Association between risk of oral precancer and genetic variations in microRNA and related processing genes
© Roy et al.; licensee BioMed Central Ltd. 2014
Received: 28 March 2014
Accepted: 10 May 2014
Published: 17 May 2014
MicroRNAs have been implicated in cancer but studies on their role in precancer, such as leukoplakia, are limited. Sequence variations at eight miRNA and four miRNA processing genes were studied in 452 healthy controls and 299 leukoplakia patients to estimate risk of disease.
Genotyping by TaqMan assay followed by statistical analyses showed that variant genotypes at Gemin3 and mir-34b reduced risk of disease [OR = 0.5(0.3–0.9) and OR = 0.7(0.5–0.9) respectively] in overall patients as well as in smokers [OR = 0.58(0.3–1) and OR = 0.68(0.5–0.9) respectively]. Among chewers, only mir29a significantly increased risk of disease [OR = 1.8(1–3)]. Gene-environment interactions using MDR-pt program revealed that mir29a, mir34b, mir423 and Xpo5 modulated risk of disease (p < 0.002) which may be related to change in expression of these genes as observed by Real-Time PCR assays. But association between polymorphisms and gene expressions was not found in our sample set as well as in larger datasets from open access platforms like Genevar and 1000 Genome database.
Variations in microRNAs and their processing genes modulated risk of precancer but further in-depth study is needed to understand mechanism of disease process.
KeywordsOral leukoplakia miRNA DNA sequence variation MDR Gene expression
Leukoplakia is one of the major forms of oral precancerous lesions that can be clinically characterized primarily as a non-scratchable white lesion of the oral mucosa . Incidence of this lesion varies from 1.3 to 2.1 per 1000 individuals in different parts of India depending on the types of tobacco habits but prevalence of this lesion was found to be about 17 per 1000 tobacco users . Several factors like tobacco carcinogen, alcohol, HPV infection and genetic predisposition are suspected causative agents for leukoplakia . A survey on Indian populations found that ~80% of oral cancers were preceded by pre-cancerous lesions and oral leukoplakia being most common among them .
MicroRNAs (miRNAs) are highly conserved small regulatory RNAs that play fundamental biological roles in all known plant and animal species . They are transcribed mainly by RNA Pol II and go through series of processing before they become functionally active. Initial large primary transcripts or “pri-miRNAs” are sequentially cleaved in the nucleus by microprocessor complex which contains DROSHA ribonuclease and DGCR8, to form premature precursors or pre-miRNA. These 70–80 nt long precursors are exported to cytoplasm by RAN-GTPase and Exportin 5 (XPO5), after which they are further cleaved to produce ‘mature’ (~22 nucleotides in length) single-stranded miRNAs that are bound to the RISC complex comprising DICER, GEMIN3, AGO1 etc. MiRNAs are reported to function by binding ‘target’ mRNAs through partial or complete base-pair complementarity at 3′UTR and this leads to either a decreased rate of polypeptide synthesis or complete degradation of mRNA templates, respectively [6, 7]. To date, more than 2500 miRNA molecules have been identified in the human genome and they play important roles in a broad range of physiological and pathological processes [8–10]. Genetic variation at miRNA and their processing genes have potential to affect regulation of multiple cellular pathways instrumental in cancer development and susceptibility . In order to investigate their potential role in altering risk of oral leukoplakia, single nucleotide polymorphisms (SNPs) in four miRNA processing and pri-miR and pre-miR regions of eight miRNA genes were studied in leukoplakia patients. Then, gene-environment interaction was studied using Multi Dimensionality Reduction (MDR) approach to understand risk of leukoplakia. We also examined whether expression of these genes are modulated by SNPs not only in our samples but also in publicly available databases, such as Genevar and 1000 Genome project.
