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Carcinogenesis Advance Access originally published online on January 12, 2008
Carcinogenesis 2008 29(3):579-584; doi:10.1093/carcin/bgm304
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Polymorphisms within micro-RNA-binding sites and risk of sporadic colorectal cancer

Debora Landi1,2, Federica Gemignani1, Alessio Naccarati3, Barbara Pardini3, Pavel Vodicka3, Ludmila Vodickova3,4, Jan Novotny5,6, Asta Försti2,7, Kari Hemminki2,7, Federico Canzian2 and Stefano Landi1,*

1 Dipartimento di Biologia, University of Pisa, Via Derna, 1, 56126 Pisa, Italy
2 Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
3 Department of Molecular Biology of Cancer, Institute of Experimental Medicine, Academy of Science of Czech Republic, Videnska 1083, 14220 Prague 4, Czech Republic
4 Centre of Occupational Health, National Institute of Public Health, Srobarova, 100 42 Prague 10, Czech Republic
5 Department of Oncology, First Faculty of Medicine, Charles University, 12000 Prague, Czech Republic
6 Oncology Centre, Hospital 26205 Pribram, Pribram, Czech Republic
7 Center for Family and Community Medicine, Karolinska Institute, SE-17177 Huddinge, Sweden

* To whom correspondence should be addressed. Tel: +39 050 2211505; Fax: +39 0502211527;Email: slandi{at}biologia.unipi.it


   Abstract
Top
 Abstract
Introduction
 Materials and methods
 Results
 Discussion
 Funding
 References
 
Recent evidence indicate that small non-coding RNA molecules, called micro-RNAs (miRNAs), can bind to the 3' untranslated regions (UTRs) of messenger RNAs and interfere with their translation, thereby regulating cell growth, differentiation, apoptosis and tumorigenesis. Genetic polymorphisms can reside on miRNA-binding sites. Thus, it is conceivable that the miRNA regulation may be affected by polymorphisms on the 3' UTRs. Since gene deregulation is one of the key mechanisms by which cells can progress to cancer, we hypothesize that common polymorphisms within miRNA-target binding sites could play a role in the individual risk of cancer. In the present study, we selected the 3' UTRs of 104 genes candidate for colorectal cancer (CRC) and we identified putative miRNA-binding sites by specialized algorithms (PicTar, DianaMicroT, miRBase, miRanda, TargetScan and microInspector). Fifty-seven single-nucleotide polymorphisms (SNPs) were identified in miRNA-binding sites. We evaluated the SNPs for their ability to affect the binding of the miRNA with its target, by assessing the variation of Gibbs free energy between the two alleles of each SNP. We found eight common polymorphisms that were further investigated by a case–control association studies. The study was carried out on a series of cases and controls from Czech Republic, a population with the highest worldwide incidence of CRC. We found statistically significant associations between risk of CRC and variant alleles of CD86 [odds ratio (OR) = 2.74; 95% confidence interval (CI) = 1.24–6.04, for the variant homozygotes] and INSR genes (OR = 1.94; 95% CI = 1.03–3.66, for the variant homozygotes). These results are the first reporting positive association between miRNA-binding SNPs sequences and cancer risk.

Abbreviations: CRC, colorectal cancer; CI, confidence interval; mRNA, messenger RNA; miRNA, micro-RNA; MAF, minor allele frequency; nt, nucleotide; OR, odds ratio; SNP, single-nucleotide polymorphism; UTR, untranslated region


   Introduction
Top
 Abstract
 Introduction
Materials and methods
 Results
 Discussion
 Funding
 References
 
In developed countries, colorectal cancer (CRC) is one of the most common types of tumor (1). Genetic background is thought to play a role in modulating individual risk (2). Among the genes investigated and associated with the risk of CRC are the genes involved in xenobiotic metabolism (3), DNA repair (4), insulin resistance, obesity and glucose levels (5) and inflammation (6).

