  PB        
 TC compared with TT 0.81 (0.68–0.96) 2.39 0.017 0.0 
 CC compared with TT 0.88 (0.66–1.15) 0.95 0.341 0.0 
 TC + CC compared with TT 0.82 (0.70–0.97) 2.33 0.020 0.0 
 CC compared with TT + TC 0.98 (0.75–1.27) 0.18 0.857 0.0 
 C compared with T 0.89 (0.79–1.01) 1.30 0.072 0.0 
Sample size        
  Large        
 TC compared with TT 0.85 (0.72–1.01) 1.86 0.064 56.7 
 CC compared with TT 1.00 (0.84–1.18) 0.02 0.985 0.0 
 TC + CC compared with TT 0.90 (0.82–1.00) 1.98 0.048 50.0a 
 CC compared with TT + TC 1.06 (0.90–1.24) 0.71 0.478 0.0 
 C compared with T 0.96 (0.89–1.03) 1.14 0.256 15.9 
  Small        
 TC compared with TT 1.33 (0.69–2.56) 0.86 0.389 66.6 
 CC compared with TT 2.13 (1.36–3.35) 3.28 0.001 0.0 
 TC + CC compared with TT 1.98(1.01–3.88) 1.98 0.048 79.7 
 CC compared with TT + TC 2.03 (1.32–3.10) 3.24 0.001 0.0 
 C compared with T 1.38 (1.12–1.68) 3.08 0.002 0.0 
rs13293512        
All cancers        
 TC compared with TT 1.01 (0.90–1.14) 0.18 0.861 0.0 
 CC compared with TT 1.04 (0.90–1.22) 0.55 0.579 49.5 
 TC + CC compared with TT 1.02 (0.91–1.14) 0.34 0.731 0.0 
 CC compared with TT + TC 1.02 (0.81–1.28) 0.17 0.869 61.2 
 C compared with T 1.02 (0.95–1.10) 0.52 0.603 34.6 
StratificationGenotypeNOR (95% CI)ZP-valueModelI2(%)
rs10877887        
All cancers        
 TC compared with TT 0.91 (0.76–1.09) 1.04 0.300 60.7 
 CC compared with TT 1.13 (0.87–1.46) 0.93 0.351 54.3 
 TC + CC compared with TT 1.10 (0.86–1.40) 0.77 0.443 80.9 
 CC compared with TT + TC 1.15 (0.99–1.33) 1.84 0.066 45.1 
 C compared with T 1.02 (0.89–1.16) 0.28 0.783 65.4 
Cancer type        
  Hepatocellular cancer        
 CC compared with TT + TC 1.85 (0.56–6.06) 1.01 0.312 92.9 
  Head and neck cancer        
 TC compared with TT 0.81 (0.68–0.96) 2.39 0.017 0.0 
 CC compared with TT 0.88 (0.66–1.15) 0.95 0.341 0.0 
 TC + CC compared with TT 0.82 (0.70–0.97) 2.33 0.020 0.0 
 CC compared with TT + TC 0.98 (0.75–1.27) 0.18 0.857 0.0 
 C compared with T 0.89 (0.76–1.06) 1.80 0.072 0.0 
Source of controls        
  HB        
 TC compared with TT 1.00 (0.76–1.31) 0.02 0.982 70.5 
 CC compared with TT 1.33 (0.94–1.90) 1.59 0.111 57.6 
 TC + CC compared with TT 0.82 (0.70–0.97) 1.55 0.122 84.2 
 CC compared with TT + TC 1.35 (0.97–1.88) 1.76 0.079 56.4 
 C compared with T 1.11 (0.92–1.33) 1.11 0.269 68.5 
  PB        
 TC compared with TT 0.81 (0.68–0.96) 2.39 0.017 0.0 
 CC compared with TT 0.88 (0.66–1.15) 0.95 0.341 0.0 
 TC + CC compared with TT 0.82 (0.70–0.97) 2.33 0.020 0.0 
 CC compared with TT + TC 0.98 (0.75–1.27) 0.18 0.857 0.0 
 C compared with T 0.89 (0.79–1.01) 1.30 0.072 0.0 
Sample size        
  Large        
 TC compared with TT 0.85 (0.72–1.01) 1.86 0.064 56.7 
 CC compared with TT 1.00 (0.84–1.18) 0.02 0.985 0.0 
 TC + CC compared with TT 0.90 (0.82–1.00) 1.98 0.048 50.0a 
 CC compared with TT + TC 1.06 (0.90–1.24) 0.71 0.478 0.0 
 C compared with T 0.96 (0.89–1.03) 1.14 0.256 15.9 
  Small        
 TC compared with TT 1.33 (0.69–2.56) 0.86 0.389 66.6 
 CC compared with TT 2.13 (1.36–3.35) 3.28 0.001 0.0 
 TC + CC compared with TT 1.98(1.01–3.88) 1.98 0.048 79.7 
 CC compared with TT + TC 2.03 (1.32–3.10) 3.24 0.001 0.0 
 C compared with T 1.38 (1.12–1.68) 3.08 0.002 0.0 
rs13293512        
All cancers        
 TC compared with TT 1.01 (0.90–1.14) 0.18 0.861 0.0 
 CC compared with TT 1.04 (0.90–1.22) 0.55 0.579 49.5 
 TC + CC compared with TT 1.02 (0.91–1.14) 0.34 0.731 0.0 
 CC compared with TT + TC 1.02 (0.81–1.28) 0.17 0.869 61.2 
 C compared with T 1.02 (0.95–1.10) 0.52 0.603 34.6 
a

