The Association of Common Functional Polymorphisms in Mir-146a and Mir-196a2 and Hepatocellular Carcinoma Risk

MicroRNAs (miRNAs) are small non-coding RNA molecules that function as tumor suppressors or oncogenes. Single nucleotide polymorphisms (SNPs) located in the miRNAs influence the function of mature miRNAs and may contribute to cancer development. Studies investigating the association between miR-146a rs2910164 and miR-196a2 rs11614913 polymorphisms and hepatocellular carcinoma (HCC) risk reported inconsistent results. We performed a meta-analysis of all available studies to summarize this situation. Eligible studies were identified by search of electronic databases including PubMed, Embase, and Cochrane library for the period up to August 2014. The association of miR-146a rs2910164 and miR-196a2 rs11614913 polymorphisms and HCC risk was assessed by odds ratios (ORs) together with their 95% confidence intervals (CIs). Finally, a total of 12 studies with 4171 cases and 4901 controls were included for miR-146a rs2910164 polymorphism and 10 studies with 4687 cases and 4990 controls were available for miR-196a2 rs11614913 polymorphism. With respect to miR-146a rs2910164 polymorphism, statistical significant increased HCC risk was found when all studies were pooled into the meta-analysis (GGþCG vs CC: OR1⁄4 1.097, 95% CI 1.005–1.197, P1⁄4 0.037). In subgroup analyses by ethnicity, source of control, and HWE in controls, significant increase of HCC risk was found in Asians, population-based studies, and studies consistent with HWE, but not in Caucasians, hospital-based studies, and studies inconsistent with HWE. With respect to miR-196a2 rs11614913 polymorphism, no significant association with HCC risk was found in the overall and subgroup analyses. The results suggest that the miR-146a rs2910164 polymorphism contributes to increased HCC susceptibility, especially in Asian populations. Further large and well-designed studies are required to validate jun Lao, MD, Zhip , PhD, and Xue Qin, PhD Abbreviations: 30UTR = 30untranslated region, CI = confidence interval, HBV = hepatitis B virus, HCC = hepatocellular carcinoma, HCV = hepatitis C virus, HWE = Hardy–Weinberg equilibrium, MiRNAs = microRNAs, OR = odds ratio, SNP = single nucleotide polymorphism. INTRODUCTION H epatocellular carcinoma (HCC), as the most frequent primary cancer of the liver, is the fifth most common cancer and the third leading cause of cancer-related deaths worldwide. The estimated annual number of cases exceeds 500,000, with a mean annual incidence of around 3–4%. Patients with HCC have a poor prognosis, with a 5-year survival rate of 5% in developing countries because of the lack of effective therapy in most patients. Etiologically, carcinogenesis of HCC is a complex, multistep, and multifactor process, in which many factors are implicated. It has been well established that major risk factors for the development of HCC are chronic infection with hepatitis B virus (HBV) or hepatitis C virus (HCV), liver cirrhosis, habitual alcohol abuse, and exposure to aflatoxin B1. However, most subjects with these environmental risk factors never develop HCC while many HCC cases develop among individuals without the above risk factors, suggesting that other factors such as genetic factor also play an important role in hepatocellular carcinogenesis. MicroRNAs (miRNAs) are a group of small non-coding RNA molecules that regulate gene expression through complementary base pairing with thousands of messenger RNAs (mRNAs) and are demonstrated to play an important role in transcriptional and translational regulation. MiRNAs elicit their effects via imperfectly binding to the 30untranslated region (30UTR) of the target mRNAs, leading to either degradation of mRNAs or repression of protein translation. Gene regulation by miRNA has been involved in a diverse range of biological and pathologic processes, and miRNAs have been demonstrated to regulate the expression levels of major cancer-related genes. Emerging evidence has indicated the important influence of miRNAs on tumor invasion and metastasis through action as metastasis promoting genes or metastasis suppressor genes. More importantly, some recent studies have revealed that miRNAs participate in carcinogenesis as tumor suppressors or oncogenes by regulating the expression of their target genes. Single nucleotide polymorphisms (SNPs) or mutations in miRNA sequence may alter miRNA expression and/or maturation, and may also change the regulatory effect of miRNA to MiR-146a rs2910164 and miR-196a2 two most common studied SNPs in 2910164 polymorphism is located at www.md-journal.com | 1 position þ60 relative to the first nucleotide of pre-miR-146a with a C to G change in the passenger strand, which results in a change from C:U pair to G:U mismatch in the stem structure of miR-146a precursor and a reduced production of mature miR146a. MiR-196a2 is composed of 2 different mature miRNAs (miR-196a-3P and miR-196a-5P) which were processed from the same stem-loop. The expression of 137 cancer-related transcripts could be significantly altered after the introduction of pre-miR-196a. Rs11614913 is located in the mature sequence of miR-196a-3P and could lead to less efficient processing of the miRNA precursor to its mature form, as well as reduced capacity to regulate target genes. In light of the important biological effects of the two miRNA SNPs, emerging epidemiological studies have been conducted to investigate the association of miR-146a rs2910164 and miR-196a2 rs11614913 polymorphisms with HCC risk, but the results remain inconsistent and underpowered. Some studies suggested that the two SNPs in miRNAs were significantly correlated with HCC susceptibility. However, other studies