research article gene regulation, modulation, and their...

Hindawi Publishing CorporationAdvances in BioinformaticsVolume 2013 Article ID 360678 11 pageshttpdxdoiorg1011552013360678

Research ArticleGene Regulation Modulation and Their Applications inGene Expression Data Analysis

Mario Flores1 Tzu-Hung Hsiao2 Yu-Chiao Chiu3 Eric Y Chuang3

Yufei Huang1 and Yidong Chen24

1 Department of Electrical and Computer Engineering University of Texas at San Antonio San Antonio TX 78249 USA2Greehey Childrenrsquos Cancer Research Institute University of Texas Health Science Center at San Antonio San Antonio TX 78229 USA3Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan4Department of Epidemiology and Biostatistics University of Texas Health Science Center at San Antonio San AntonioTX 78229 USA

Correspondence should be addressed to Yufei Huang yufeihuangutsaedu and Yidong Chen cheny8uthscsaedu

Received 2 December 2012 Accepted 24 January 2013

Academic Editor Mohamed Nounou

Copyright copy 2013 Mario Flores et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Commonmicroarray and next-generation sequencing data analysis concentrate on tumor subtype classification marker detectionand transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and theirshared functions Genetic regulatory network (GRN) based approaches have been employed in many large studies in order toscrutinize for dysregulation and potential treatment controls In addition to gene regulation and network construction the conceptof the network modulator that has significant systemic impact has been proposed and detection algorithms have been developedin past years Here we provide a unified mathematic description of these methods followed with a brief survey of these modulatoridentification algorithms As an early attempt to extend the concept to new RNA regulation mechanism competitive endogenousRNA (ceRNA) into a modulator framework we provide two applications to illustrate the network construction modulation effectand the preliminary finding from these networks Those methods we surveyed and developed are used to dissect the regulatednetwork under different modulators Not limit to these the concept of ldquomodulationrdquo can adapt to various biological mechanismsto discover the novel gene regulation mechanisms

1 Introduction

With the development of microarray [1] and lately the nextgeneration sequencing techniques [2] transcriptional pro-filing of biological samples such as tumor samples [3ndash5]and samples from other model organisms have been carriedout in order to study sample subtypes at molecular level ortranscriptional regulation during the biological processes [6ndash8] While common data analysis methods employ hierarchi-cal clustering algorithms or pattern classification to explorecorrelated genes and their functions the genetic regulatorynetwork (GRN) approaches were employed to scrutinize fordysregulation between different tumor groups or biologicalprocesses (see reviews [9ndash12])

To construct the network most of research is focused onmethods based on gene expression data derived from high-throughput technologies by using metrics such as Pearsonor Spearman correlation [13] mutual information [14] co-determinationmethod [15 16] Bayesianmethods [17 18] andprobabilistic Boolean networks [19] Recently new transcrip-tional regulation via competitive endogenous RNA (ceRNAs)has been proposed [20 21] introducing additional dimensionin modeling gene regulation This type of regulation requiresthe knowledge of microRNA (miRNA) binding targets [2223] and the hypothesis of RNA regulations via competitionof miRNA binding Common GRN construction tries toconfine regulators to be transcription factor (TF) proteinsa primary transcription programming machine which relies

2 Advances in Bioinformatics

Interact

Regulator Target

119909 119910

(a)

Regulator

119909

119910

119911

119908

of 119910 and 119908

Regulator of 119911

Target of 119910

Target of 119909

Target of 119909

(b)

Figure 1 Regulator-target pair in genetic regulatory network model (a) basic regulator-target pair and (b) regulator-target complex

on sequence-specific binding sites at target genesrsquo promoterregions In contrast ceRNAs mediate gene regulation viacompeting miRNAs binding sites in target 31015840UTR regionwhich exist in gt50 of mRNAs [22 24] In this study wewill extend the current network construction methods byincorporating regulation via ceRNAs