Pheterogeneity is 0.112 which is higher than 0.10, thus fixed model has been used.

Furthermore, we executed stratification analysis based on different cancer types, source of controls, and sample size (Table 4). When the oral cavity cancer was divided into the head and neck cancer, the rs10877887 variant genotype was significantly associated with a decreased cancer risk in head and neck cancer (TC compared with TT: P=0.017; OR = 0.81; 95% CI = 0.68–0.96; TC + CC compared with TT: P=0.020; OR = 0.82; 95% CI = 0.70–0.97; Figure 2A). When stratified by sample size, in the small sample size subgroup, the variant genotype showed an increased significant association between rs10877887 and overall cancer risks in four genetic models (CC compared with TT: P=0.001; OR = 2.13; 95% CI = 1.36–3.35; TC + CC compared with TT: P=0.048; OR = 1.98; 95% CI = 1.01–3.88; CC compared with TT + TC: P=0.001; OR = 2.03; 95% CI = 1.32–3.10; C compared with T: P=0.002; OR = 1.38; 95% CI = 1.12–1.68; Table 4; Figure 2B). While in the large sample size subgroup, rs10877887 SNP showed a decreased risk in the dominate model (P=0.048; OR = 0.90; 95% CI = 0.82–1.00; Table 4).

Forest plot of ORs for the association of let-7i rs10877887 polymorphism with cancer risks and is illustrated in subgroup analysis

Figure 2
Forest plot of ORs for the association of let-7i rs10877887 polymorphism with cancer risks and is illustrated in subgroup analysis

(A) Stratified by cancer type in dominate model. (B) Stratified by sample size in recessive model.

Figure 2
Forest plot of ORs for the association of let-7i rs10877887 polymorphism with cancer risks and is illustrated in subgroup analysis

(A) Stratified by cancer type in dominate model. (B) Stratified by sample size in recessive model.

Quantitative synthesis for the association of SNPs and cancer prognosis

Then, we analyzed the association of let-7 family polymorphisms and cancer overall survival. The let-7i rs10877887 SNP was shown to be associated with a higher risk for cancer prognosis in the dominate model (CT + CC compared with TT: P=0.004; HR = 1.32; 95% CI = 1.09–1.60; Table 5). The other two SNPs (let-7a-1 rs10739971 and let-7a-2 rs629367) were not found to be associated with cancer survival.

