Is the Identiﬁcation of SNP-miRNA Interactions Supporting the

Prediction of Human Lymphocyte Transcriptional Radiation Responses?

Marzena Dolbniak

, Joanna Zyla

, Sylwia Kabacik

, Grainne Manning

, Christophe Badie

Ghazi Alsbeih

and Joanna Polanska

Institute of Automatic Control, Faculty of Automatic Control, Electronic and Computer Science,

Silesian University of Technology, Akademicka 16, Gliwice, Poland

Cancer Genetics and Cytogenetics Group, Biological Effects Department,

Centre for Radiation, Chemical and Environmental Hazards, Public Health England, Didcot, OX11 ORQ, U.K.

Radiation Biology Section, Biomedical Physics Dept., King Faisal Specialist Hospital & Research Centre,

Riyadh 11211, Kingdom of Saudi Arabia

Keywords:

GWAS, miRNA, Single Nucleotide Polymorphism, Radiosensitivity, Gene Expression, Radiation.

Abstract:

Genome-Wide Association Studies (GWAS) are of great importance in identifying the genetic variants asso-

ciated with traits/diseases. Due to the high number of candidate SNPs some ﬁltering techniques are necessary

to be applied. The aim of the study was to develop the comprehensive approach allowing for detailed analysis

of both SNP-gene and SNP-miRNA-gene relations. We elaborated and optimized the novel signal analysis

pipeline improving signiﬁcantly the results of the analysis on genotype-phenotype interplay. Direct links be-

tween genotype results and gene expression levels were enriched by detailed analysis of SNP-miRNA-gene

interactions at both mature miRNA structure/seed region and target binding site level. The proposed technique

was applied to the data on lymphocyte radiation response and increased by almost 100% number of potential

functional SNPs.

1 INTRODUCTION

Genome-Wide Association Studies (GWAS) are the

most popular kind of research to identify the ge-

netic variants associated with traits/diseases. Meth-

ods to identify the candidate single nucleotide poly-

morphisms (SNPs) depend on the study design and

different statistical approaches and have been widely

discussed in (Bush and Moore, 2012; Evangelou and

Ioannidis, 2013). Filtering the obtained set of can-

didate SNPs by applying the biological information,

such as the potential effects of mutation in gene cod-

ing regions, modiﬁcations of gene related signal path-

ways or gene ontology terms, or the detailed analysis

of microRNA-SNP interactions can reduce the num-

ber of false candidate SNPs, and become a separate

research ﬁeld in GWAS analysis. Over the years, a

lot of algorithms were proposed to support such func-

tional analysis in (Patnala et al., 2013; Wang et al.,

2010), but the most challenging issue still belongs to

the prediction of microRNA-SNP interactions, as the

understanding of microRNAs function continue to in-

crease.

MicroRNAs (miRNAs) are small non-coding

RNAs, which regulate gene expression. Single nu-

cleotide polymorphisms may be functional with re-

spect to miRNAs biogenesis or the speciﬁc roles of

mature miRNA (Dong et al., 2013). From the miR-

NAs biogenesis pathway, SNPs can modify the pri-

mary miRNA (pre-miRNA) or mature miRNA struc-

tures. While considering the regulatory function of

miRNA, speciﬁcally relevant target site recognition

of the seed region of miRNA (6-8 nucleotides at

the 5’ end of the miRNA) is of great importance.

Roughly, single nucleotide polymorphisms can im-

pact the functionality of miRNA by modifying its pri-

mary target binding sites or by creating new binding

sites. As a consequence gene disregulation may lead

to phenotype changes and eventually prove to be crit-

ical for the susceptibility to cancer as well as other

diseases (Slaby et al., 2012; Vitale et al., 2011). Al-

though some of the published works include miRNA-

SNP interaction analysis, most of them focus on SNPs

in primary binding site only (Deveci et al., 2014).

Radiation sensitivity (radiosensitivity), which is

the relative susceptibility of cells, tissues, organs or

243

Dolbniak M., Zyla J., Kabacik S., Manning G., Badie C., Alsbeih G. and Polanska J..

Is the Identiﬁcation of SNP-miRNA Interactions Supporting the Prediction of Human Lymphocyte Transcriptional Radiation Responses?.

DOI: 10.5220/0005286102430250

In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS-2015), pages 243-250

ISBN: 978-989-758-070-3

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

organisms to the harmful effect of radiation, can be

inﬂuenced by many factors amongst which are epi-

genetic modiﬁcations (Ma et al., 2010; Lahtz and

Pfeifer, 2011), and miRNA regulation (Zhao et al.,

2012). Among the group of radiation-responsive

genes, BBC3 (BCL2 binding component 3) is of great

importance, since it encodes a protein named PUMA

(p53 upregulated modulator of apoptosis) which is in-

volved in p53-dependent and -independent apoptosis

induced by a variety of signals amongst which there

is ionizing radiation (Yu and Zhang, 2005). This gene

is known as a reliable biomarker of radiation expo-

sure (Budworth et al., 2012). It is a good candi-

date to investigate the potential role of SNP-gene and

SNP-miRNA interactions in radiosensitivity and on

the long term, relevant for a better understanding of

inter-individual radiation responsiveness supposed to

be linked at least partially to apoptosis mechanisms.