did not support such an association. A meta-analysis by Xu et al on this issue suggested that the miR-146a rs2910164 polymorphism was associated with a decreased HCC risk especially among Asians, while the miR-196a2 rs11614913 polymorphism was correlated with an increased HCC risk among Caucasians. However, the evidence was limited because only 7 studies were included for miR-146a rs2910164 polymorphism and 5 studies were for miR-146a rs2910164 polymorphism. In addition, the source of heterogeneity was not extensively explored in this study. As many new studies with relatively large sample size emerging, to provide the most comprehensive assessment of the association between the miR-146a rs2910164 and miR-196a2 rs11614913 polymorphisms and HCC risk, we performed an updated meta-analysis of all available studies. MATERIALS AND METHODS Literature Search We conducted a comprehensive literature search in PubMed, EMBASE, and Cochrane library up to August 01, 2014 using the following search strategy: (‘‘hepatocellular carcinoma,’’ ‘‘HCC,’’ or ‘‘liver cancer’’) and (‘‘miR-146a’’ or ‘‘miR-196a2’’). There was no restriction on sample size, time period, language, population, or type of report. All eligible studies were retrieved and their references were checked for other relevant studies. In addition, we also used the ‘‘Related Articles’’ function in PubMed to search for other potential relevant articles. When several studies reported on the same or overlapping data, we chose the most recent or largest population. The meta-analysis was performed according to the proposal of Meta-analysis of Observational Studies in Epidemiology group (MOOSE). Selection Criteria Studies included in the meta-analysis were required to meet the following criteria: (1) Case–control studies which evaluated the association between miR-146a rs2910164 and/or miR-196a2 rs11614913 polymorphisms and HCC risk; (2) used an unrelated case–control design; (3) presented an odds ratio Peng et al (OR) together with 95% confidence interval (CI) or other information for estimating OR (95% CI); and (4) the control group did not contain malignant tumor patients. Conference 2 | www.md-journal.com abstracts, meta-analyses, case reports, review articles, editorials, and letters were excluded. Data Extraction Two authors (Shan Li and Qiliu Peng) independently extracted information from all eligible studies according to the inclusion criteria listed above. Data extracted from the qualified studies including the first author, publication year, country of origin, ethnicity, genotyping method, source of control, matching variables, HCC diagnosis, and total number of cases and controls and genotype frequencies of cases and controls. When a study did not state the ethnic descendent or if it was impossible to separate participants according to such phenotype, the group reported was categorized as ‘‘Mixed/ other.’’ The two authors checked the data extraction results and reached consensus on all of the items. Statistical Analysis The strength of the association between miR-146a rs2910164 and miR-196a2 rs11614913 polymorphisms and HCC risk was assessed by ORs together with 95% CIs. The significance of the pooled ORs was determined by Z test and the P values <0.05 were considered significant. We evaluated the miR-146a rs2910164 and miR-196a2 rs11614913 polymorphisms and HCC risk using codominant models, recessive model, and dominant model. The Chi-square-based Q test was used to assess the statistical heterogeneity among studies. If the result of the Q test was PQ< 0.10, suggesting the existence of heterogeneity, the pooled ORs were calculated using the random-effects model (the DerSimonian and Laird method). Otherwise, when the result of the Q test was PQ 0.1, indicating the absence of heterogeneity, the fixed-effects model (the Mantel–Haenszel method) was applied. Logistic metaregression and subgroup analyses were used to explore the sources of heterogeneity among studies. The following parameters were included as covariates in the metaregression analysis: ethnicity (Asians vs Caucasians), genotyping methods (PCR-RFLP vs not PCR-RFLP), source of controls (HB vs PB), and Hardy– Weinberg equilibrium (HWE) status (Yes vs No). Subgroup analyses were performed according to ethnicity, source of control, and HWE status. Galbraith plots analysis was performed to further explore the source of heterogeneity. Sensitivity analysis was performed by sequentially excluded the individual studies to assess the robustness of the results. Begg’s funnel plot and Egger’s regression asymmetry test were performed to evaluate the publication bias. If the publication bias presented, the Duval and Tweedie non-parametric ‘‘trim and fill’’ method was applied to adjust for it. The distribution of the genotypes in the control group was tested for HWE using a goodness-of-fit Chi-square test. All analyses were performed using Stata software, version 12.0 (Stata Corp., College Station, TX). All P values were two-sided. To ensure the reliability and the accuracy of the results, 2 authors entered the data into the statistical software programs independently with the same results. RESULTS Study Characteristics Medicine Volume 93, Number 29, December 2014 Based on the search strategy, 43 records were found, but only 18 full-text studies were preliminarily identified for further detailed evaluation after screening the titles and abstracts. Copyright # 2014 Wolters Kluwer Health, Inc. All rights reserved. T A B L E 1 . C h a ra ct e ri st ic s o f th e In cl u d e d S tu d ie s F ir st A u th or Y ea r C ou n tr y E th n ic it y G en ot yp in g M et h od s S ou rc e of C on tr ol M at ch ed V ar ia b le s S am p le S iz e (C as e/ C on tr ol ) H C C D ia gn os is H W E