Gene deregulation is one of the key mechanisms by which cells can progress to cancer (7). Recently, new mechanisms governing the expression of genes related to cancer development have been identified. Among them, the post-transcriptional regulation through micro-RNAs (miRNAs) is one of the most interesting and powerful in the maintenance and management of the cellular differentiation. miRNAs are endogenous non-coding RNAs of ~22 nucleotides (nts), regulating genes in animals and plants by pairing to the 3' untranslated regions (UTRs) of messenger RNAs (mRNAs) of target genes and specifying mRNA cleavage or repression of protein synthesis (8). There is increasing evidence that miRNAs have an important regulatory role in a broad range of biological processes, including developmental timing, cellular differentiation, proliferation, apoptosis, gene regulation, cancer development, insulin secretion and cholesterol biosynthesis (8). miRNA genes represent only a small part of the genome, but they regulate ~20–30% of all human genes and there is an average of 200 predicted targets per miRNA (9).

The maturation of a miRNA is a multistep process that begins in the nucleus and ends in the cytoplasm. Briefly, miRNAs are transcribed by RNA polymerase II as part of a long RNA precursor, defined as ‘primary miRNA’. Within the primary miRNA, the ~22 nt mature miRNA forms part of one arm of a ~80 nt imperfect stem–loop sequence (10). miRNA processing involves the nuclear cleavage of this RNA stem–loop structure. This liberates a 60 nt RNA hairpin intermediate, bearing a characteristic 2 nt 3' overhang, defined as ‘pre-miRNA’ (11). Pre-miRNA is then exported into the cytoplasm (12) and further processed by an RNase III enzyme called ‘Dicer’ (8). Dicer removes the terminal loop, leaving a second 2 nt 3' overhang. The result is a miRNA duplex intermediate, comprising the mature miRNA and its opposing strand. This short duplex intermediate is separated and the single-stranded mature miRNA is incorporated into the RNA-induced silencing complex (13). The composition of RNA-induced silencing complex remains incompletely defined, but a key component is an Argonaute protein (8).

The complex miRNA–RNA-induced silencing complex can inhibit the translation when it binds the 3' UTR-mRNA targets with an imperfect complementarity. In this case, there is a reduced level of protein without reductions in the mRNA levels. The maximal complementarity is usually restricted to the nucleotides 2–8 in the 5' end of the miRNA and this region is called the ‘seed sequence’ (14). When the miRNA has a perfect complementarity with its mRNA target, there is a reduced protein level accompanied to a reduced mRNA level because the mRNA is cleaved (phenomenon known as ‘RNA silencing’) (15).

Recent studies have shown a link between altered miRNA expression patterns and cancer (16). Calin et al. (17,18) were the first to show a link with chronic lymphocytic leukemia. In 2004, Takamizawa et al. (19) first reported the role of let-7 in lung cancer. Moreover, altered miRNA expression patterns have been described in B-cell lymphomas (20), Burkitt's lymphoma (21), breast cancer (22), lung cancer (23), hepatocellular carcinoma (24), glioblastoma (25), follicular thyroid carcinoma (26) and CRC (27). Given the important role of miRNA in gene regulation and in carcinogenesis, we hypothesized that germ line polymorphisms in 3' UTRs targeted by miRNAs might alter the strength of miRNA binding, with consequences on regulation of target genes thereby affecting the individual's cancer risk. To test this hypothesis, we evaluated 104 genes belonging to different pathways relevant for colorectal carcinogenesis. We searched for all the polymorphisms residing in the 3' UTRs of the candidate genes and assessed in silico the abilities of each single-nucleotide polymorphism (SNP) to impact the binding between miRNAs and their target sites. In order to verify our initial hypothesis, we genotyped the resulting putatively functional polymorphisms in 697 CRC cases and 624 controls from the Czech Republic, where the incidence of colon cancer ranks the third highest worldwide and is the highest for rectal cancer (28,29).