This could lead to radiotherapy regime improvements

personalized cancer treatments.

In this study we perform a novel comprehen-

sive functional analysis taking into consideration both

SNP-gene and SNP-miRNA interactions. We demon-

strate that this type of approach can potentially im-

prove the discovery of candidate process relevant

SNPs compared to standard SNP-gene based methods

only.

2 MATERIALS AND METHODS

2.1 Materials

The group under investigation is composed of 44 un-

related Caucasian individuals (unR), with two types

of data collected.

The ﬁrst dataset includes qPCR measurements for

BBC3 gene, taken in two conditions: 1) in normal

conditions - no irradiation, and 2) just after the irradi-

ation with a single dose of 2Gy. The irradiation was

performed at room temperature with an A.G.O. HS

X-ray system (Aldermaston, Reading, UK) (output 13

mA, 250 kV peak, 0.5 Gy/min for doses 0.5 4 Gy and

0.2 mA 4.9 mGy/min for doses up to 100 mGy). The

T-lymphocyte cultures were used and prepared using

the method described previously (O’Donovan et al.,

1995; Finnon et al., 2008).

The second dataset includes results on genotyp-

ing 567,096 polymorphisms (SNP) by Axiom GW

Human hg36.1 arrays. The BBC3 gene expressions

obtained at both experimental conditions were previ-

ously published (Kabacik et al., 2011a; Kabacik et al.,

2011b; Manning et al., 2013).

2.2 Selection of Polymorphisms

At the ﬁrst step, the quality control for both qPCR

and microarray experiments was performed. Dur-

ing the next step, the genotype-phenotype interac-

tions were modelled per every SNP following the pro-

cedure previously presented in (Zyla et al., 2014).

The genotype-phenotype models were constructed for

both BBC3 expression level in normal condition (no

irradiation, 0Gy) and for standardized fold change

(FCH) signal. The ﬁnal two sets of candidate ra-

diosensitivity related SNPs were deﬁned as follows:

1) all SNPs signiﬁcantly (p-value <0.05) related to

the BBC3 gene expression fold change (FCH) and

not signiﬁcantly related to the BBC3 gene expression

level in normal condition (0Gy) - named SET 1, and

2) all SNPs signiﬁcantly related to BBC3 gene ex-

pression fold change (FCH) and signiﬁcantly related

to the BBC3 gene expression level in normal condi-

tion (0Gy) - SET 2 (Figure 1).

Figure 1: The deﬁnition of the ﬁnal two sets of candidate

radiosensitivity related SNPs: 1) SET 1 - all SNPs signiﬁ-

cantly related to the expression fold change (FCH) and not

signiﬁcantly related to the expression in normal condition

(0Gy) - dark grey colour; 2) SET 2 - all SNPs signiﬁcantly

related to the expression fold change (FCH) and signiﬁ-

cantly related to the expression in normal condition (0Gy) -

light grey colour.

2.3 Functional Analysis

2.3.1 SNP-gene Interactions

The obtained two sets of candidate radiosensitivity

related SNPs were limited to the SNPs occurring in

genes. The information on SNP location in genome

and its transcriptomic assignment was collected us-

ing SNPLab software (GRCh38). Each SNP located

in exon and having missense activity (nonsynony-

mous SNP - nsSNPs) was further analysed to pre-

dict its impact on cell functioning. Polymorphisms

of this type lead to a change of the amino acid in the

protein sequence. To assess the impact of nsSNPs

on the organism the PredictSNP software was used

(Bendl et al., 2014), which integrates the results from

six the most popular algorithms (MAPP, SIFT SNAP,

BIOINFORMATICS2015-InternationalConferenceonBioinformaticsModels,MethodsandAlgorithms

244

PolyPhen1, PolyPhen2, and PhD-SNP). Additionally,

each of the genes modiﬁed by candidate SNPs was in-

vestigated toward the overrepresentation of Gene On-

tology (GO) terms (Szkiba et al., 2014), and KEGG

pathways (Beissbarth and Speed, 2004).

2.3.2 SNP-miRNA interactions - standard

approach

Three the most popular bioinformatical systems

which analyse miRNA and SNP interactions are:

1) SNPinfo (Xu and Taylor, 2009), 2) PolymiRTS

Database (Bhattacharya et al., 2014), and 3) miRSNP

(Liu et al., 2012). All of the above focus mainly

on SNP and miRNA binding site interactions, in

particular SNPinfo focuses on polymorphisms in 3’

UTR region of mRNA and uses miRanda algorithm

(Grifﬁths-Jones et al., 2008) for target binding site

prediction, while PolymiRTS Database searches for

SNPs modifying seed regions of miRNA and uses

TargetScan algorithm (Lewis et al., 2005) and experi-

mental data (Dweep et al., 2014; Hsu et al., 2014; Ver-

goulis et al., 2012) for target binding site predictions.

The last of the mentioned algorithms, miRSNP, as the

only one identiﬁes polymorphisms in pre-miRNA se-

quences and uses miRanda algorithm for identiﬁca-

tion of SNPs modifying the binding sites.