Patients and controls
This study was approved by “Review committee for protection of research risk to humans, Indian Statistical Institute”. Unrelated controls (n = 452) and oral leukoplakia patients (n = 299) were recruited from two tertiary referral hospitals, namely R. Ahmed Dental College and Hospital  and Gurunanak Institute of Dental Science and Research, Kolkata, India . All patients and controls had at least one kind of tobacco habit. Patients were referred to these hospitals for biopsy and diagnosis by primary health centers and were not under any prior medication for leukoplakia. Unrelated controls visited these hospitals for treatment of dental ailments, not related to any kind of precancer and cancer, and had no family history of cancer. Demographic details and tobacco habits were noted after written consent.
Sample collection and processing
Venous blood (~3 ml) was collected from all patients and controls and stored at −20 ºC until DNA isolation. Genomic DNA was isolated by salt extraction method  and QIAamp DNA extraction kit (QIAGEN, Valencia, CA). Biopsy tissues collected from patients were used for histopathology and expression study. Normal tissues were also collected from few individuals who were recruited as controls. To study RNA expression, part of the biopsy tissues were collected in RNA Later and stored at −20°C until use. Total RNA was extracted using All Prep DNA/RNA Mini Kit (QIAGEN, Valencia, CA) following protocol provided with the kit.
All 12 SNPs selected in this study have been implicated in various tobacco related squamous cell carcinomas in previous reports [15–18]. Eight of them: rs11614913(C/T) at miR-196a2, rs2910164 (G/C) at miR-146a, rs7372209 (C/T) at miR-26a-1, rs6505162 (A/C) at miR-423, rs213210 (A/G) at miR-219-1, rs2660304 (G/T) at miR-137, rs2187473 (T/C) at miR-34b and rs24168 (A/G) at miR-29a are located at miRNAs. Remaining four SNPs are located in miRNA processing genes: rs197412(C/T) at GEMIN3, rs3742330 (A/G) at DICER1, rs11077 (A/C) at XPO5 and rs14035(C/T) at RAN[15, 16].
Genotyping at all SNPs was performed by Taqman method in 7900HT FAST Real-Time PCR system (Applied Biosystems, USA). To reconfirm genotypes determined by Taqman method, ~15% of the samples were randomly selected and further genotyped by a different person (who had no prior information of the genotypes) either by re-sequencing (ABI 3100 Genetic Analyzer, Applied Biosystem, USA) or PCR-RFLP. Except for mir146a and mir196a-2, primers for sequencing and RFLP studies were designed by us.
Expressions of miR processing genes (Gemin3 and Xpo5) and miRNAs (mir29a, mir34b and mir423) in 19 leukoplakia and 19 control tissues were studied by TaqMan method (7900HT Fast Real Time PCR System, Applied Biosystem, USA). Normalized expression of a gene was calculated as ΔCt with respect to endogenous control genes, RNase P or RNU44 in case of miR processing or miRNA genes respectively.
Each SNP data from controls was checked for Hardy-Weinberg equilibrium (HWE). Male/female distribution and number of smoker/chewers were different in control and leukoplakia groups. So, age-, sex- and tobacco dose-adjusted risk of leukoplakia was calculated as odds ratios (ORs) with 95% confidence intervals (CIs) for all genotypes by binary logistic regression analysis using PLINK software . Risk of leukoplakia was also calculated in patients stratifying them on basis of their tobacco habits. Chi-square test with Yates’ correction, when necessary, was used for comparison of genotype proportions. Three different genetic models for disease, such as, dominant model (comparing homozygous major genotype with variant allele-carrying genotypes), recessive model (comparing major allele-carrying genotypes with homozygous variant genotype) and additive model (i.e. trend test) were considered in analysis. Since there were multiple SNPs involved in the study, multiple testing corrections were done using Benjamini-Hochberg method . Spearman’s correlation test was performed to evaluate difference between gene expressions depending upon genotypes at SNPs.