In tumorigenesis gene mutation is the main cause ofthe cancer [25] The mutation may not directly reflect inthe change at the gene expression level however it willdisrupt gene regulation [26ndash28] In Hudson et al theyfound that mutated myostatin and MYL2 showed differentcoexpressionswhen comparing towild-typemyostatin Chunet al also showed that oncogenic KRAS modulates HIF-1120572 and HIF-2120572 target genes and in turn modulates cancermetabolism Stelniec-Klotz et al presented a complex hierar-chicalmodel ofKRASmodulated network followed by doubleperturbation experiments Shen et al [29] showed a temporalchange of GRNs modulated after the estradiol stimulationindicating important role of estrogen in modulating GRNsFunctionally modulation effect of high expression of ESR1was also reported byWilson andDering [30]where they stud-ied previously published microarray data with cells treatedwith hormone receptor agonists and antagonists [31ndash33] Inthis study a comprehensive review of existing algorithms touncover themodulators was provided Given either mutationor protein expression status was unknown under many ofreported studies the problem of how to partition the diversesamples with different conditions such as active or inactiveoncogene status (and perhaps a combination of multiplemutations) and the prediction of a putative modulator ofgene regulation remains a difficult task

By combining gene regulation obtained from coexpres-sion data and ceRNAs we report here an early attempt tounify two systems mathematically while assuming a knownmodulator estrogen receptor (ER) By employing the TCGA[3] breast tumor gene expressions data and their clinical testresult (ER status) we demonstrate the approach of obtainingGRN via ceRNAs and a new presentation of ER modulationeffects By integrating breast cancer data into our uniqueceRNAs discovery website we are uniquely positioned tofurther explore the ceRNA regulation network and further

develop the discovery algorithms in order to detect potentialmodulators of regulatory interactions

2 Models of Gene Regulation and Modulation

21 Regulation of Gene Expression Thecomplex relationshipsamong genes and their products in a cellular system canbe studied using genetic regulatory networks (GRNs) Thenetworksmodel the different states or phenotypes of a cellularsystem In this model the interactions are commonly mod-eled as regulator-target pairs with edges between regulatorand target pair representing their interaction direction asshown in Figure 1(a) In this model a target gene is a genewhose expression can be altered (activated or suppressed)by a regulator gene This definition of a target gene impliesthat any gene can be at some point a target gene or adirect or indirect regulator depending on its position in thegenetic regulatory network The regulator gene is a gene thatcontrols (activates or suppresses) its target genesrsquo expressionThe consequences of these activated (or suppressed) genessometimes are involved in specific biological functions suchas cell proliferation in cancer Examples of regulator-targetpair in biology are common For example a target geneCDCA7 (cell division cycle-associated protein 7) is a c-Myc(regulator) responsive gene and it is part of c-Myc-mediatedtransformation of lymphoblastoid cells Furthermore asshown in Figure 1(b) a regulator gene can also act as a targetgene if there exists an upstream regulator

If the interaction is modeled after Boolean network (BN)model [34] then

119910119894(119905 + 1) = 119891

119894(1199091198951(119905) 119909

119895119896(119905) 119910119894(119905)) (1)

where each regulator 119909119895isin 0 1 is a binary variable as well

as it is target 119910119894 As described by (1) the target 119910

119894at time

119905 + 1 is completely determined by the values of its regulatorsat time 119905 by means of a Boolean function 119891

119894isin 119865 where

119865 is a collection of Boolean functions Thus the Booleannetwork 119866(119881 119865) is defined as a set of nodes (genes) 119881 =1199091 1199092 119909

119899 and a list of functions (edges or interactions)

119865 = 1198911 1198912 119891

119899 Similarly such relationship can be

defined in the framework of Bayesian network where the

Advances in Bioinformatics 3

R

Target gene

TF

119910

(a)

Singaltransductionproteins

ReceptorHormone

(b)

microRNAs

3998400UTR

Target gene 119910

(c)

Figure 2 Three different cases of regulation of gene expression that share the network representation of a regulator target interaction

similar regulators-target relationship as defined in (1) can bemodeled by the distribution

119875 (119910119894(119905 + 1) 119909

1198951(119905) 119909

119895119896(119905) 119910119894(119905))

= 119875 (119910119894(119905 + 1) | Parents (119910

119894(119905 + 1)))

times 119875 (Parents (119910119894(119905 + 1)))