2.3.3 Comprehensive SNP-miRNA Analysis

Procedure

The detailed up-to-date analysis of SNP interaction on

mature miRNA or miRNA’s seed region done together

with tracking of binding site modiﬁcations seem to be

crucial for our study. Since none of the above sys-

tems allows for complete analysis of SNPs and mature

miRNA structure interactions, we have developed a

software performing the analyses. Using miRBase

v21 we found chromosomal coordinates (GRCh38) of

mature miRNAs and miRNAs seed regions and com-

pared them with coordinates of analysed SNPs from

previously found SET 1 or SET 2. Combining that

set with miRNAs having binding sites modiﬁed by

SNPs from the same SET 1 or SET 2 gives the com-

plete set of miRNAs under investigation allowing for

the detection of candidate SNPs responsible for mod-

iﬁcation of miRNA gene expression regulation pro-

cesses. The next step required the deﬁnition of bind-

ing sites being targets for chosen miRNAs, and it was

done at two levels: experimentally validated sites and

in silico predicted sites only. We used miRTarBase

(Hsu et al., 2014) and miRWalk (Dweep et al., 2014)

to ﬁnd experimentally validated targets and for every

miRNA which does not have validated target genes

we used TargetScan and DIANA-microT-CDS predic-

tion algorithms. As it has become a common practice

for researches to look at predictions produces by sev-

eral miRNA-target prediction programs, we focused

on intersection of results obtained from this two algo-

rithms. The group of genes being targets for chosen

miRNAs was investigated toward the overrepresenta-

tion of Gene Ontology (GO) terms and KEGG path-

ways (Beissbarth and Speed, 2004). The data ﬂow

pipeline for whole procedure is presented in Figure 2.

Figure 2: Scheme of data ﬂow in SNP-miRNA interaction

identiﬁcation.

3 RESULTS AND DISCUSSION

Quality control revealed that one of the SNPs was

missing in 91% cases and it was removed from fur-

ther analysis. None outliers were detected in qPCRs.

The total of 472,712 SNPs was considered during

the BBC3 gene expression interaction modelling due

to the lack of diveristy in the analysed samples for

94,383 SNPs. Table 1 includes the number of SNPs

representing three types of modelled interactions be-

tween BBC3 gene expression level and genotyped

polymorphisms (genotype, dominant and recessive).

For both endpoints (0Gy and FCH) number of SNPs

with particular interaction model being minimum p-

value was given in the row named Best model chosen,

while the next row gives the number of SNPs signif-

icant at α=0.05, split by SNP-BBC3 gene interaction

model type.

While analysing the expression of BBC3 gene,

the majority of SNPs represent dominant or recessive

model of interaction with FDR level around 50%. All

the polymorphisms with minimal p-value being less

than 0.05 were considered as the candidate SNPs re-

lated to the radiosensitivity phenomena. After apply-

ing the inclusion criteria deﬁned for the ﬁnal two sets

IstheIdentificationofSNP-miRNAInteractionsSupportingthePredictionofHumanLymphocyteTranscriptional

RadiationResponses?

245

Table 1: The results of the model selection for BBC3 at both

endpoints - 0Gy and FCH.

G D R TOTAL

Total 183,965 309,155 348,188 841,308

0Gy

p<0.05 9,614 37,672 42,559 89,845

FDR [%] 95.68 41.03 40.91 46.81

FCH

p<0.05 8,833 31,084 34,440 74,357

FDR [%] 100 49.73 50.55 56.57

0Gy

Best model 2,462 215,268 254,930 472,712

p<0.05 1,550 35,073 39,865 76,488

FCH

Best model 2,670 215,590 254,452 472,712

p<0.05 1,525 29,034 32,355 62,914

G - Genotype model; D - Dominant model; R - Recessive model.

of candidate radiosensitivity related SNPs, SET 1 in-

cluded 40,953 SNPs, while SET 2 consists of 21,961

SNPs.

3.1 Functional Analysis

3.1.1 SNP-gene Interactions

Table 2 presents detailed information about function-

ality of signiﬁcant SNPs at two investigated end-

points.

Table 2: Transcriptomic location of candidate SNPs SET 1

and SET 2.

No. of SNPs in particular transcriptomic location

SET 1 [%] SET 2 [%]

TOTAL 40,953 100 21,961 100

Total functional 17,096 41.8 9,169 41.8

intron 15,413 37.6 8,271 37.7

exon

synSNP 105 0.3 69 0.3

nsSNP 244 0.6 121 0.6

UTR3’ 418 1.0 223 1.0

UTR5’ 67 0.2 29 0.1

nearGene3’ * 83 0.2 34 0.2

nearGene5’ ** 287 0.7 171 0.8

splice3’ 1 0.01 0 0.0

splice5’ 5 0.01 1 0.01

frameshift 1 0.01 0 0.0

STOP codon loss 1 0.01 0 0.0

ncRNA 94 0.2 53 0.2

cds-reference 377 0.9 197 0.9

* nearGene3’ - within 3’ 0.5kb to a gene;

** nearGene5’ - within 5’ 2kb to a gene.

Initially, the impact of nonsynonymousSNP

(nsSNP) was investigated by PredictSNP software

and the results are presented in table 3. For all dele-

terious SNPs literature study was performed and for

SET 1 three of them occur in genes highly relevant

and afﬁliated to cancer processes: AMACR (Jianq

et al., 2013), SERPINB5 (Kapoor, 2014), ABCC11

(Yamada et al., 2013): In SET 2 only one gene is

highly relevant to cancer processes: TLR6 (Miedema

et al., 2012).