Multi dimensionality reduction (MDR) analysis
To analyze possible interaction among SNPs and other covariates, non-parametric MDR approach was used [22, 23]. All 12 SNPs and covariates (Pack Year and Chewing Year) were considered to construct interaction models in patients comparing with those of control individuals. For every SNP, three genotypes were coded as 0, 1 and 2; no-smoking/chewing dose was coded as 0, low smoking/chewing dose (<mean PY or CY in controls) coded as 1 and high smoking/chewing dose (>mean PY or CY in controls) was coded as 2. Statistical significance was determined using permutation testing in MDRpt (version 1.0_beta_2). Ten fold cross-validation and 1000 fold permutation testing were used and interaction models with p-value <0.05 were considered as significant. Cross-validation consistency (CVC) score is a measure of degree of consistency with which identified interaction is most evident among all possibilities . Among significant models, cross validation consistency (CVC) ≥ 9 was considered as important, as the data was cross validated 10 times by MDR. Best model was then defined with largest testing balance accuracy (TBA) among important models. Testing Balanced Accuracy (TBA) is a measure of degree to which interaction accurately predicts case–control status, taking into account the ratio of cases to controls, with scores between 0.50 (indicating that model predicts no better than chance) and 1.00 (indicating perfect prediction). Scores of at least 0.55 are considered ‘interesting’ .
Analysis of 1000 Genome database
Small RNA expression data and whole genome sequencing data on 485 individuals of 1000 Genome project has been recently released and is openly accessible . MiRNA RNA-Seq and genotype data, obtained from lymphoblastoid cell line, of 87 Caucasian was extracted for our analysis. MiRNA RNA-Seq data was analyzed using miRExpress software  which converts available FASTQ data into read counts. Read counts were normalised using two methods- i) RPM or Reads per million  and ii) DEseq tool from Bioconductor software . Genotype data were obtained from 1000 Genome web browser.
Analysis of data from Genevar platform
Genevar (GENe Expression Variation) platform has gene variation and gene expression data from HapMap3 population (http://www.sanger.ac.uk/resources/software/genevar/) . After launching the application in a Java enabled computer, gene name and rs ID is entered in the “eQTL-SNP-Gene” option. Genevar provides spearman’s correlation coefficient and p value for analysis between gene expression and genotypes for 8 different populations.
Demography and tobacco habits of leukoplakia patients and controls
Subjects and tobacco habits
Control [N = 452] (%)
Leukoplakia [N = 299] (%)
Age (Mean ± SD)
48.5 ± 11
47.4 ± 10.4
Polymorphisms at miRNA and miRNA processing genes and risk of leukoplakia (Only SNPs with significant p-values are shown)
Genotype and Alleles
Control (%)/leukoplakia (%)
O.R (95% C.I)a
TT + CT
CT + TT
Within Tobacco Stratified Individuals
(bTobacco Smokers : Controls- 267/Leukoplakia-255)
TT + CT
CT + TT
(bTobacco Chewers : Controls-277/Leukoplakia-129)
GG + AG
MDR interaction analysis between loci and tobacco habits in “leukoplakia vs control” group
Best predictive interactive modela
1000 permutation p valued
PY CY mir196a2
PY CY mir34b mir29a
PY CY mir29a mir34b mir423 Xpo5
Discussion and conclusion
Variant homozygote at Gemin3 reduced risk of leukoplakia significantly (OR = 0.5, 95% CI = 0.3–0.9). Few other studies had also reported association between oral premalignant lesion and SNP at Gemin3[15, 30, 31]. This polymorphism did not seem to alter Gemin3 expression in our small dataset of 19 leukoplakia tissues or larger Hapmap3 data set. This SNP lies in the exonic region and, thus, may alter gene activity or could be in the vicinity of other causal SNPs. Study by Clague et al.  has shown that SNPs rs197412 at Gemin3, rs7372209 at mir26a-1 and rs3742330 at Dicer1 altered risk of oral pre-malignant lesion but except Gemin3, our study could not find association between polymorphisms at mir26a-1 and Dicer1 and risk of leukoplakia (data not shown). It is worth mentioning that sample size was smaller (136 patients and 136 controls) in the report by Clague et al. . Variant allele containing genotypes at mir34b were found to reduce risk of leukoplakia in overall sample population (p = 0.05) as well as in smokers (p = 0.04) (Table 2). Again, we did not find any difference in expression of mir34b across its three genotypes at rs2187473. But this SNP lies 1 kb downstream of rs4938723 that has been shown to lie in the promoter region of mir34b and influence risk of various types of cancer [32–34]. So, it needs to be investigated whether these two SNPs are in LD. In our recent study on cancer patients, we got similar results for Gemin3 and mir-34b. It is interesting to study how these two polymorphisms alter risk of both oral precancer and cancer. It was found that risk of leukoplakia was reduced significantly (p =0.02) when patients harbored protective variant genotypes at both loci (CC at Gemin3 and CT/TT at mir34b).