Table 5
The meta-analysis results for the association of miRNA SNPs and cancer prognosis
miRNA-SNPsModelNumber of studiesNumber of patientsHR (95% CI)PHeterogeneity (P)
rs10877887 CT + CC compared with TT 420 1.32 (1.09–1.60) 0.004 0.367 
rs629367 CC compared with AA 911 2.01 (0.40–10.14) 0.130 0.010 
rs10739971 GA + AA compared with GG 1095 1.05 (0.77–1.42) 0.782 0.800 
miRNA-SNPsModelNumber of studiesNumber of patientsHR (95% CI)PHeterogeneity (P)
rs10877887 CT + CC compared with TT 420 1.32 (1.09–1.60) 0.004 0.367 
rs629367 CC compared with AA 911 2.01 (0.40–10.14) 0.130 0.010 
rs10739971 GA + AA compared with GG 1095 1.05 (0.77–1.42) 0.782 0.800 

Heterogeneity

Several comparisons appeared for slight heterogeneities between studies which were shown in Table 4. We further performed sensitivity analyses to explore individual study’s influence on the pooled results by removing one study at a time from pooled analysis (Supplementary Table S3). Any significant heterogeneity was not found in any genetic models which suggested a relative reliable result.

Publication bias

Begg’s rank correlation and Egger’s linear regression were conducted to evaluate publication bias. A slight publication bias for rs10877887 in dominate model was indicated according to the results of Begg’s test and Egger’s test (Supplementary Table S4).

TSA and FPRP analyses

Amongst the positive results, we found the dominate model for let-7i rs10877887 SNP in the larger sample size subgroup was adopted for the TSA to strengthen the robustness of our findings. According to TSA result, the required information size was 14,497 subjects to demonstrate the issue (Figure 3). Until now, the cumulative z-curve has not crossed the trial monitoring boundary before reaching the required information size, indicating that the cumulative evidence is insufficient and further trials are necessary.

The required information size to demonstrate the relevance of let-7i rs10877887 polymorphism with risk of cancer in the larger sample size subgroup (dominate model)

Figure 3
The required information size to demonstrate the relevance of let-7i rs10877887 polymorphism with risk of cancer in the larger sample size subgroup (dominate model)
Figure 3
The required information size to demonstrate the relevance of let-7i rs10877887 polymorphism with risk of cancer in the larger sample size subgroup (dominate model)

Then, we calculated the FPRP values for all observed significant findings. With the assumption of a prior probability of 0.01, the FPRP values for the small sample size subgroup in the co-dominate (CC compared with TT), recessive and allelic models were all <0.20, suggesting that these significant associations were noteworthy (Table 6).

Table 6
FPRP values for the associations between let-7 rs10877887 polymorphism and overall cancer risk
Prior probability
VariablesOR (95% CI)PaPowerb0.250.10.010.0010.0001
TC compared with TT         
  Head and neck cancer 0.81 (0.68–0.96) 0.017 0.666 0.071 0.187 0.716 0.962 0.996 
  PB 0.81 (0.68–0.96) 0.017 0.666 0.071 0.187 0.716 0.962 0.996 
         
CC compared with TT         
  Small sample size 2.13 (1.36–3.35) 0.001 0.922 0.003 0.010 0.097 0.520 0.916 
         
TC + CC compared with TT         
  Head and neck cancer 0.82 (0.70–0.97) 0.020 0.635 0.086 0.221 0.757 0.969 0.997 
  PB 0.82 (0.70–0.97) 0.020 0.635 0.086 0.221 0.757 0.969 0.997 
  Large sample size 0.90 (0.82–1.00) 0.048 0.667 0.178 0.393 0.877 0.986 0.999 
  Small sample size 1.98 (1.01–3.88) 0.048 0.941 0.133 0.315 0.835 0.981 0.998 
         
CC compared with TT + TC         
  Small sample size 2.03 (1.32–3.10) 0.001 0.899 0.003 0.010 0.099 0.526 0.918 
         
C compared with T         
  Small sample size 1.38 (1.12–1.68) 0.002 0.864 0.007 0.020 0.186 0.698 0.959 
Prior probability
VariablesOR (95% CI)PaPowerb0.250.10.010.0010.0001
TC compared with TT         
  Head and neck cancer 0.81 (0.68–0.96) 0.017 0.666 0.071 0.187 0.716 0.962 0.996 
  PB 0.81 (0.68–0.96) 0.017 0.666 0.071 0.187 0.716 0.962 0.996 
         