Table 3: The percentage of deleterious nsSNPs in SET 1

and SET 2 candidate SNPs.

nsSNP predictions

SET 1 SET 2

Total 244 122

Deleterious 27 20

[%] 11.07 16.39

Additionally, both groups of genes with sig-

niﬁcant functional SNP-gene interactions, equal to

5,450 in case of SET 1 and 3,505 for SET

2, were investigated on the overrepresentation of

KEGG pathways and the summary of that analy-

sis presents table 4. In general, 45 KEGG path-

ways are overrepresented either in genes related to

SET 1 or SET 2. Twenty two of them (48.89%)

are signiﬁcantly overrepresented in both gene sets.

Among 32 signiﬁcantly overrepresented KEGG path-

ways for genes disregulated by candidate func-

tional SNPs from SET 2, the highest odds ratio

was noticed for ko00604 (Glycosphingolipid biosyn-

thesis, OR=4.51, p-value=0.010638) and ko00532

(Glycosaminoglycan biosynthesis, OR=3.68, p-

value=0.012608) which is consistent with the latest

literature based reports on the strong relation between

these two processes and irradiation or cancerogene-

sis (Aureli et al., 2014; Hirshoren et al., 2014). One

of the most signiﬁcant overrepresented pathways was

ko04360 (Axon guidance, OR=2.97, p-value=3.32E-

07), process strongly related to local invasion and

metastatic spread of the tumour (Ochi et al., 2002).

All these three pathways were also signiﬁcantly over-

represented among genes modiﬁed by SNPs from

SET 1 (p-values equal to 0.027915, 0.003554, and

2.75E-07 for ko00604, ko00532, and ko04360 respec-

tively). While looking at the signal pathways differen-

tially represented in both gene sets, ko04810 (Regula-

tion of actin cytoskeleton), ko04520 (Adherens junc-

tion), and ko05200 (Pathways in cancer) are overrep-

resented in SET 1 - related to the response to irradi-

ation with no relation to 0Gy, and are not enriched

in SET 2, which includes genes related to both to

background (0Gy response) and response to irradia-

tion level (FCH) (Bansal et al., 2014). The oppo-

site analysis - looking for pathways overrepresented

in SET 2 and not enriched in SET 1 brings ko04540

(Gap junction) and ko04210 (Apoptosis) known as re-

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246

lated to radioadaptive response (Nenoi et al., 2014).

The detailed analysis of pathways overrepresented

in both SET 1 and SET 2, three signal pathways,

highly relevant to cancerogenesis, are overrepresen-

taed in both groups of genes - SET 1 and SET

2. Calcium signalling pathway (ko04020) plays the

main role in cell signalling and is thought, for exam-

ple, to have an impact for preventing metastases in

breast cancer (Davis et al., 2014). MAPK signalling

(ko04010) is the main path responsible for cell com-

munication and reaction for stress. Many types of

cancers have mutations in genes in MAPK pathway

(Dhillon et al., 2007). The third one, focal adhesion

(ko04510) has recently been identiﬁed as key deter-

minat of cancer cell resistance to radio- and chemo-

therapy (Eke and Cordes, 2014).

Table 4: Details of KEGG pathways overrepresentation

analysis among genes modiﬁed by SET 1 and SET 2 candi-

date nsSNPs.

KEGG pathways

SET 1 SET 2

No. of SNPs 17,096 21,961

No. of genes 5,450 3,505

No. of analysed KEGG pathways 232 227

No. of overrepresented

KEGG pathways 35 32

FDR [%] 33.14 35.47

3.2 SNP-miRNA-gene Interactions

Summary of the results obtained presents table 5.

Novel comprehensive approach allows to identify

both types of interactions (polymorphism inside ma-

ture miRNAs and in target sites) which cannot be ob-

tained with the use of standard data analysis systems,

and which signiﬁcantly increases the number of ob-

served interactions.

Table 5: Summary of the comprehensive SNP-miRNA in-

teraction analysis for two SNP sets SET 1 and SET 2.

No. of miRNA and polymorphisms interactions

SET 1 SET 2

SNPs in miRNA structure

mature miRNA 13 2

seed region 4 2

SNPs in target site of miRNA

CLASH 9 8

experimentally validated 2 2

in silico predicted 414 218

Several involved in cancer progression miRNAs,

with polymorphisms in mature structure were found.

The most relevant are: hsa-let-7a-3p with rs12326928

(p-value=0.0101; strong impact on breast cancer)

(Yu et al., 2007)), hsa-miR-519c-5p with rs1816087

(p-value=0.0101; regulate human breast cancer

resistance protein) (Li et al., 2011)), hsa-miR-512-3p

with rs4145874 (p-value=0.0119; regulation of genes

associated with cancer) (Chen et al., 2010)), and

hsa-miR-22-5p with rs9828426 (p-value=0.0482;

regulation of genes associated with breast cancer

(Patel et al., 2011)).

Exemplary polymorphism modifying mature

miRNA among those found for both SET 1 and SET

2 candidate SNP sets is rs2974617 (p-value=0.0152).