   Materials and methods
Top
 Abstract
 Introduction
 Materials and methods
Results
 Discussion
 Funding
 References
 
Study population
A hospital-based case–control study was conducted to assess gene–environment interactions in relation to CRC risk. Cases were incident patients with a new diagnosis of CRC attending nine oncological departments (two in Prague, one in Benesov, Brno, Liberec, Ples, Pribram, Usti nad Labem and Zlin) all over the Czech Republic from September 2004 to February 2006. All cases had histological confirmation of their tumor diagnosis. During the study period, a total of 968 cases were diagnosed with CRC in these hospitals. This study includes 697 (72%) patients who could be interviewed and provided biological samples of sufficient quality for genetic analysis. The lost cases were similar to those enrolled with respect to age, sex, tumor location and extent.

Controls were selected from among patients admitted to the same hospital during the same period and were subjects undergoing colonoscopy for various gastrointestinal complaints. The reasons for undergoing the colonoscopy were i) positive fecal occult blood test, ii) hemorrhoids, iii) abdominal pain of unknown origin and iv) macroscopic bleeding. Due to the high incidence of CRC in the Czech Republic, colonoscopy is largely recommended and practiced. To avoid selection bias, the criterion for inclusion of controls was that the reason for the current admittance to the hospital should be a new disease (not diagnosed previously) for that patient. This criterion was used to avoid inclusion of patients with chronic diseases, who might be repeatedly admitted to hospital and modify their habits because of their disease. This procedure paralleled the criterion for cases who were also newly diagnosed incident cases. Among 899 selected controls, a total of 624 (70%) were analyzed in this study. Twelve percent could not be interviewed because of refusal or mental or other impairment. Eighteen percent were interviewed but did not provide a blood sample. Controls included people who had benign lower abdominal pains, hemorrhoids with active bleeding, solitary rectal ulcer, lower gastrointestinal bleeding, diverticular disease, diarrhea and anemia. People with polyps, adenomas or other diagnoses related to cancer or to diseases known to predispose to cancer (such as, e.g. ulcerative colitis, inflammatory bowel disease and Crohn's disease) were excluded. Sex and broad age groups were used as stratifying criteria for frequency matching. Both cases and controls had to be in good mental condition, be able to see and hear and follow an interview and (for controls) not have diagnoses clearly related to cancer or chronic inflammatory diseases.

All subjects were informed and gave written consent to participate in the study to allow their biological samples to be genetically analyzed, according to the Helsinki declaration. The design of the study was approved by the Ethical Committee of the Institute of Experimental Medicine, Prague, Czech Republic.

Interviews
Cases and controls were personally interviewed by trained personnel using a structured questionnaire to determine demographic characteristics and potential risk factors for CRC. For each subject, age and sex were recorded. Study subjects provided information on their lifestyle habits, body mass index, diabetes, tentative occupational exposure to xenobiotics and family/personal history of cancer. A detailed dietary history questionnaire focused on average food consumption 1 year before the diagnosis of disease. Lifelong long-term (at least six consecutive months) drug use was included in the questionnaire. An initial open question was followed by a list of 20 chronic diseases that usually are treated pharmacologically and their treatments were recorded. No drug list was used. For each exposure, the ages at initial use and cessation were recorded and the cumulative duration was computed. Drugs were grouped using the anatomical therapeutic chemical classification. Other relevant risk factors explored were smoking, alcohol, physical activity and family history of cancer (Table I).