(2)

where Parents(119910119894(119905 + 1)) = 119909

1198951

(119905) 119909119895119896(119905) 119910119894(119905) is the set

of regulators or parents of 119910119894 119875(119910119894(119905 + 1) | Parents (119910

119894(119905 +

1))) is the conditional distribution defining the regulator-target relationship and 119875(Parents(119910

119894(119905 + 1))) models the

prior distribution of regulators Unlike in (1) the target andregulators in (2) are modeled as random variables Despiteof this difference in both (1) and (2) the target is always afunction (or conditional distribution) of the regulator (or par-ents)When the relationship is defined by a Boolean functionas in (1) the conditional distribution in (2) take the formof a binomial distribution (or a multinomial distributionwhen both regulators and target take more than two states)Other distributions such as the Gaussian and Poisson can beintroduced to model more complex relationships than theBoolean The network construction inference and controlhowever are beyond the scope of this paper and we leave thetopics to the literatures [9 35 36]

The interactions among genes and their products ina complex cellular process of gene expression are diversegoverned by the central dogma of molecular biology [37]There are different regulation mechanisms that can actuateduring different stages Figure 2 shows three different casesof regulation of gene expression Figure 2(a) shows the caseof regulation of expression in which a transcription factor(TF) regulates the expression of a protein-coding gene (indark grey) by binding to the promoter region of target gene 119910Figure 2(b) is the case of regulation at the protein level inwhich a ligand protein interacts with a receptor to activaterelay molecules to transduce outside signals directly into cellbehavior Figure 2(c) is the case of regulation at the RNAlevel in which one or more miRNAs regulate target mRNA119910 by translational repression or target transcript degradationvia binding to sequence-specific binding sites (called miRNAresponse elements or MREs) in 31015840UTR region As illustratedin Figure 2(c) the target genesproteins all contain a domainof binding or docking site enabling specific interactions

Modulator

Interact

Regulator Target

119909 119910

119898

Figure 3 Graphical representation of the triplet interaction ofregulator 119909 target 119910 and modulator119898

between regulator-target pairs a common element in net-work structure

22 Modulation of Gene Regulation Different from the con-cept of a coregulator commonly referred in the regulatorybiology a modulator denotes a gene or protein that is capableof altering the endogenous gene expression at one stage ortime In the context of this paper we specifically definea modulator to be a gene that can systemically influencethe interaction of regulator-target pair either to activateor suppress the interaction in the presenceabsence of themodulator One example of modulator is the widely studiedestrogen receptor (ER) in breast cancer studies [38ndash40] theER status determines not only the tumor progression but alsothe chemotherapy treatment outcomes It is well known thatbinding of estrogen to receptor facilitates the ER activitiesto activate or repress gene expression [41] thus effectivelymodulating the GRN Figure 3 illustrates the model of theinteraction between a modulator (119898) and a regulator (119909)target (119910) pair that it modulates

Following the convention used in (1) and (2) the modu-lation interaction in Figure 3 can be modeled by

119910 = F(119898)

(119909) (3)

where 119910 represents target expression 119909 represents the parents(regulators) of target 119910 andF(119898)(sdot) is the regulation function


modulated by119898 WhenF(119898)(sdot) is stochastic the relationshipis modeled by the conditional distribution as

119901 (119910 119909 | 119898) = 119901 (119910 | 119909119898) 119901 (119909 | 119898) (4)

where 119901(119910|119909119898) models the regulator-target relationshipmodulated by 119898 and 119901(119909|119898) defines the prior distributionof regulators (parents) expression modulated by119898 Differentdistribution models can be used to model different mech-anisms for modulation At the biological level there aredifferent mechanisms for modulation of the interaction 119909-119910and currently several algorithms for prediction of the mod-ulators has been developed This survey presents the latestformulations and algorithms for prediction of modulators

3 Survey of Algorithms of Gene Regulationand Modulation Discovery

During the past years many computational tools have beendeveloped for regulation network construction and thendepending on the hypothesis modulator concept can betested and extracted Here we will focus on modulator detec-tion algorithms (MINDy Mimosa GEM and Hermes) Tointroduce gene-gene interaction concept we will also brieflydiscuss algorithms for regulation network construction(ARACNE) and ceRNA identification algorithm (MuTaMe)