It modiﬁes the mature structure of hsa-miR-4796-5p

and is located in TRIM36 gene. This gene and its

products are members of tripartite motif (TRIM)

family. Most of the genes from TRIM family are

observed being differentially expressed in many

types of cancers. The TRIM36 has a signiﬁcant role

in chromosome segregation and cell cycle regulation

(Hatakeyamai, 2011). hsa-miR-4796-5p is not well

described in literature. However, we found 40

potential target genes. This group was investigated

by overrepresentation analysis of GO and KEGG

pathways. The most important gene ontology term is

GO:0097237 (cellular response to toxic substance;

p-value=0.038) and already mentioned ko04020

(calcium signalling pathway; p-value=0.015).

The parallel analysis was performed to ﬁnd

signiﬁcant polymorphisms located in miRNAs target

sites. While looking at the results obtained with the

use of CLASH database, SET 1 contains, for exam-

ple, polymorphism rs11556080 (p-value=0.0270),

which changes the regulation of CD99 gene. CD99

gene is located on X chromosome and unlikely to

most of genes, it does not undergo X inactivation.

CD99 is found in tumour cell of Ewing’s sarcoma.

Its knockdown reduces the tumour progression

(Rocchi et al., 2010). The recent studies suggest

CD99 as a biological marker for non-small lung

cancer (Edlund et al., 2012). This dysregulation can

be connected with the distribution of target site for

hsa-miR-877-3p. The results of DIANA software

(mirPath, 0.8 MicroT threshold) suggest that it can

interact with genes connected with cancer (ko05200

pathways in cancer; p-value=0.006) and ko04115

(p53 signaling pathway; p-value=0.017) - ﬁgure 3.

The SET 2 contains, among the others, rs989902

and rs184967 located respectively in PTPN13 and

MSH3 genes. The ﬁrst polymorphism can disturb

target site for hsa-miR-186-5p (role in human colon

carcinoma cells (Chen et al., 2013)), the second

hsa-miR-92b-3p (connected with brain cancer and

metastasis (Nass et al., 2009)). Both genes are highly

IstheIdentificationofSNP-miRNAInteractionsSupportingthePredictionofHumanLymphocyteTranscriptional

RadiationResponses?

247

relevant for investigated trait. PTPN13 plays the

role in the process of metastasis in lung cancer (Han

et al., 2013), where MSH3 is one of the main genes

responsible for miss-match in the repair process.

Also, gene MSH3 was found as a candidate to

describe the radiosensitivity phenomena (Mangoni

et al., 2011). Using PolymiRTS Database 3.0 we

obtained the information on relations of signiﬁcant

polymorphisms to different diseases (presented in

table 6).

Figure 3: Pathway interaction between BBC3 and dysregu-

lated gene CD99 based on genemania.org.

Table 6: Number of diseases and traits associated with poly-

morphisms from SET 1 and SET 2.

SET 1 SET 2

No. of diseases/traits interactions 283 127

No. of unique diseases/traits 144 77

No. of cancer disease interaction 21 5

The comparison between novel comprehensive

approach and SNPinfo based one reveals, that ap-

plying the detailed analysis of SNP-miRNA interac-

tions combined with the integrative PredictSNP algo-

rithm signiﬁcantly increases the number of candidate

functionally validated SNPs by 222 SNPs for SET 1

(92.88%, from 239 to 461) and by 150 SNPs for SET

2 (96.00%, from 125 to 245) - table 7.

Table 7: Summary of novel functional analysis for SET 1

and SET 2.

SET 1 SET 2

TOTAL 461 245

Deleterious nsSNPs 27 20

SNPs in target site 421 223

SNPs in miRNA 13 2

4 CONCLUSIONS

We developed the novel comprehensive technique

improving signiﬁcantly the results of the analysis

on genotype-phenotype interactions. Direct links

between genotype results and gene expression lev-

els were enriched by detailed analysis of SNP-

miRNA-gene interactions at both mature miRNA

structure/seed region and target binding site level.

The presented analysis can ﬁlter out non-functional

SNPs from extremely large number of relevant poly-

morphisms resulting of GWAS analysis.

The proposed technique was applied to the prob-

lem of searching for genetic signature of radiosen-

sitivity. Eight polymorphisms highly relevant to the

process of description of the radiosensitivity phenom-

ena were obtained, majority of them were indirectly

validated during the literature study

ACKNOWLEDGEMENTS

The authors would like to thank Dr. S. Majid, Ms.

N. Al-Harbi, Ms. S. Al-Qahtani for running the Ax-

iom Affymetrix platform, Paul Finnon for cell cul-

ture, Anna Krawczyk, the author of SNPLab soft-

ware, for her help in data collection. The work was

ﬁnancially supported by NCN grant HARMONIA

4 DEC-2013/08/M/ST6/00924 (JP), the National In-

stitute for Health Research Centre for Research in

Public Health Protection at PHE (CB), the National

Science, Technology & Innovation Plan (NSTIP)

Project 11-BIO1429-20 (KFSHRC RAC# 2120 003)

(GA), SUT- BKM/524/ RAU1/2014/t.6 (MD), SUT-

BKM/524/ RAU1/2014/t.16 (JZ). Additionally, MD

and JZ are holders of scholarship DoktoRis- Schol-

arship program for Innovative Silesia. Calculations

were carried out using infrastructure of GeCONiI

(POIG.02.03.01-24-099/13).