Selection of polymorphisms
In this study, we analyzed 104 candidate genes for CRC: 51 genes are involved in inflammatory processes, 37 belong to synthesis of prostaglandins and thromboxanes and 16 genes are connected with obesity and insulin resistance. The selected genes were among the most characterized and studied, according to the literature and the BioCarta and KEGG pathways (http://cgap.nci.nih.gov/Pathways). They are listed in Table II. For all of them, the 3' UTRs were selected according to the University of California Santa Cruz genome browser (http://genome.ucsc.edu) and defined as transcribed sequences from the stop codon to the end of the last exon of each gene. Putative miRNA-binding sites within the 3' UTR of each gene were identified by means of specialized algorithms (e.g. miRBase (30), miRanda (31), PicTar (32), MicroInspector (33), Diana-MicroT (34) and TargetScanS (35), using the default parameters included in the software, for each of them. The SNPs residing on the miRNA-binding sites were found by an extensive search in dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP), using BLAST (http://www.ncbi.nlm.-nih.gov/BLAST/Blast.cgi) and BLAST-SNP algorithms (http://www.ncbi.nlm.nih.gov/SNP/snpblastByChr.html). For all the identified SNPs, miRanda was run again to assess the Gibbs binding free energy ({Delta}G, expressed in KJ/mol) both for the common and the variant alleles. The difference of the free energies between the two alleles was computed as ‘variation of {Delta}G’ (i.e. {Delta}{Delta}G). Because in some genes the same sequence is predicted to bind several miRNAs, and thus, a SNPs in these sequences could impact the binding site of more than one miRNA, we used the sum of all the |{Delta}{Delta}G|s for each SNP (|{Delta}{Delta}Gtot|) as parameter for predicting the biological impact of the polymorphism. Although each target can bind only one miRNA at a time in each tissue, this approach is based on the basic idea that the more miRNAs are predicted to bind to a given target, the more likely it is that at least one of them truly binds to the target. Thus, if the algorithms predict that only one miRNA binds to a given target, at least in theory, we should consider this target less important than others predicted to bind many different miRNAs. Paralogue miRNAs, when reported to have a different sequence, are considered as different miRNAs. When summing the {Delta}{Delta}Gs, we did not account for the exactly duplicated miRNAs, which are, in this sense, considered as unique.


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  		Table I. Characteristics of CRC patients and control subjects

 


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  		Table II. List of 104 candidate genes evaluated for the presence of polymorphic miRNA target sites

 
In order to give a priority list of polymorphisms having real impact on miRNA bindings, we ranked the values of |{Delta}{Delta}Gtot| and we chose (arbitrarily) the upper tertile of the distribution (|{Delta}{Delta}Gtot| ≥ 5.27 KJ/mol) as the significant cutoff. In other words, when the SNP had a |{Delta}{Delta}Gtot| ≥ 5.27 KJ/mol was considered biologically relevant, whereas for |{Delta}{Delta}Gtot| < 5.27 KJ/mol the SNPs were considered biologically neutral and not included in further analyses. Moreover, as second criterion for SNP selection, we excluded the SNPs having the minor allele frequency (MAF) lower than 0.10 (for Caucasians). Thus, only polymorphisms falling in the upper tertile of |{Delta}{Delta}Gtot| with MAF > 0.10 were selected for genotyping. According to Breslow and Day (36), this MAF cutoff gave a statistical power of 85% or greater to detect an odds ratio (OR) of at least 1.5 in our sample set (for the codominant model).

Laboratory techniques
Genotyping was carried out with the 5' nuclease assay (TaqMan; Applied Biosystems, Foster City, CA). Two TaqMan probes were used, one for each allele. Analysis was performed using the ABI PRISM® 7900HT Sequence Detection System and SDS 2.2 software (Applied Biosystems).

The reaction employed the TaqMan Universal PCR Master Mix 2x, TaqMan primers and probes (20x or 40x), water and 5 ng of DNA. The thermal cycle program used included one cycle at 95°C for 10 min to activate the AmpliTaq Gold enzyme activation, 40 cycles at 92°C for 15 s to denature the DNA and at 60°C for 1 min for the stage of annealing/extension. The assays ID of probes are C___441625_10 (for CD86, rs17281995), C___7537839_10 (for IL12B, rs1368439), C___1844363_10 (for IL16, rs1131445), C___27465694_10 (for NOD2, rs3135500), C___8356120_10 (for INSR, rs1051690), C___8685434_10 (for ALOX15, rs916055) and C___362670_20 (for PLA2G2A, rs11677). The SNP rs16870224 (PTGER4) was genotyped according to an ABI assay-by-design. The sequences of primers and probes for this polymorphism are 5'-AGTGTCTCACTAAAGCATGAAATGTGAA (forward primer), 5'-CCTTGTATTAACAATAAACCTTCTCACAGAGA (reverse primer), VIC-5'-CATACGATTTAAGGTATTTAA (reporter1) and FAM-5'-CATACGATTTAAAGTATTTAA (reporter2).