CC compared with TT         
  Small sample size 2.13 (1.36–3.35) 0.001 0.922 0.003 0.010 0.097 0.520 0.916 
         
TC + CC compared with TT         
  Head and neck cancer 0.82 (0.70–0.97) 0.020 0.635 0.086 0.221 0.757 0.969 0.997 
  PB 0.82 (0.70–0.97) 0.020 0.635 0.086 0.221 0.757 0.969 0.997 
  Large sample size 0.90 (0.82–1.00) 0.048 0.667 0.178 0.393 0.877 0.986 0.999 
  Small sample size 1.98 (1.01–3.88) 0.048 0.941 0.133 0.315 0.835 0.981 0.998 
         
CC compared with TT + TC         
  Small sample size 2.03 (1.32–3.10) 0.001 0.899 0.003 0.010 0.099 0.526 0.918 
         
C compared with T         
  Small sample size 1.38 (1.12–1.68) 0.002 0.864 0.007 0.020 0.186 0.698 0.959 

aChi-square test was adopted to calculate the genotype frequency distributions. bStatistical power was calculated using the number of observations in the subgroup and the OR and P values in this table.PB, source of controls is population-based

Discussion

Concerning the history of the let-7 family polymorphism studies, the first report began from the year of 2011. Fang Huang et al. first screened the functional SNPs from the gene region of let-7 gene family as well as 10 kb upstream, and they selected the let-7i promoter rs10877887 SNP and the let-7a-1/let-7f-1/let-7d gene cluster promoter rs13293512 SNP as the studied polymorphism sites [28]. Almost at the same time, a few other investigators adopted a similar screening strategy and selected four SNPs as the aiming-studied SNPs (let-7a-1 rs10739971; let-7a-2 rs629367 and rs1143770; let-7f-2 rs17276588) [29,30]. Although let-7 gene family had ten gene members, only six SNPs mentioned above could be selected to study in their gene region. In our meta-analysis, only the let-7i rs10877887 and let-7a-1/let-7f-1/let-7d rs13293512 SNPs in the risk study and let-7i rs10877887, let-7a-1 rs10739971, and let-7a-2 rs629367 SNPs in the prognosis study were recruited into the pooled analysis.

The let-7i rs10877887 SNP was the hottest SNP in let-7 family which all the scholars focussed on. It was located in the -286 bp region of let-7i gene which was the promoter region. Meanwhile, it was also located in the tail gene region of an lncRNA-linc01465. In the overall cancer risk analysis, we found that it nearly reached a statistical significance for an increased risk in recessive genetic model (Table 4). When stratified by cancer type, source of controls, and sample size, it was found that let-7i rs10877887 SNP variant genotype was associated with a decreased risk in dominate model in the subgroup of head and neck cancer, PB source of controls, and large sample size. While in the subgroup of small sample size, in all the genetic models, this rs10877887 SNP was associated with an increased cancer risk, except the co-dominate model (TC compared with TT). Then, we could analyze that the relative nonsignificance in the overall analysis was maybe due to the opposite results for the small and large sample size subgroups. We speculated this SNP seemed to tend to protect the cancer risk. Thus, more studies amplified sample size and multicenter studies are required in the future study to verify our findings.

The rs13293512 SNP located in -8496 bp upstream of the let-7a-1/let-7f-1/let-7d gene cluster which could be a promoter region for this gene cluster. For the let-7a-1/let-7f-1/let-7d rs13293512 SNP, only Longbiao Zhu et al. found that it was associated with head and neck cancer in the recessive genetic model [31], other three studies found no significance between this rs13293512 SNP and cancer risks. In the overall analysis, the integrated meta-analysis results also did not find this SNP had associated with cancer risk. More studies were needed to confirm this result in the future.