31 ARACNE (Algorithm for the Reconstruction of AccurateCellular Networks) ARACNE [14 42] is an algorithm thatextracts transcriptional networks from microarray data byusing an information-theoreticmethod to reduce the indirectinteractions ARACNE assumes that it is sufficient to estimate2-way marginal distributions when sample size119872 gt 100 ingenomics problems such that

119901 (119909119894) =

1

119911

119890minus[sum119873

119894=1120601119894(119909119894)+sum119873

119894119895120601119894119895(119909119894119910119895)] (5)

Or a candidate interaction can be identified using estima-tion of mutual information MI of genes 119909 and 119910 MI(119909 119910) =MI119909119910 where MI

119909119910= 1 if genes 119909 and 119910 are identical and

MI119909119910

is zero if 119901(119909 119910) = 119901(119909)119901(119910) or 119909 and 119910 are sta-tistically independent Specifically the estimation of mutualinformation of gene expressions 119909 and 119910 of regulator andtarget genes is done by using the Gaussian kernel estimatorTheARACNE takes additional two steps to clean the network(1) removing MI if its 119875 value is less than that derived fromtwo independent genes via random permutation and (2) dataprocessing inequality (DPI) The algorithm further assumesthat for a triplet gene (119892

119909 119892119910 119892119911) where 119892

119909regulates 119892

119911

through 119892119910 then

MI119909119911lt min (MI

119909119910MI119910119911) if 119909 997888rarr 119910 997888rarr 119911

with no alternative path(6)

where rarr represents regulation relationship In other wordsthe lowest mutual information MI

119909119911is from an indirect

interaction and thus shall be removed from the GRN by

ARACNE in the DPI step A similar algorithm was proposed[43] to utilize conditional mutual information to exploremore than 2 regulators

32 MINDy (Modulator Inference by Network Dynamics)Similar toARACNEMINDy is also an information-theoreticalgorithm [44] However MINDy aims to identify poten-tial transcription factor-(TF-target) gene pairs that can bemodulated by a candidate modulator MINDy assumes thatthe expressions of the modulated TF-target pairs are ofdifferent correlations under different expression state of themodulator For simplicity and computational considerationMINDy considers only two modulator expression states thatis up- (119898 = 1) or down-expression (119898 = 0) Then ittests if the expression correlations of potential TF-target pairsare significantly different for modulator up-expression versusdown-expression The modulator dependent correlation isassessed by the conditional mutual information (CMI) or119868(119909 119910 | 119898 = 0) and 119868(119909 119910 | 119898 = 1) Similar to ARACNEthe CMI is calculated using the Gaussian kernel estimator Totest if a pair of TF (119910) and target (119909) is modulated by 119898 theCMI difference can be calculated as

Δ119868 = 119868 (119909 119910 | 119898 = 1) minus 119868 (119909 119910 | 119898 = 0) (7)

The pair is determined to be modulated if Δ119868 = 0 The sig-nificance 119875 values for Δ119868 = 0 is computed using permutationtests

33 Mimosa Similarly to MINDy Mimosa [45] was pro-posed to identifymodulated TF-target pairs However it doesnot preselect a set of modulators of interest but rather aims toalso search for the modulators Mimosa also assumes that amodulator takes only two states that is absence and presenceor 0 and 1 The modulated regulator-target pair is furtherassumed to be correlated when a modulator is present butuncorrelated when it is absent Therefore the distributionof a modulated TF-target pair 119909 and 119910 naturally follows amixture distribution

119901 (119909 119910) = 120587119901 (119909 119910 | 119898 = 0) + (1 minus 120587) 119901 (119909 119910119898 = 1) (8)

where 120587 is the probability of themodulator being absent Par-ticularly an uncorrelated and correlated bivariant Gaussiandistributions were introduced to model different modulatedregulator-target relationship such that

