Aberrant allele frequencies of the SNPs located in microRNA target sites are potentially associated with human cancers

MicroRNAs (miRNAs) are a class of noncoding small RNAs that regulate gene expression by base pairing with target mRNAs at the 3'-terminal untranslated regions (3'-UTRs), leading to mRNA cleavage or translational repression. Single-nucleotide polymorphisms (SNPs) located at miRNA-binding sites (miRNA-binding SNPs) are likely to affect the expression of the miRNA target and may contribute to the susceptibility of humans to common diseases. We herein performed a genome-wide analysis of SNPs located in the miRNA-binding sites of the 3'-UTR of various human genes. We found that miRNA-binding SNPs are negatively selected in respect to SNP distribution between the miRNA-binding 'seed' sequence and the entire 3'-UTR sequence. Furthermore, we comprehensively defined the expression of each miRNA-binding SNP in cancers versus normal tissues through mining EST databases. Interestingly, we found that some miRNA-binding SNPs exhibit significant different allele frequencies between the human cancer EST libraries and the dbSNP database. More importantly, using human cancer specimens against the dbSNP database for case-control association studies, we found that twelve miRNA-binding SNPs indeed display an aberrant allele frequency in human cancers. Hence, SNPs located in miRNA-binding sites affect miRNA target expression and function, and are potentially associated with cancers.     

SNP Regulation of microRNA Expression and Subsequent Colon Cancer Risk

Introduction

MicroRNAs (miRNAs) regulate messenger RNAs (mRNAs) and as such have been implicated in a variety of diseases, including cancer. MiRNAs regulate mRNAs through binding of the miRNA 5’ seed sequence (~7–8 nucleotides) to the mRNA 3’ UTRs; polymorphisms in these regions have the potential to alter miRNA-mRNA target associations. SNPs in miRNA genes as well as miRNA-target genes have been proposed to influence cancer risk through altered miRNA expression levels.

Methods

MiRNA-SNPs and miRNA-target gene-SNPs were identified through the literature. We used SNPs from Genome-Wide Association Study (GWAS) data that were matched to individuals with miRNA expression data generated from an Agilent platform for colon tumor and non-tumor paired tissues. These samples were used to evaluate 327 miRNA-SNP pairs for associations between SNPs and miRNA expression levels as well as for SNP associations with colon cancer.

Results

Twenty-two miRNAs expressed in non-tumor tissue were significantly different by genotype and 21 SNPs were associated with altered tumor/non-tumor differential miRNA expression across genotypes. Two miRNAs were associated with SNP genotype for both non-tumor and tumor/non-tumor differential expression. Of the 41 miRNAs significantly associated with SNPs all but seven were significantly differentially expressed in colon tumor tissue. Two of the 41 SNPs significantly associated with miRNA expression levels were associated with colon cancer risk: rs8176318 (BRCA1), OR_AA 1.31 95% CI 1.01, 1.78, and rs8905 (PRKAR1A), OR_GG 2.31 95% CI 1.11, 4.77.

Conclusion

Of the 327 SNPs identified in the literature as being important because of their potential regulation of miRNA expression levels, 12.5% had statistically significantly associations with miRNA expression. However, only two of these SNPs were significantly associated with colon cancer.     

Genetic polymorphisms and microRNAs: new direction in molecular epidemiology of solid cancer

MicroRNAs (miRNAs) are small non-coding RNAs, which regulate gene expression. Single nucleotide polymorphisms (SNPs) may occur in miRNA biogenesis pathway genes, primary miRNA, pre-miRNA or a mature miRNA sequence. Such polymorphisms may be functional with respect to biogenesis and actions of mature miRNA. Specific SNPs were identified in predicted miRNA target sites within 3' untranslated regions of mRNAs. These SNPs have a potential to affect the efficiency of miRNA binding to the target sites or can create or disrupt binding sites. Resulting gene dysregulation may involve changes in phenotype and may eventually prove critical for the susceptibility to cancer and its onset as well as for estimates of prognosis and therapy response. In this review, we provide a comprehensive list of potentially functional miRNA-related SNPs and summarize their importance as candidate cancer biomarkers.     

MicroRNA regulation and the variability of human cortical gene expression

Understanding the driving forces of gene expression variation within human populations will provide important insights into the molecular basis of human phenotypic variation. In the genome, the gene expression variability differs among genes, and at present, most research has focused on identifying the genetic variants responsible for the within population gene expression variation. However, little is known about whether microRNAs (miRNAs), which are small noncoding RNAs modulating expression of their target genes, could have impact on the variability of gene expression. Here we demonstrate that miRNAs likely lead to the difference of expression variability among genes. With the use of the genome-wide expression data in 193 human brain samples, we show that the increased variability of gene expression is concomitant with the increased number of the miRNA seeds interacting with the target genes, suggesting a direct influence of miRNA on gene expression variability. Compared with the non-miRNA-target genes, genes targeted by more than two miRNA seeds have increased expression variability, independent of the miRNA types. In addition, single-nucleotide polymorphisms (SNPs) located in the miRNA binding sites could further increase the gene expression variability of the target genes. We propose that miRNAs are one of the driving forces causing expression variability in the human genome.     