DECLARATION OF INTEREST

The authors alone are responsible for the content and

writing of the paper.

REFERENCES

Aureli, M., Murdica, V., Loberto, N., Samarani, M.,

Prinetti, A., Bassi, R., and Sonnino, S. (2014). Explor-

ing the link between ceramide and ionizing radiation.

Glycoconj J., 31(6-7):449–459.

BIOINFORMATICS2015-InternationalConferenceonBioinformaticsModels,MethodsandAlgorithms

248

Bansal, N., Mims, J., Kuremsky, J., Olex, A., Zhao, W., Yin,

L., Wani, R., Qian, J., Center, B., Marrs, G., Poros-

nicu, M., Fetrow, J., Tsang, A., and Furdui, C. (2014).

Broad phenotypic changes associated with gain of ra-

diation resistance in head and neck squamous cell can-

cer. Antioxid Redox Signal., 21(2):221–236.

Beissbarth, T. and Speed, T. (2004). GOstat: ﬁnd statisti-

cally overrepresented gene ontologies within a group

of genes. Bioinformatics., 197(1):1464–1465.

Bendl, J., Stourac, J., Salanda, O., Pavelka, A., Wieben, E.,

Zendulka, J., Brezovsky, J., and Dombrsky, J. (2014).

PredictSNP: Robust and accurate consensus classi-

ﬁer for prediction of disease-related mutations. PLoS

Comput Biol., 10(1):e1003440.

Bhattacharya, A., Ziebarth, J., and Cui, Y. (2014).

PolymiRTS database 3.0: linking polymorphisms in

microRNAs and their target sites with human dis-

eases and biological pathways. Nucleic Acids Res.,

42(D1):D86–D91.

Budworth, H., Snijders, A., Marchetti, F., Mannion, B.,

Bhatnagar, S., Kwoh, E., Tan, Y., Wang, S., Blakely,

W., Coleman, M., Peterson, L., and Wyrobek, A.

(2012). Dna repair and cell cycle biomarkers of ra-

diation exposure and inﬂammation stress in human

blood. PLoS One, 7(11):e48619.

Bush, W. and Moore, J. (2012). Chapter 11: Genome-wide

association studies. PLoS Comput Biol., 8(12).

Chen, F., Zhou, C., Lu, Y., Yuan, L., Peng, F., Zheng, L.,

and Li, X. (2013). Expression of hsa-mir-186 and its

role in human colon carcinoma cells. Nan Fang Yi Ke

Da Xue Xue Bao, 33(5):654–60.

Chen, F., Zhu, H., Zhou, L., Wu, S., Wang, J., and Chen, Z.

(2010). Inhibition of c-ﬂip expression by mir-512-3p

contributes to taxol-induced apoptosis in hepatocellu-

lar carcinoma cells. Oncology Reports, 23(5):1457–

1462.

Davis, F., Azimi, I., Faville, R., Peters, A., Jalink, K.,

Putney, J., Goodhill, G., Thompson, E., Roberts-

Thomson, S., and Monteith, G. (2014). Induction

of epithelialmesenchymal transition (EMT) in breast

cancer cells is calcium signal dependent. Oncogene.,

33:2307–2316.

Deveci, M., Catalyrek, U., Svoboda, M., and Toland,

A. (2014). mrSNP: Software to detect SNP ef-

fects on microRNA binding. BMC Bioinformatics.,

15):doi:10.1186/1471–2105–15–73.

Dhillon, A., Hagan, S., Rath, O., and Kolc, W. (2007).

MAP kinase signalling pathways in cancer. Onco-

gene., 26:3279–3290.

Dong, H., Lei, J., Ding, L., Wen, Y., Ju, H., and Zhang, X.

(2013). MicroRNA: Function, detection, and bioanal-

ysis. Chem. Rev., 113(8):6207–6233.

Dweep, H., Sticht, C., Pandey, P., and Gretz, N. (2014).

miRWalk - database: prediction of possible mirna

binding sites by ’walking’ the genes of 3 genomes.

Nucleic Acids Research, 42:D78D85.

Edlund, K., Lindskog, C., Saito, A., Berglund, A., Pontn,

F., Gransson-Kultima, H., Isaksson, A., Jirstrm, K.,

Planck, M., Johansson, L., Lambe, M., Holmberg,

L., Nyberg, F., Ekman, S., Bergqvist, M., Lan-

delius, P., Lamberg, K., Botling, J., Ostman, A., and

Micke, P. (2012). CD99 is a novel prognostic stromal

marker in non-small cell lung cancer. Int J Cancer,

131(10):2264–2273.

Eke, I. and Cordes, N. (2014). Focal adhesion signaling and

therapy resistance in cancer. Semin Cancer Biol., pii:

S1044-579X(14)00098-4.

Evangelou, E. and Ioannidis, J. (2013). Meta-analysis meth-

ods for genome-wide association studies and beyond.

Nat. Rev. Genet., 14:379389.

Finnon, P., Robertson, N., Dziwura, D., Raffy, C., Zhang,

W., Ainsbury, L., Kaprio, J., Badie, C., and Bouf-

ﬂer, S. (2008). Evidence for signiﬁcant heritability

of apoptotic and cell cycle responses to ionising radi-

ation. Hum Genet., 123(5):485–493.

