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  • Review Article
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Genetic variation in microRNA networks: the implications for cancer research

Nature Reviews Cancer volume 10, pages 389–402 (2010)Cite this article

Subjects

A Corrigendum to this article was published on 10 June 2010

This article has been updated

Key Points

  • Single nucleotide polymorphisms (SNPs) in microRNA (miRNA) genes (miR-SNPs) can be predicted to affect function by modulating the transcription of the primary transcript, pri-miRNA and pre-miRNA processing and maturation, or miRNA–mRNA interactions. Functional support for each of these mechanisms has been found for several individual miR-SNPs.

  • SNPs in mature miRNAs and miRNA binding sites function analogously to modulate the miRNA–mRNA interaction and create or destroy miRNA binding sites.

  • Several elements have to converge for an miRNA binding site SNP to be considered functional: the SNP must have a proven association with cancer, both the miRNA and its predicted target must be expressed in the tissue, and the allelic changes must result in differential binding of the miRNA and affect expression of the target gene.

  • Computational prediction of miRNA binding sites and up-to-date coverage of SNPs is an essential part of these studies. Programs such as Patrocles and PolymiRTS intercalate and cross-reference these data with dbSNP information, and as such are invaluable in aiding the search for polymorphic miRNA binding sites.

  • Case–control studies have provided evidence for an association of miR-SNPs and SNPs in miRNA-binding sites and cancer risk. These studies differ in the degree of functional support for the predicted interaction and mechanistic insight, as well as validation status.

  • Although still lacking biological validation, SNPs in the miRNA processing machinery are likely to affect the miRNAome as a whole, perhaps leading to overall suppression of miRNA output. Despite several reported associations, none of the studies of SNPs in miRNA processing machinery has been independently validated, nor has the biological mechanisms of how they affect miRNA maturation and cancer been delineated.

  • IsomiRs are miRNA structural variants that may arise from variable cleavage sites for DROSHA and DICER1 in the hairpin. A few isomiRs have been implicated in cancer, but associations with cancer risk have not been established.

  • Both the regulatory and coding regions of genes can harbour miRNA binding sites, but research in this area remains scant. Sensitive alleles identified in epidemiological studies, but with obscure functional roles, should perhaps be tested under miRNA prediction algorithms that are not limited to the 3′ untranslated region of genes, particularly if evidence indicates that altered expression of that gene can be associated with the phenotypes.

Abstract

Many studies have highlighted the role that microRNAs have in physiological processes and how their deregulation can lead to cancer. More recently, it has been proposed that the presence of single nucleotide polymorphisms in microRNA genes, their processing machinery and target binding sites affects cancer risk, treatment efficacy and patient prognosis. In reviewing this new field of cancer biology, we describe the methodological approaches of these studies and make recommendations for which strategies will be most informative in the future.

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Figure 1: Illustrative overview of the miRNA network.
Figure 2: Diagrammatic representation of SNPs in pri-miRNA and pre-miRNA sequences.
Figure 3: Diagrammatic representation of SNPs in miRNA seed and regulatory regions.
Figure 4: Diagrammatic representation of SNPs in miRNA processing machinery.

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Change history

  • 09 June 2010

    There were several mistakes in TABLES 1-3 of this article. In TABLE 1 on page 385, for rs2910164 in hepatocelluar carcinoma the odds ratio data refer to males only. For rs11614913 in lung cancer the odds ratio is for both sexes and not for males only. For rs11614913 in oesophageal cancer the reference genotype should be CC/CT and the analysed genotype should be TT. For rs11614913 in breast cancer the reference genotype should be CC and the analysed genotypes should be CT and then TT. For rs895819 in breast cancer the reference genotype should be CT. In TABLE 2 on page 396, for the SEDT8 alleles, the miRNA binds the derived alleles and not the ancestral alleles. For the BCTRP allele, the miRNA binds deletion alleles that are derived and not insertion alleles that are ancestral. For the BMPR1B allele, the miRNA binds the C allele and not the derived allele. For the CD86 allele, the miRNA binds the ancestral allele and not the derived allele. In TABLE 3 on page 398, the odds ratio given for GEMIN4 rs7813 is for all cases of renal cell carcinoma, not just clear cell carcinoma as indicated. For GEMIN3 rs197414 the cancer site should be bladder and not renal cell carcinoma and the reference is 35 and not 111. This has been corrected online.