In order to analyze implication of polymorphism on gene expression, we not only studied our sample set (N = 19) but also explored publicly available databases like 1000 Genome and Genevar platform. We assumed that if a SNP alters gene expression, it would do so in all cells including lymphoblastoid cell line. Although, the sample size was in the range of 87–100 in databases, we still did not find any significant variation in expression across genotypes at a SNP, similar to results in our sample set (data not shown).
Alongside we used MDR to predict statistical epistasis which describes an effect exceeding the combined effects of each genetic factor . It has been observed that there might be non-linear interaction between tobacco habits and mir29a, mir34b, mir423 and Xpo5 and, as a result, risk of leukoplakia may be modulated (Table 3). So, we compared expression of these genes in leukoplakia with respect to control tissues to understand whether there is any deregulation in expression independent of polymorphism. Although expression of mir29a, mir34b, mir423 and Xpo5 was not modulated by genotypes at SNPs but expression of these genes was significantly different in leukoplakia with respect to control tissues. This finding further emphasizes importance of these miRNAs in leukoplakia development. In a recent report, miR-29a was found to be down regulated in serum of patients with high risk oral lesions (HRL), showing high specificity in identifying HRLs . They also observed that miR-423 was differentially expressed across oral squamous cell carcinoma, carcinoma in situ, and controls. MiR-423 was found to significantly promote cell growth and cell cycle progression at the G1/S transition in HCC cells . Functional studies have reported both up- and down- regulation of miR-34b in tongue and oral squamous cell carcinoma, respectively [17, 38]. MiR-34b is also involved in a number of tumorigenesis-related molecular mechanisms, including epithelial-mesenchymal-transition (EMT) . So, it suggests that our observation related to mir29a, mir34b, mir423 and Xpo5 in leukoplakia might have important implications in transformation of normal epithelium to leukoplakia.
There are several reports on association of microRNA SNPs with a wide repertoire of disease [11, 40] but most of them did not show how these variations actually alter miRNA expression or activity. Here, it is shown that SNPs at miRNA and processing genes are associated with risk of leukoplakia statistically but most of the associations are lost after correction for multiple testing. First limitation of the study is small number of candidate SNPs so it is important to re-examine association study with more number of SNPs since it is known that a large number of miRNAs are associated with risk of different precancer and cancer. Second limitation is sample size of leukoplakia patients. Partially, this might be due to the fact that many precancer patients prefer to avoid hospital since precancer is not life-threatening. But it is always worthwhile to study larger number of patients. Importance of this study lies in the observation that expression of genes, which interact with environmental factors to modulate risk of leukoplakia, was significantly altered in leukoplakia compared to control tissues (Figure 1). Since miRNAs target genes involved in cell proliferation, apoptosis etc., it is essential not only to study expression of more miRNAs involved in these pathways but also explore significance of more miRNA-associated polymorphisms on large sample sets to validate their significance in leukoplakia.