119901 (119909 119910 | 119898 = 0) =

1

2120587

119890minus(12)(119909

2+1199102) (9a)

119901 (119909 119910 | 119898 = 1)

=

1

2120587radic1 minus 1205722

119890minus(12)(119909

2+1199102+2120572119909119910)(1minus120572

2)

(9b)

where 120572 models the correlation between 119909 and 119910 when themodulator is present With this model Mimosa sets out tofit the samples of every pair of potential regulator targetwith the mixture model (7) This is equivalent to finding


MicroRNAs

Modulator

Regulator

Target3998400UTR

3998400UTRmRNA119898

mRNA 119910

Figure 4 Modulation of gene regulation by competing mRNAs

a partition of the paired expression samples into the cor-related and uncorrelated samples The paired expressionsamples that possess such correlated-uncorrelated partition(03 lt 120587 lt 07 and |120572| gt 08) are determined to bemodulated To identify the modulator of a (or a group of)modulated pair(s) a weighted 119905-test was developed to searchfor the genes whose expressions are differentially expressedin the correlated partition versus the uncorrelated partition

34 GEM (Gene Expression Modulator) GEM [46] improvesover MINDy by predicting how a modulator-TF interactionaffects the expression of the target gene It can detect newtypes of interactions that result in stronger correlation butlow Δ119868 which therefore would be missed by MINDy GEMhypothesizes that the correlation between the expression of amodulator 119898 and a target 119909 must change as that of the TF119909 changes Unlike the previous surveyed algorithms GEMfirst transforms the continuous expression levels to binarystates (up- (1) or down-expression (0)) and then works onlywith discrete expression states To model the hypothesizedrelationship the following model is proposed

119875 (119909 = 1 | 119910119898) = 120572119888+ 120572119898119898 + 120572

119910119910 + 120574119898119910 (10)

where 120572119888is a constant 120572

119898and 120572

119910model the effect of

modulator and TF on the target genes and 120574 represents theeffect of modulator-TF interaction on the target gene If themodulator-TF interaction has an effect on 119909 then 120574 willbe nonzero For a given (119909 119910119898) triplets GEM devised analgorithm to estimate the model coefficients in (10) and a testto determine if 120574 is nonzero or119898 is a modulator of 119909 and 119910

35MuTaMe (Mutually TargetedMREEnrichment) Thegoalof MuTaMe [21] is to identify ceRNA networks of a gene ofinterest (GoI) ormRNA that sharemiRNA response elements(MREs) of samemiRNAs Figure 4 shows twomRNAs whereone is the GoIy and the other is a candidate ceRNA ormodulator 119898 In the figure the miRNA represented in colorred has MREs in both mRNA 119910 and mRNA 119898 in this casethe presence of mRNA 119898 will start the competition with 119910for miRNA represented in color red

Thehypothesis ofMuTaMe is thatmRNAs that havemanyof the same MREs can regulate each other by competingfor miRNAs binding The input of this algorithm is a GoIwhich is targeted by a group of miRNAs known to the userThen from a database of predicted MREs for the entiretranscriptome it is possible to obtain the binding sites andits predicted locations in the 31015840UTR for all mRNAsThis datais used to generate scores for each mRNA based on severalfeatures

(a) the number of miRNAs that an mRNA119898 shares withthe GoI 119910

(b) the density of the predicted MREs for the miRNA itfavors the cases in which more MREs are located inshorter distances

(c) the distribution of the MREs for every miRNA itfavors situations in which theMREs tend to be evenlydistributed

(d) the number of MREs predicted to target 119898 it favorssituationswhere eachmiRNA containsmoreMREs in119898

Then each candidate transcript119898will be assigned a scorethat results from multiplying the scores in (a) to (d) Thisscore indicates the likelihood of the candidates to be ceRNAsand will be used to predict ceRNAs

36 Hermes Hermes [20] is an extension of MINDy thatinfers candidate modulators of miRNA activity from expres-sion profiles of genes and miRNAs of the same samplesHermes makes inferences by estimating the MI and CMIHowever different from MINDy (7) Hermes extracts thedependences of this triplet by studying the difference betweenthe CMI of 119909 expression and 119910 expression conditional on theexpression of119898 and theMI of 119909 and 119910 expressions as follows