Statistical analysis
Each polymorphism was tested to ensure the fitting with Hardy–Weinberg equilibrium with alpha threshold of 0.05. We used a multivariate logistic regression analysis to examine associations between these polymorphisms and CRC risk by estimating the ORs and 95% confidence intervals (CIs), adjusted for sex and age (as linear variable) as covariates. Genotypes have been divided into three groups: major allele homozygous (also defined as +/+), heterozygous (+/–) and homozygous variant (–/–). This analysis had been done using a codominant model: ORs and 95% CI were calculated for each genotype compared with the group +/+. Test for linear trend of ORs were calculated using the categorized variable as quantitative, after assigning a linear score to each ordered category. When associations were detected, also the recessive (homozygotes variant versus heterozygotes + homozygotes common allele) and dominant (homozygotes variant + heterozygotes versus homozygotes common allele) models were evaluated. The statistical P-level of significance was also evaluated after Bonferroni's correction for multiple testing. All statistical analyses were conducted using STATA software version 8.0 (Stata Corp. LP, College Station, TX).


   Results
Top
 Abstract
 Introduction
 Materials and methods
 Results
Discussion
 Funding
 References
 
Among the 104 starting candidate genes, 46 did not present any SNP in their 3' UTRs and 21 genes did not show SNPs in miRNA target sequences. The remaining 37 genes showed 57 SNPs in the predicted miRNA-binding sites. However, among them, only eight SNPs were positive for both the selection criteria (|{Delta}{Delta}Gtot| ≥ 5.27 KJ/mol and MAF > 0.10) and were further investigated in the case–control study. Table III shows the nucleotide variation, the predicted binding miRNAs, variation of {Delta}G (|{Delta}{Delta}G|) for each miRNA and the overall |{Delta}{Delta}Gtot| for the eight selected SNPs. These SNPs were in the 3' UTR of CD86 and NOD2 (receptors involved in the immune response); IL16 and IL12B (cytokines), ALOX15, PLA2G2A and PTGER4 (biosynthesis of prostaglandins and thromboxanes) and INSR (obesity and insulin resistance).


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  		Table III. Candidate SNPs with |{Delta}{Delta}Gtot|a > 5.27 KJ/mol (upper tertile of the distribution of |{Delta}{Delta}Gtot|) and MAF > 0.10 (validated in Caucasians), evaluated in the case–control association study

 
We tested the genotypes for Hardy–Weinberg equilibrium, using P = 0.05 as significance threshold and all SNPs were in equilibrium in controls. Table IV shows the statistical analyses, for the codominant model only. Both the heterozygotes and homozygotes for the variant allele rs17281995 within CD86 were associated with an increased risk of CRC (OR = 1.33; 95% CI = 1.00–1.76 and OR = 2.93; 95% CI = 1.29–6.67, respectively). Moreover, the homozygotes for the variant allele rs1051690 within INSR showed an OR of 1.86 with a 95% CI of 0.99–3.50 (P = 0.052). This polymorphism reached the significance level using a recessive model (heterozygotes + homozygotes for common variant versus homozygotes rare variant) with an OR of 1.94 and 95% CI of 1.04–3.64 (P = 0.03). After Bonferroni's correction for multiple testing, the new threshold for the statistical significance was 0.0062. Under this corrected P-value, the trend test for CD86 held statistical significance. It is important to note that the association found for CD86 in the dominant model, with an OR of 1.44 (95% CI = 1.10–1.88; P = 0.015) was also very close to this tight limit of significance. Stratifying for the site of cancer, the increased risk for CD86 was observed both for the colon and rectum; however, it was more evident in the rectum (OR = 1.5; 95% CI 1.05–2.15; P = 0.028, dominant model).