This project is funded by Department of Biotechnology, Government of India and Indian Statistical Institute. Ms. Roshni Roy and Mr. Navonil De Sarkar are research fellows funded by Indian Statistical Institute and CSIR, Government of India, respectively. Authors would like to thank patients and control individuals for giving consent to use their DNA and tissue in this research.
- Leukoplakia Report. [http://screening.iarc.fr/atlasoral_list.php?%20cat=Az&lang=1]
- Reddi SP, Shafer AT: Oral premalignant lesions: management considerations. Oral Maxillofac Surg Clin. 2006, 18: 425-433. 10.1016/j.coms.2006.08.002.View ArticleGoogle Scholar
- Liu W, Shi LJ, Wu L, Feng JQ, Yang X, Li J, Zhou ZT, Zhang CP: Oral cancer development in patients with leukoplakia–clinicopathological factors affecting outcome. PLoS One. 2012, 7: e34773-10.1371/journal.pone.0034773.PubMed CentralView ArticlePubMedGoogle Scholar
- Gupta PC, Bhonsle RB, Murti PR, Daftary DK, Mehta FS, Pindborg JJ: An epidemiologic assessment of cancer risk in oral precancerous lesions in India with special reference to nodular leukoplakia. Cancer. 1989, 63: 2247-2252. 10.1002/1097-0142(19890601)63:11<2247::AID-CNCR2820631132>3.0.CO;2-D.View ArticlePubMedGoogle Scholar
- Wilfred BR, Wang WX, Nelson PT: Energizing miRNA research: a review of the role of miRNAs in lipid metabolism, with a prediction that miR-103/107 regulates human metabolic pathways. Mol Genet Metab. 2007, 91: 209-217. 10.1016/j.ymgme.2007.03.011.PubMed CentralView ArticlePubMedGoogle Scholar
- Ambros V: The functions of animal microRNAs. Nature. 2004, 431: 350-355. 10.1038/nature02871.View ArticlePubMedGoogle Scholar
- Bartel B: MicroRNAs directing siRNA biogenesis. Nat Struct Mol Biol. 2005, 12: 569-571. 10.1038/nsmb0705-569.View ArticlePubMedGoogle Scholar
- Vasudevan S, Tong Y, Steitz JA: Switching from repression to activation: microRNAs can up-regulate translation. Science. 2007, 318: 1931-1934. 10.1126/science.1149460.View ArticlePubMedGoogle Scholar
- Kim VN: MicroRNA biogenesis: coordinated cropping and dicing. Nat Rev Mol Cell Biol. 2005, 6: 376-385. 10.1038/nrm1644.View ArticlePubMedGoogle Scholar
- Kozomara A, Griffiths-Jones S: miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014, 42: D68-D73. 10.1093/nar/gkt1181.PubMed CentralView ArticlePubMedGoogle Scholar
- Ryan BM, Robles AI, Harris CC: Genetic variation in microRNA networks: the implications for cancer research. Nat Rev Cancer. 2010, 10: 389-402. 10.1038/nrc2867.PubMed CentralView ArticlePubMedGoogle Scholar
- Majumder M, Sikdar N, Ghosh S, Roy B: Polymorphisms at XPD and XRCC1 DNA repair loci and increased risk of oral leukoplakia and cancer among NAT2 slow acetylators. Int J Cancer. 2007, 120: 2148-2156. 10.1002/ijc.22547.View ArticlePubMedGoogle Scholar
- Roy R, De Sarkar N, Ghose S, Paul RR, Pal M, Bhattacharya C, Chowdhury SK, Ghosh S, Roy B: Genetic variations at microRNA and processing genes and risk of oral cancer. Tumor Biol. 2013, 35: 3409-3414.View ArticleGoogle Scholar
- Miller SA, Dykes DD, Polesky HF: A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res. 1988, 16: 1215-10.1093/nar/16.3.1215.PubMed CentralView ArticlePubMedGoogle Scholar