Powerful SNP-Set Analysis for Case-Control Genome-wide Association Studies

GWAS have emerged as popular tools for identifying genetic variants that are associated with disease risk. Standard analysis of a case-control GWAS involves assessing the association between each individual genotyped SNP and disease risk. However, this approach suffers from limited reproducibility and difficulties in detecting multi-SNP and epistatic effects. As an alternative analytical strategy, we propose grouping SNPs together into SNP sets on the basis of proximity to genomic features such as genes or haplotype blocks, then testing the joint effect of each SNP set. Testing of each SNP set proceeds via the logistic kernel-machine-based test, which is based on a statistical framework that allows for flexible modeling of epistatic and nonlinear SNP effects. This flexibility and the ability to naturally adjust for covariate effects are important features of our test that make it appealing in comparison to individual SNP tests and existing multimarker tests. Using simulated data based on the International HapMap Project, we show that SNP-set testing can have improved power over standard individual-SNP analysis under a wide range of settings. In particular, we find that our approach has higher power than individual-SNP analysis when the median correlation between the disease-susceptibility variant and the genotyped SNPs is moderate to high. When the correlation is low, both individual-SNP analysis and the SNP-set analysis tend to have low power. We apply SNP-set analysis to analyze the Cancer Genetic Markers of Susceptibility (CGEMS) breast cancer GWAS discovery-phase data.     

SNPs occur in regions with less genomic sequence conservation

Rates of SNPs (single nucleotide polymorphisms) and cross-species genomic sequence conservation reflect intra- and inter-species variation, respectively. Here, I report SNP rates and genomic sequence conservation adjacent to mRNA processing regions and show that, as expected, more SNPs occur in less conserved regions and that functional regions have fewer SNPs. Results are confirmed using both mouse and human data. Regions include protein start codons, 3' splice sites, 5' splice sites, protein stop codons, predicted miRNA binding sites, and polyadenylation sites. Throughout, SNP rates are lower and conservation is higher at regulatory sites. Within coding regions, SNP rates are highest and conservation is lowest at codon position three and the fewest SNPs are found at codon position two, reflecting codon degeneracy for amino acid encoding. Exon splice sites show high conservation and very low SNP rates, reflecting both splicing signals and protein coding. Relaxed constraint on the codon third position is dramatically seen when separating exonic SNP rates based on intron phase. At polyadenylation sites, a peak of conservation and low SNP rate occurs from 30 to 17 nt preceding the site. This region is highly enriched for the sequence AAUAAA, reflecting the location of the conserved polyA signal. miRNA 3' UTR target sites are predicted incorporating interspecies genomic sequence conservation; SNP rates are low in these sites, again showing fewer SNPs in conserved regions. Together, these results confirm that SNPs, reflecting recent genetic variation, occur more frequently in regions with less evolutionarily conservation.     

In silico evidence of high frequency of miRNA-related SNPs in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is the dominant histological type of esophageal cancer significantly reported in developing nations. There is an increasing evidence suggesting that single nucleotide polymorphisms (SNPs) in the untranslated regions of genes (3′-UTRs) targeted by microRNAs (miRNAs) can change the target gene's expression and thereby affect the individual's cancer risk. Thus, in support of the role of SNPs occurring in miRNA target sites (miR-TS-SNPs) in the cancer, we analyzed the next generation sequencing data of 10 ESCC patients. In each patient, about 3,000 SNPs in 3′-UTRs were obtained in their whole-exome sequencing profiles. We applied two separate methods, manual and computational in silico approaches, to predict the miR-TS-SNPs with more effects on the miRNA–target interactions. dbSNP, 1000G, ExAC, Iranome, miRandb, miRCancer, TargetScan, Human, miRNASNP2 and miRBase databases were used for positive selection of miR-TS-SNPs and DIANA-miRPath v3.0 for pathway analysis. We identified six rare germline miR-TS-SNPs and two other ones with unknown miR-TS-SNPs. We interestingly observed all of these variants in only one patient, which can be evidence of the relationship between miR-TS-SNPs and cancer incidence. The study of cancer genetics including miR-TS-SNPs reveals miRNAs and their related pathways, which will be greatly useful in cancer research from noninvasive biomarkers to new treatments.     

Evaluation of polymorphisms in microRNA‐binding sites and pancreatic cancer risk in Chinese population

As promising biomarkers and therapy targets, microRNAs (miRNAs) are involved in various physiological and tumorigenic processes. Genetic variants in miRNA‐binding sites can lead to dysfunction of miRNAs and contribute to disease. However, systematic investigation of the miRNA‐related single nucleotide polymorphisms (SNPs) for pancreatic cancer (PC) risk remains elusive. We performed integrative bioinformatics analyses to select 31 SNPs located in miRNA‐target binding sites using the miRNASNP v2.0, a solid database providing miRNA‐related SNPs for genetic research, and investigated their associations with risk of PC in two large case‐control studies totally including 1847 cases and 5713 controls. We observed that the SNP rs3802266 is significantly associated with increased risk of PC (odds ratio (OR) = 1.21, 95% confidence intervals (CI) = 1.11‐1.31, P = 1.29E‐05). Following luciferase reporter gene assays show that rs3802266‐G creates a stronger binding site for miR‐181a‐2‐3p in 3′ untranslated region (3′UTR) of the gene ZHX2. Expression quantitative trait loci (eQTL) analysis suggests that ZHX2 expression is lower in individuals carrying rs3802266‐G with increased PC risk. In conclusion, our findings highlight the involvement of miRNA‐binding SNPs in PC susceptibility and provide new clues for PC carcinogenesis.