Grifﬁths-Jones, S., Saini, H., van Dongen, S., and Enright,

A. (2008). mirbase tools for microrna genomics. Nu-

cleic Acids, 36:154–158.

Han, X., Xue, L., Zhou, L., Gong, L., Zhu, S., Yao, L.,

Wang, S., Lan, M., Li, Y., and Zhang, W. (2013). The

role of ptpn13 in invasion and metastasis of lung squa-

mous cell carcinoma. Exp Mol Pathol., 95(3):270–58.

Hatakeyamai, S. (2011). TRIM proteins and cancer. Nat

Rev Cancer, 11(11):792–804.

Hirshoren, N., Bulvik, R., Neuman, T., Rubinstein, A.,

Meirovitz, A., and Elkin, M. (2014). Induction of hep-

aranase by HPV E6 oncogene in head and neck squa-

mous cell carcinoma. J Cell Mol Med., 18(1):181–

186.

Hsu, S., Tseng, Y., Shrestha, S., Lin, Y., Khaleel, A., Chou,

C., Chu, C., Huang, H., Lin, C., Ho, S., Jian, T.,

Lin, F., Chang, T., Weng, S., Liao, K., Liao, I., Liu,

C., and Huang, H. (2014). MiRTarBase update 2014:

an information resource for experimentally validated

miRNA-target interactions. Nucleic Acids Research,

42:D78D85.

Jianq, N., Zhu, S., Chen, J., Niu, Y., and Zhou, L. (2013).

A-methylacyl-CoA racemase (AMACR) and prostate-

cancer risk: a meta-analysis of 4,385 participants.

PLoS One., 8(10):e74386.

Kabacik, S., Mackay, A., Tamber, N., Manning, G., Finnon,

P., Paillier, F., Ashworth, A., Boufﬂer, S., and Badie,

C. (2011a). Gene expression following ionising radi-

ation: identiﬁcation of biomarkers for dose estimation

and prediction of individual response. Int J Radiat

Biol., 87(2):115–129.

Kabacik, S., Ortega-Molina, A., Efayan, A., Finnon, P.,

Boufﬂer, S., Serrano, M., and Badie, C. (2011b). A

minimally invasive assay for individual assessment

of the atm/chek2/p53 pathway activity. Cell Cycle.,

10(7):1152–1161.

Kapoor, S. (2014). Maspin and its evolving role in tumor

progression in systemic malignancies. Breast Cancer.,

21(2):249.

Lahtz, C. and Pfeifer, G. (2011). Epigenetic changes of dna

repair genes in cancer. J Mol Cell Biol., 3(1):51–55.

Lewis, B., Burge, C., and Bartel, D. (2005). Conserved

seed pairing, often ﬂanked by adenosines, indicates

IstheIdentificationofSNP-miRNAInteractionsSupportingthePredictionofHumanLymphocyteTranscriptional

RadiationResponses?

249

that thousands of human genes are microrna targets.

Cell, 120:15–20.

Li, X., Pan, Y., Seigel, G., Hu, Z., Huang, M., and

Yu, A. (2011). Breast cancer resistance protein

BCRP/ABCG2 regulatory microRNAs (hsa-mir-328,

-519c and -520h) and their differential expression in

stem-like ABCG2+ cancer cells. Biochemical Phar-

macology, 81(6):783 – 792.

Liu, C., Zhang, F., Li, T., Lu, M., Wang, L., Yue, W.,

and Zhang, D. (2012). MirSNP, a database of poly-

morphisms altering miRNA target sites, identiﬁes

miRNA-related SNPs in GWAS SNPSs and eQTLs.

BMC Genomics., 13:661.

Ma, S., Liu, X., Jiao, B., Yang, Y., and Liu, X. (2010). Low-

dose radiation-induced responses: focusing on epige-

netic regulation. Int J Radiat Biol., 86(7):517–528.

Mangoni, M., Bisanzi, S., Carozzi, F., Sani, C., Biti, G.,

Livi, L., Barletta, E., Costantini, A., and Gorini,

G. (2011). Association between genetic polymor-

phisms in the xrcc1, xrcc3, xpd, gstm1, gstt1, msh2,

mlh1, msh3, and mgmt genes and radiosensitivity in

breast cancer patients. Int J Radiat Oncol Biol Phys.,

81(1):52–58.

Manning, G., Kabacik, S., Finnon, P., Boufﬂer, S., and

Badie, C. (2013). High and low dose responses of

transcriptional biomarkers in ex vivo x-irradiated hu-

man blood. Int J Radiat Biol., 89(7):511–522.

Miedema, K., Tissing, W., Poele, E. T., Kamps, W., Al-

izadeh, B., Kerkhof, M., de Jongste, J., Smit, H.,

de Pagter, A., Bierings, M., Boezen, H., Postma, D.,

de Bont, E., and Koppelman, G. (2012). Polymor-

phisms in the TLR6 gene associated with the inverse

association between childhood acute lymphoblastic

leukemia and atopic disease. Leukemia., 26(6):1203–

1210.