There is a phenomenon that even in the same kind of cancer patients with the same stage and pathological classification, the prognosis might not be the same owing to the genetic causes leading to some contributions [32]. It was accepted that the genetic polymorphisms could predict the cancer prognosis [33], and we found in this meta-analysis the let-7i rs10877887 SNP was associated with a higher risk for cancer prognosis in the dominate model. Due to the limited studies of the let-7 family polymorphisms and cancer prognosis, this result need more samples to verify. And the original studies used in the meta-analysis were all hepatocellular cancer, thus this let-7i rs10877887 SNP maybe had the potential to be a biomarker for the specific prediction of the hepatocellular cancer prognosis.

Advantages and limitations

To our knowledge, this is the first time to report the association between let-7 family polymorphisms and cancer risk/prognosis. Of course, this meta-analysis still had several limitations. First, only studies written in English and Chinese were searched in our analysis, while reports in other languages or some other ongoing studies were not available. Second, the pooled sample size was relatively limited thus we could only preliminarily appraise the association of let-7 polymorphism with currently reported types of cancers. More studies are still required to pool together to make the analysis more reliable.

Summary and future directions

In summary, this meta-analysis suggested that the let-7i rs10877887 variant genotype was significantly associated with a decreased cancer risk in head and neck cancer, and the let-7i rs10877887 SNP was shown to be associated with a higher risk for cancer prognosis in the dominate model. Additional well-designed studies in larger samples and functional studies regarding let-7 family SNPs are required to confirm our findings.

Author contribution

Q.X. designed the study; B.G.W. and Q.X. performed publication search, data extraction, methodology quality assessment, and statistical analysis; B.G.W., L.J., and Q.X. did the TSA and FPRP analysis; B.G.W. wrote the manuscript; Q.X. revised the manuscript. All authors disclose no conflicts of interest that might bias their work.