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  		Table IV. Logistic regression analysis for the selected SNPs in the miRNA target sequences

 

   Discussion
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
Funding
 References
 
In the present study, two polymorphisms (rs17281995 in CD86 and rs1051690 in INSR) were found associated with an increased risk of CRC. Five different miRNAs bind to a target site that contains the same polymorphism within CD86: miR-337, miR-582, miR-200a*, miR-184 and miR-212 (Table III). When C substitutes G, miR-337, miR-582 and miR-200a* are predicted to bind less tightly to the CD86 3' UTR. In contrast, miR-184 and miR-212 increase their binding affinity. Among all the SNPs analyzed, this is the only one that impacts the binding site of five different miRNAs at the same time. In other words, the SNP that produces the largest predicted effect is also the most strongly associated with risk in the present case–control association study. Moreover, this finding holds even after the Bonferroni's correction, which is generally considered very stringent (37). Furthermore, it should be considered that part of the selected miRNA target sites could have been the result of erroneous prediction by the algorithms (limited experimental information is available for most of the selected miRNAs, according to the latest update of Tarbase 4.0; http://www.diana.pcbi.upenn.edu/tarbase.html). It should be stressed that miR-582 was shown to be present in normal colon tissues, and its expression appeared to be reduced in CRC following measurements done with quantitative real-time polymerase chain reaction (38). We could speculate that this finding seems in agreement with the fact that the SNP predicted to cause a reduced binding of miR-582 to its target is associated with increased risks of CRC. Taken together, all these facts encourage further investigation, by replicating the results in other populations and by making in vitro experiments in the biological activity of these polymorphisms.

CD86, with CD80, is a costimulatory ligand expressed on the surface of the antigen presenting cells (dendritic cells, macrophages and B cells), in the immune system (39). Binding of these molecules to the T-cell costimulatory receptors, CD28 and CTLA-4 is essential for the activation and regulation of T-cell immunity (40). Functionally, the binding of CD28 with CD80 and CD86 ligands delivers a positive signal to T cells that culminates in T-cell proliferation, cytokine production and prevents induction of T-cell tolerance (41). In contrast, the interaction of CTLA-4 with CD80/CD86 results in negative signaling leading to attenuation of T-cell activation. Such processes are essential for the regulation of a T-cell response and maintaining T-cell homeostasis (42). CD80 and CD86 have been shown to modulate also the Th1/Th2 T-cell differentiation. While CD80 preferentially favors Th1-type T-cell differentiation, CD86 augments IL4 production and overall Th2-type T-cell responses (mostly producing anti-inflammatory cytokines) (43). Normal colonic tissue expresses CD80 and CD86 only sporadically, whereas in the pathogenesis of Crohn's and inflammatory bowel diseases, a sharp increase is observed paralleling the impaired and/or excessive activation of mucosal macrophages and T lymphocytes (44). It should be considered that even a normal colonic mucosa undergoes a continuous state of subchronic inflammation due to the normal bacterial activity present within its lumen (45). Thus, our results support the hypothesis that the regulation of the immune response and its consequent level of local inflammatory microenvironment could play an important role for the modulation of the individual risk. CD86 is probably one of the key molecules involved in these processes. If we assume the role of miR-582 for CD86, one could speculate that the rare variant of rs17281995 is linked to increased levels of CD86, thereby favoring an inflammatory microenvironment predisposing to CRC.

Although less strongly associated with the risk of CRC, it is worth to note that also the variation G/A within the 3' UTR of INSR has a predicted strong impact in the biology of the miRNA target, as it can be observed by the fifth strongest |{Delta}{Delta}Gtot| (11.25 KJ/mol, see Table III). It is important to stress that this value is due largely by the predicted alteration with miR-612 (7.41 KJ/mol), the second strongest individual |{Delta}{Delta}G| observed in this study.