Nass, D., Rosenwald, S., ans S. Gilad, E. M., Tabibian-

Keissar, H., Schlosberg, A., Kuker, H., Sion-Vardy,

N., Tobar, A., Kharenko, O., Sitbon, E., Yanai, G. L.,

Elyakim, E., Cholakh, H., Gibori, H., Spector, Y.,

Bentwich, Z., Barshack, I., and Rosenfeld, N. (2009).

Mir-92b and mir-9/9* are speciﬁcally expressed in

brain primary tumors and can be used to differentiate

primary from metastatic brain tumors. Brain Pathol-

ogy, 19:375–383.

Nenoi, M., Wang, B., and Vares, G. (2014). In vivo ra-

dioadaptive response: A review of studies relevant to

radiation-induced cancer risk. Hum Exp Toxicol., pii:

0960327114537537.

Ochi, K., Mori, T., Toyama, Y., Nakamura, Y., and

Arakawa, H. (2002). Identiﬁcation of semaphorin3B

as a direct target of p53. Neoplasia., 4(1):82–87.

O’Donovan, M., Freemantle, M., Hull, G., Bell, D., Arlett,

C., and Cole, J. (1995). Extended-term cultures of

human T-lymphocytes: a practical alternative to pri-

mary human lymphocytes for use in genotoxicity test-

ing. Mutagenesis., 10(3):189–201.

Patel, J., Appaiah, H., Burnett, R., Bhat-Nakshatri, P.,

Wang, G., Mehta, R., Badve, S., Thomson, M., Ham-

mond, S., Steeg, P., Liu, Y., and Nakshatri, H. (2011).

Control of EVI-1 oncogene expression in metastatic

breast cancer cells through microRNA mir-22. Onco-

gene, 30(11):1290–1301.

Patnala, R., Clements, J., and Batra, J. (2013). Candi-

date gene association studies: a comprehensive guide

to useful in silico tools. BMC Genet., 14:39:doi:

10.1186/1471–2156–14–39.

Rocchi, A., Manara, M., Sciandra, M., Zambelli, D., Nardi,

F., Nicoletti, G., Garofalo, C., Meschini, S., Astolﬁ,

A., Colombo, M., Lessnick, S., Picci, P., and Scot-

landi, K. (2010). CD99 inhibits neural differentiation

of human ewing sarcoma cells and thereby contributes

to oncogenesis. J Clin Invest, 120(3):668–680.

Slaby, O., Bienertova-Vasku, J., Svoboda, M., and Vyzula,

R. (2012). Genetic polymorphisms and microRNAs:

new direction in molecular epidemiology of solid can-

cer. J Cell Mol Med., 16(1):8–21.

Szkiba, D., Kapun, M., von Haeseler, A., and Gallach,

M. (2014). SNP2GO: functional analysis of genome-

wide association studies. Genetics., 197(1):285–289.

Vergoulis, T., Vlachos, T., Alexiou, P., Georgakilas, G.,

Maragkakis, M., Reczko, M., Gerangelos, S., Koziris,

N., Dalamagas, T., and Hatzigeorgiou, A. (2012). Tar-

base 6.0: Capturing the exponential growth of mirna

targets with experimental support. Nucleic Acids Re-

sources, 40(D1):222–229.

Vitale, A., Tan, H., and Jin, P. (2011). MicroRNAs, SNPSs

and cancer. Nucleic Acids Invest., 2(6):32–38.

Wang, K., Li, M., and Hakonarson, H. (2010). Analysing

biological pathways in genome-wide association stud-

ies. Nat. Rev. Genet., 11:843–854.

Xu, Z. and Taylor, J. (2009). SNPinfo: integrating GWAS

and candidate gene information into functional SNP

selection for genetic association studies. Nucl. Acids

Res., (Web Server issue):W600–5.

Yamada, A., Ishikawa, T., Ota, I., Kimura, M., Shimizu,

D., Tanabe, M., Chishima, T., Sasaki, T., Ichikawa,

Y., Morita, S., Yoshiura, K., Takabe, K., and Endo,

I. (2013). High expression of ATP-binding cassette

transporter ABCC11 in breast tumors is associated

with aggressive subtypes and low disease-free sur-

vival. Breast Cancer Res Treat., 137(3):773–782.

Yu, F., Yao, H., Zhu, P., Zhang, X., Pan, Q., Gong, C.,

Huang, Y., Hu, X., Su, F., Lieberman, J., and Song, E.

(2007). let-7 regulates self renewal and tumorigenicity

of breast cancer cells. Cell, 131(6):1109 – 1123.

Yu, J. and Zhang, L. (2005). The transcriptional targets of

p53 in apoptosis control. Biochem Biophys Res Com-

mun., 3(331):851–858.

Zhao, L., Bode, A., Cao, Y., and Dong, Z. (2012).

Regulatory mechanisms and clinical perspectives of

miRNA in tumor radiosensitivity. Carcinogenesis.,

33(11):2220–2227.

Zyla, J., Badie, C., Alsbeih, G., and Polanska, J. (2014).

Modelling of genetic interactions in GWAS reveals

more complex relations between genotype and phe-

notype. In Proceeding of: Bioinformatics 2014: 5th

International Conference on Bioinformatics Models,

Methods and Algorithms. SCITEPRESS.

BIOINFORMATICS2015-InternationalConferenceonBioinformaticsModels,MethodsandAlgorithms

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