- Clague J, Lippman SM, Yang H, Hildebrandt MA, Ye Y, Lee JJ, Wu X: Genetic variation in MicroRNA genes and risk of oral premalignant lesions. Mol Carcinog. 2010, 49: 183-189.PubMed CentralPubMedGoogle Scholar
- Ye Y, Wang KK, Gu J, Yang H, Lin J, Ajani JA, Wu X: Genetic variations in microRNA-related genes are novel susceptibility loci for esophageal cancer risk. Cancer Prev Res (Phila). 2008, 1: 460-469. 10.1158/1940-6207.CAPR-08-0135.View ArticleGoogle Scholar
- Kozaki K, Imoto I, Mogi S, Omura K, Inazawa J: Exploration of tumor-suppressive microRNAs silenced by DNA hypermethylation in oral cancer. Cancer Res. 2008, 68: 2094-2105. 10.1158/0008-5472.CAN-07-5194.View ArticlePubMedGoogle Scholar
- Chen D, Cabay RJ, Jin Y, Wang A, Luo Y, Shah-Khan M, Zhou X: MicroRNA deregulations in head and neck squamous cell carcinomas. J Oral Maxillofac Res. 2013, 4: e2-PubMed CentralView ArticlePubMedGoogle Scholar
- Hu Z, Chen J, Tian T, Zhou X, Gu H, Xu L, Zeng Y, Miao R, Jin G, Ma H, Chen Y, Shen H: Genetic variants of miRNA sequences and non-small cell lung cancer survival. J Clin Invest. 2008, 118: 2600-2608.PubMed CentralPubMedGoogle Scholar
- Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007, 81: 559-575. 10.1086/519795.PubMed CentralView ArticlePubMedGoogle Scholar
- Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I: Controlling the false discovery rate in behavior genetics research. Behav Brain Res. 2001, 125: 279-284. 10.1016/S0166-4328(01)00297-2.View ArticlePubMedGoogle Scholar
- Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH: Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet. 2001, 69: 138-147. 10.1086/321276.PubMed CentralView ArticlePubMedGoogle Scholar
- Hahn LW, Ritchie MD, Moore JH: Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics. 2003, 19: 376-382. 10.1093/bioinformatics/btf869.View ArticlePubMedGoogle Scholar
- Epistasis Blog. [http://compgen.blogspot.in/2006/12/mdr-101-part-4-results.html]
- Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Kang HM, Marth GT, McVean GA: An integrated map of genetic variation from 1,092 human genomes. Nature. 2012, 491: 56-65. 10.1038/nature11632.View ArticlePubMedGoogle Scholar
- Wang WC, Lin FM, Chang WC, Lin KY, Huang HD, Lin NS: miRExpress: analyzing high-throughput sequencing data for profiling microRNA expression. BMC Bioinforma. 2009, 10: 328-10.1186/1471-2105-10-328.View ArticleGoogle Scholar
- Motameny S, Wolters S, Nürnberg P, Schumacher B: Next generation sequencing of miRNAs – strategies, resources and methods. Genes. 2010, 1: 70-84. 10.3390/genes1010070.PubMed CentralView ArticlePubMedGoogle Scholar
- Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biol. 2010, 11: R106-10.1186/gb-2010-11-10-r106.PubMed CentralView ArticlePubMedGoogle Scholar
- Yang TP, Beazley C, Montgomery SB, Dimas AS, Gutierrez-Arcelus M, Stranger BE, Deloukas P, Dermitzakis ET: Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies. Bioinformatics. 2010, 26: 2474-2476. 10.1093/bioinformatics/btq452.PubMed CentralView ArticlePubMedGoogle Scholar