The levels of glucose and insulin increase after each meal. Following binding of insulin to the extracellular portion of the insulin receptors, a second messenger system diverges into two separate pathways that regulate distinct biological effects: the phosphoinositide-3-kinase pathway or the mitogen-activated protein kinase pathway (46). In insulin resistance, the ability of insulin to initiate these phosphorylation cascades is diminished (47). Epidemiologic studies indicate that the insulin resistance, elevated fasting plasma insulin, glucose and free fatty acids, glucose intolerance, increased body mass index and visceral adiposity are all associated with adenomas, a precursor lesion for CRC (48). Several mechanisms exist whereby insulin signaling can be modulated at the level of the INSR. Generally, these processes involve either the modification of INSR contained in the cells at the level of transcription or protein degradation or by modification of the enzymatic activity of individual INSR (49). Puig and Tijan (50) have demonstrated that the FOXO1 transcription factor mediates a downregulation of INSR transcription in response to insulin stimulation of insulin receptor signaling (51). Insulin also reduces the effective concentration of cellular insulin receptor by increasing internalization and degradation of the protein as well (51). Activation of this system produces a down-regulation of multiple components of the insulin signaling system (52).

We were aware that there could be a potential bias in this study because controls were selected among people undergoing colonoscopy. However, in terms of diet, socioeconomic status, ethnic and familial origins and all the variables collected with in questionnaires, there is no evidence that the controls are not representative of the general population of the Czech Republic. Moreover, in previous studies on the same series of colonoscopized controls, the frequencies of polymorphic alleles within DNA repair genes were shown to be comparable with those of the general healthy Czech population (53,54), making it reasonable to think that our controls are representative of the general population.

This study has other strengths: i) an adequate sample size (in relation to the criteria of power we set) and ii) the representative character of the study subjects for the entire country and, basically, for central Europe, a very homogeneous area, with generally typical lifestyle and dietary habits, in particular in the last 50–60 years. It is of interest to note that our results are corroborated also by a recent research showing that some miRNA-binding SNPs exhibit significant different allele frequencies between the human cancer expressed sequence tag libraries and the dbSNP database. More importantly, using human cancer specimens against the dbSNP database for case–control association studies, it has been found that 12 miRNA-binding SNPs indeed display an aberrant allele frequency in human cancers. Hence, it can be concluded that SNPs located in miRNA-binding sites affect miRNA target expression and function and are potentially associated with cancers (55). In addition, recently it has also been shown that polymorphic miRNA-binding sites could affect the individual blood pressure levels, further stressing the importance of the polymorphisms involved in these mechanisms for several human traits (56).

In conclusion, we have analyzed 104 CRC-related genes and selected, with in silico methods, eight SNPs predicted to have an impact on miRNA binding. Two of them were associated with CRC risk, one remained statistically significant after Bonferroni's correction. This study provides evidence that these SNPs in miRNA-binding sites may be important to cancer risk and supports future work to validate the results in other well-characterized populations as well as to explore the biological significance of these particular SNPs. Our work is the first showing an association between cancer risk and polymorphic miRNA-binding sites. These results show also that a careful a priori selection of candidate SNPs could increase the signal-to-noise ratio in case–control association studies. The proposed approach could ease the identification of functionally relevant polymorphisms involved in carcinogenesis to be further analyzed by appropriate (but time consuming) experiments of molecular biology.


   Funding
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Funding
References
 
Czech Republic (GACR 310/05/2626 and GACR 310/07/1430); Associazione Italiana Ricerca Cancro (Italy) regional grant.


   Acknowledgments
 
Conflict of Interest Statement: None declared.


   References
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Funding
 References
 

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Received October 24, 2007; revised December 6, 2007; accepted December 22, 2007.


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