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Acknowledgements

This work was supported by the Intramural Research Program of the National Institute of Health, NCI-CCR.

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Authors and Affiliations

  1. Cancer Prevention Fellowship Program, Center for Cancer Training, National Cancer Institute, National Institutes of Health, 37 Convent Drive, Building 37, Room 3068A, Bethesda, 20892-4258, MD, USA

    Bríd M. Ryan

  2. Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 37 Convent Drive, Building 37, Room 3068A, Bethesda, 20892-4258, MD, USA

    Bríd M. Ryan, Ana I. Robles & Curtis C. Harris

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  1. Bríd M. Ryan
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  2. Ana I. Robles
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  3. Curtis C. Harris
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Correspondence to Curtis C. Harris.

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Supplementary information

Supplementary information S1 (table)

microRNA SNPs (PDF 162 kb)

Supplementary Information S2 (box)

Web-server tools to identify SNPs that may affect miRNA binding. (PDF 307 kb)

Related links

Related links

DATABASES

MiRbase 

Let-7b

let-7e

miR-10a

miR-16-1

miR-21

mir-26a-1

mir-27a

miR-122

mir-124-1

miR-182*

mir-149

miR-189

mir-196a-2

miR-378

miR-423

miR-453

miR-575

miR-582

mir-BART22

mmu-miR-10a

mmu-miR-27a

mmu-miR-27c

mmu-miR-155

mmu-miR-222

FURTHER INFORMATION

Curtis C. Harris' homepage

dbSNP

International HapMap Project

Patrocles database

PolymiRTS database

RNAfold software

Targetscan database

Glossary

Fragile sites

Parts of a chromosome that are sensitive to break formation during metaphase when DNA replication is perturbed. The genes that lie within these regions are frequently deleted or rearranged in cancer.

Case–control study

An epidemiological study that compares two groups of individuals: those who have the condition under study (the cases) and those without the condition (the controls).

Passenger (3p) strand

Precursor miRNA sequences form a stem-loop structure. The single-stranded mature sequence lies at the 5′ end (5p strand). Generally, the strand complementary to the mature miRNA at the 3′ end is degraded (3p strand), although in some cases it is not.

Minor allele frequency

The minor allele frequency of an SNP is the frequency of the least common allele in a population.

Genome-wide association studies (GWAS)

Large case–control studies in which genetic variation, in the form of SNPs, are examined across a genome to identify genetic associations with disease.

Tag SNPs

A genetic change that is in high linkage disequilibrium with other SNPs. The term 'tag' is used as these SNPs can be used to mark the genetic variations of all the SNPs they are associated with, without sequencing all the SNPs. They are frequently used in genome-wide association studies.

Linkage disequilibrium

The non-random inheritance of alleles at two or more loci. The resulting haplotype is generally inherited from a single chromosome. Natural selection of a favourable phenotype can contribute to linkage disequilibrium between alleles.

Alternative splicing

Splicing is a post-transcriptional mechanism in which introns are removed and exons are joined together allowing the production of a specific protein product. Alternative splicing occurs when different combinations of exons (and introns) are cut together, allowing genes to produce more than one mRNA isoform.

Expression Quantitative Trait Locus (eQTL) mapping

Quantitative trait loci (QTL) are regions of DNA that are closely linked to the genes that underlie the trait in question. Expression QTL (eQTL) are genetic loci that regulate gene expression traits. Because of the intricate association of miRNAs and gene expression, mir-SNPs are unique candidates for eQTL studies and eQTLs provide support for mir-SNP functionality.

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Ryan, B., Robles, A. & Harris, C. Genetic variation in microRNA networks: the implications for cancer research. Nat Rev Cancer 10, 389–402 (2010). https://doi.org/10.1038/nrc2867

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