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

  •  
  • HB

    hospital based

  •  
  • HR

    hazard ratio

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • NA

    not available

  •  
  • NM

    not mentioned

  •  
  • OR

    odds ratio

  •  
  • PB

    population based

  •  
  • PCR-RFLP

    restriction fragment length polymorphism-polymerase chain reaction

  •  
  • qPCR

    quantitative polymerase chain reaction

  •  
  • SNP

    single nucleotide polymorphism

  •  
  • TSA

    trial sequential analysis

References

References
1
Tsuchiya
S.
,
Okuno
Y.
and
Tsujimoto
G.
(
2006
)
MicroRNA: biogenetic and functional mechanisms and involvements in cell differentiation and cancer
.
J. Pharmacol. Sci.
101
,
267
270
[PubMed]
2
Bartel
D.P.
(
2004
)
MicroRNAs: genomics, biogenesis, mechanism, and function
.
Cell
116
,
281
297
[PubMed]
3
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]
4
Fehlmann
T.
,
Ludwig
N.
,
Backes
C.
,
Meese
E.
and
Keller
A.
(
2016
)
Distribution of microRNA biomarker candidates in solid tissues and body fluids
.
RNA Biol.
13
,
1084
1088
[PubMed]
5
Lai
E.C.
(
2003
)
microRNAs: runts of the genome assert themselves
.
Curr. Biol.
13
,
R925
R936
[PubMed]
6
Duan
R.
,
Pak
C.
and
Jin
P.
(
2007
)
Single nucleotide polymorphism associated with mature miR-125a alters the processing of pri-miRNA
.
Hum. Mol. Genet.
16
,
1124
1131
[PubMed]
7
Shastry
B.S.
(
2009
)
SNPs: impact on gene function and phenotype
.
Methods Mol. Biol.
578
,
3
22
[PubMed]
8
Cherradi
N.
(
2015
)
microRNAs as potential biomarkers in adrenocortical cancer: progress and challenges
.
Front. Endocrinol. (Lausanne)
6
,
195
[PubMed]
9
Chen
Z.
,
Xu
L.
,
Ye
X.
,
Shen
S.
,
Li
Z.
,
Niu
X.
et al
(
2013
)
Polymorphisms of microRNA sequences or binding sites and lung cancer: a meta-analysis and systematic review
.
PLoS ONE
8
,
e61008
[PubMed]
10
Boyerinas
B.
,
Park
S.M.
,
Hau
A.
,
Murmann
A.E.
and
Peter
M.E.
(
2010
)
The role of let-7 in cell differentiation and cancer
.
Endocr. Relat. Cancer
17
,
F19
F36
[PubMed]
11
Krol
J.
,
Loedige
I.
and
Filipowicz
W.
(
2010
)
The widespread regulation of microRNA biogenesis, function and decay
.
Nat. Rev. Genet.
11
,
597
610
12
Kumar
M.S.
,
Lu
J.
,
Mercer
K.L.
,
Golub
T.R.
and
Jacks
T.
(
2007
)
Impaired microRNA processing enhances cellular transformation and tumorigenesis
.
Nat. Genet.
39
,
673
677
[PubMed]
13
Ricarte-Filho
J.C.
,
Fuziwara
C.S.
,
Yamashita
A.S.
,
Rezende
E.
,
da-Silva
M.J.
and
Kimura
E.T.
(
2009
)
Effects of let-7 microRNA on cell growth and differentiation of papillary thyroid cancer
.
Transl. Oncol.
2
,
236
241
[PubMed]
14
Liu
J.
and
Ni
S.
(
2018
)
Association between genetic polymorphisms in the promoters of let-7 and risk of cervical squamous cell carcinoma
.
Gene
642
,
256
260
[PubMed]
15
Wang
Y.
,
Wei
T.
,
Xiong
J.
,
Chen
P.
,
Wang
X.
,
Zhang
L.
et al
(
2015
)
Association between genetic polymorphisms in the promoter regions of let-7 and risk of papillary thyroid carcinoma: a case-control study
.
Medicine (Baltimore)
94
,
e1879
[PubMed]
16
Zhang
Y.
,
Zhu
L.
,
Wang
R.
,
Miao
L.
,
Jiang
H.
,
Yuan
H.
et al
(
2014
)
Genetic variants in let-7/Lin28 modulate the risk of oral cavity cancer in a Chinese Han population
.
Sci. Rep.
4
,
7434
[PubMed]
17
Gao
L.B.
,
Pan
X.M.
,
Li
L.J.
,
Liang
W.B.
,
Zhu
Y.
,
Zhang
L.S.
et al
(
2011
)
RAD51 135G/C polymorphism and breast cancer risk: a meta-analysis from 21 studies
.
Breast Cancer Res. Treat.
125
,
827
835
[PubMed]
18
Thorlund
K.
,
Engstrøm
J.
and
Wetterslev
J.
(
2011
)
User manual for trial sequential analysis (TSA)
,
Copenhagen Trial Unit, Centre for Clinical Intervention Research
,
Copenhagen, Denmark
,
Available at: www.ctu.dk/tsa
19
Wetterslev
J.
,
Thorlund
K.
,
Brok
J.
and
Gluud
C.
(
2008
)
Trial sequential analysis may establish when firm evidence is reached in cumulative meta-analysis