- Horikawa Y, Wood CG, Yang H, Zhao H, Ye Y, Gu J, Lin J, Habuchi T, Wu X: Single nucleotide polymorphisms of microRNA machinery genes modify the risk of renal cell carcinoma. Clin Cancer Res. 2008, 14: 7956-7962. 10.1158/1078-0432.CCR-08-1199.PubMed CentralView ArticlePubMedGoogle Scholar
- Yang H, Dinney CP, Ye Y, Zhu Y, Grossman HB, Wu X: Evaluation of genetic variants in microRNA-related genes and risk of bladder cancer. Cancer Res. 2008, 68: 2530-2537. 10.1158/0008-5472.CAN-07-5991.View ArticlePubMedGoogle Scholar
- Bensen JT, Tse CK, Nyante SJ, Barnholtz-Sloan JS, Cole SR, Millikan RC: Association of germline microRNA SNPs in pre-miRNA flanking region and breast cancer risk and survival: the Carolina Breast Cancer Study. Cancer Causes Control. 2013, 24: 1099-1109. 10.1007/s10552-013-0187-z.PubMed CentralView ArticlePubMedGoogle Scholar
- Li L, Wu J, Sima X, Bai P, Deng W, Deng X, Zhang L, Gao L: Interactions of miR-34b/c and TP-53 polymorphisms on the risk of nasopharyngeal carcinoma. Tumor Biol. 2013, 34: 1919-1923. 10.1007/s13277-013-0736-9.View ArticleGoogle Scholar
- Xu Y, Liu L, Liu J, Zhang Y, Zhu J, Chen J, Liu S, Liu Z, Shi H, Shen H, Hu Z: A potentially functional polymorphism in the promoter region of miR-34b/c is associated with an increased risk for primary hepatocellular carcinoma. Int J Cancer. 2011, 128: 412-417. 10.1002/ijc.25342.View ArticlePubMedGoogle Scholar
- Edwards TL, Lewis K, Velez DR, Dudek S, Ritchie MD: Exploring the performance of Multifactor Dimensionality Reduction in large scale SNP studies and in the presence of genetic heterogeneity among epistatic disease models. Hum Hered. 2009, 67: 183-192. 10.1159/000181157.PubMed CentralView ArticlePubMedGoogle Scholar
- Maclellan SA, Lawson J, Baik J, Guillaud M, Poh CF, Garnis C: Differential expression of miRNAs in the serum of patients with high-risk oral lesions. Cancer Med. 2012, 1: 268-274. 10.1002/cam4.17.PubMed CentralView ArticlePubMedGoogle Scholar
- Lin J, Huang S, Wu S, Ding J, Zhao Y, Liang L, Tian Q, Zha R, Zhan R, He X: MicroRNA-423 promotes cell growth and regulates G(1)/S transition by targeting p21Cip1/Waf1 in hepatocellular carcinoma. Carcinogenesis. 2011, 32: 1641-1647. 10.1093/carcin/bgr199.View ArticlePubMedGoogle Scholar
- Wong TS, Liu XB, Wong BY, Ng RW, Yuen AP, Wei WI: Mature miR-184 as potential oncogenic microRNA of squamous cell carcinoma of tongue. Clin Cancer Res. 2008, 14: 2588-2592. 10.1158/1078-0432.CCR-07-0666.View ArticlePubMedGoogle Scholar
- Siemens H, Jackstadt R, Hunten S, Kaller M, Menssen A, Gotz U, Hermeking H: miR-34 and SNAIL form a double-negative feedback loop to regulate epithelial-mesenchymal transitions. Cell Cycle. 2011, 10: 4256-4271. 10.4161/cc.10.24.18552.View ArticlePubMedGoogle Scholar
- Slaby O, Bienertova-Vasku J, Svoboda M, Vyzula R: Genetic polymorphisms and microRNAs: new direction in molecular epidemiology of solid cancer. J Cell Mol Med. 2012, 16: 8-21. 10.1111/j.1582-4934.2011.01359.x.PubMed CentralView ArticlePubMedGoogle Scholar
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