.
J. Clin. Epidemiol.
61
,
64
75
[PubMed]
20
Brok
J.
,
Thorlund
K.
,
Wetterslev
J.
and
Gluud
C.
(
2009
)
Apparently conclusive meta-analyses may be inconclusive–Trial sequential analysis adjustment of random error risk due to repetitive testing of accumulating data in apparently conclusive neonatal meta-analyses
.
Int. J. Epidemiol
38
,
287
298
[PubMed]
21
Wacholder
S.
,
Chanock
S.
,
Garcia-Closas
M.
,
El Ghormli
L.
and
Rothman
N.
(
2004
)
Assessing the probability that a positive report is false: an approach for molecular epidemiology studies
.
J. Natl Cancer Inst.
96
,
434
442
[PubMed]
22
He
J.
,
Wang
M.Y.
,
Qiu
L.X.
,
Zhu
M.L.
,
Shi
T.Y.
,
Zhou
X.Y.
et al
(
2013
)
Genetic variations of mTORC1 genes and risk of gastric cancer in an Eastern Chinese population
.
Mol. Carcinog.
52
,
E70
E79
[PubMed]
23
Fu
W.
,
Zhuo
Z.J.
,
Chen
Y.C.
,
Zhu
J.
,
Zhao
Z.
,
Jia
W.
et al
(
2017
)
NFKB1 -94insertion/deletion ATTG polymorphism and cancer risk: evidence from 50 case-control studies
.
Oncotarget
8
,
9806
9822
[PubMed]
24
DerSimonian
R.
and
Laird
N.
(
1986
)
Meta-analysis in clinical trials
.
Control. Clin. Trials
7
,
177
188
[PubMed]
25
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]
26
Begg
C.B.
and
Mazumdar
M.
(
1994
)
Operating characteristics of a rank correlation test for publication bias
.
Biometrics
50
,
1088
1101
[PubMed]
27
Egger
M.
,
Davey Smith
G.
,
Schneider
M.
and
Minder
C.
(
1997
)
Bias in meta-analysis detected by a simple, graphical test
.
BMJ
315
,
629
634
[PubMed]
28
Huang
F.
,
Hu
L.M.
,
Liu
J.B.
,
Zhang
Y.X.
and
Hu
Z.B.
(
2011
)
Relationship between genetic polymorphism of promoter region let-7 and genetic susceptibility to hepatocellular carcinoma
.
Zhonghua Yu Fang Yi Xue Za Zhi
45
,
1093
1098
[PubMed]
29
Xu
Q.
,
Dong
Q.
,
He
C.
,
Liu
W.
,
Sun
L.
,
Liu
J.
et al
(
2014
)
A new polymorphism biomarker rs629367 associated with increased risk and poor survival of gastric cancer in chinese by up-regulated miRNA-let-7a expression
.
PLoS ONE
9
,
e95249
[PubMed]
30
Shin
K.M.
,
Jung
D.K.
,
Hong
M.J.
,
Kang
H.J.
,
Lee
W.K.
,
Yoo
S.S.
et al
(
2016
)
The pri-let-7a-2 rs1143770C>T is associated with prognosis of surgically resected non-small cell lung cancer
.
Gene
577
,
148
152
[PubMed]
31
Zhu
L.
(
2014
)
Association and function studies of genetic variants in let-7 promoter region with the susceptibility of head and neck cancer
.
Nanjing Medical University: [D] Jiangsu
32
Srinivasan
S.
,
Clements
J.A.
and
Batra
J.
(
2016
)
Single nucleotide polymorphisms in clinics: fantasy or reality for cancer?
Crit. Rev. Clin. Lab. Sci.
53
,
29
39
[PubMed]
33
Jin
F.
,
Xiong
W.J.
,
Jing
J.C.
,
Feng
Z.
,
Qu
L.S.
and
Shen
X.Z.
(
2011
)
Evaluation of the association studies of single nucleotide polymorphisms and hepatocellular carcinoma: a systematic review
.
J. Cancer Res. Clin. Oncol.
137
,
1095
1104
[PubMed]
34
Sui
Z.Y.
,
Li
J.
,
Cheng
G.L.
and
Wang
S.F.
(
2016
)
A single nucleotide polymorphism in the promoter region (rs10877887) of let-7 is associated with hepatocellular carcinoma in a Chinese population
.
Genet. Mol. Res.
15
,
35
Shen
L.Q.
,
Xie
Y.Z.
,
Qian
X.F.
,
Zhuang
Z.X.
,
Jiao
Y.
and
Qi
X.F.
(
2015
)
A single nucleotide polymorphism in the promoter region of let-7 family is associated with lung cancer risk in Chinese
.
Genet. Mol. Res.
14
,
4505
4512
36
Xie
K.
,
Liu
J.
,
Zhu
L.
,
Liu
Y.
,
Pan
Y.
,
Wen
J.
et al
(
2013
)
A potentially functional polymorphism in the promoter region of let-7 family is associated with survival of hepatocellular carcinoma
.
Cancer Epidemiol.
37
,
998
1002
[PubMed]
37
Li
Y.
,
Xu
Q.
,
Liu
J.
,
He
C.
,
Yuan
Q.
,
Xing
C.
et al
(
2016
)
Pri-let-7a-1 rs10739971 polymorphism is associated with gastric cancer prognosis and might affect mature let-7a expression
.
Onco Targets Ther.
9
,
3951
3962
[PubMed]
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