119868 = 119868 (119909 119910 | 119898) minus 119868 (119909 119910) (11)

These quantities and their associated statistical signif-icance can be computed from collections of expressionof genes with number of samples 250 or greater Hermesexpands MINDy by providing the capacity to identify can-didate modulator genes of miRNAs activity The presenceof these modulators (119898) will affect the relation betweenthe expression of the miRNAs targeting a gene (119909) and theexpression level of this gene (119909)

In summary we surveyed some of the most popularalgorithms for the inference of modulator Additional mod-ulator identification algorithms are summarized in Table 1It is worth noting that the concept of modulator appliesto cases beyond discussed in this paper Such exampleincludes themultilayer integrated regulatorymodel proposedin Yan et al [49] where the top layer of regulators could bealso considered as ldquomodulatorsrdquo

4 Applications to Breast Cancer GeneExpression Data

Algorithms of utilizing modulator concept have been imple-mented in various software packages Here we will discuss


Table 1 Gene regulation network and modulator identification methods

Algorithm Features ReferencesARACNE Interaction network constructed via mutual information (MI) [14 42]

Network profiler A varying-coefficient structural equation model (SEM) to represent themodulator-dependent conditional independence between genes [47]

MINDy Gene-pair interaction dependency on modulator candidates by using theconditional mutual information (CMI) [44]

Mimosa Search for modulator by partition samples with a Gaussian mixture model [45]

GEM A probabilistic method for detecting modulators of TFs that affect the expression oftarget gene by using a priori knowledge and gene expression profiles [46]

MuTaMe Based on the hypothesis that shared MREs can regulate mRNAs by competing formicroRNAs binding [21]

Hermes Extension of MINDy to include microRNAs as candidate modulators by using CMIand MI from expression profiles of genes and miRNAs of the same samples [20]

ER120572 modulator Analyzes the interaction between TF and target gene conditioned on a group ofspecific modulator genes via a multiple linear regression [48]

two new applications MEGRA and TraceRNA implementedin-house specifically to utilize the concept of differentialcorrelation coefficients and ceRNAs to construct a modu-lated GRN with a predetermined modulator In the case ofMGERA we chose estrogen receptor ESR1 as the initialstarting point since it is one of the dominant and systemicfactor in breast cancer in the case of TraceRNAwe also chosegene ESR1 and its modulated gene network Preliminaryresults of applications to TCGA breast cancer data arereported in the following 2 sections

41MGERA TheModulatedGeneRegulationAnalysis algo-rithm (MGERA) was designed to explore gene regulationpairs modulated by the modulator 119898 The regulation pairscan be identified by examining the coexpression of two genesbased on Pearson correlation (similar to (7) in the contextof correlation coefficient) Fisher transformation is adoptedto normalize the correlation coefficients biased by samplesizes to obtain equivalent statistical power among data withdifferent sample sizes Statistical significance of differencein the absolute correlation coefficients between two genes istested by the student 119905-test following Fisher transformationFor the gene pairs with significantly different coefficientsbetween two genes active and deactive statuses are iden-tified by examining the modulated gene expression pairs(MGEPs)TheMGEPs are further combined to construct the119898 modulated gene regulation network for a systematic andcomprehensive view of interaction under modulation

To demonstrate the ability of MGERA we set estrogenreceptor (ER) as the modulator and applied the algorithm toTCGA breast cancer expression data [3] which contains 588expression profiles (461 ER+ and 127 ERminus) By using 119875 valuelt001 and the difference in the absolute Pearson correlationcoefficients gt06 as criteria we identified 2324 putativeER+ MGEPs and a highly connected ER+ modulated generegulation network was constructed (Figure 5) The top tengenes with highest connectivity was show in Table 2 Thecysteinetyrosine-rich 1 gene (CYYR1) connected to 142genes was identified as the top hub gene in the network andthus may serve as a key regulator under ER+ modulation

Table 2 Hub genes derived from modulated gene regulationnetwork (Figure 5)

Gene Number of ER+ MGEPsCYYR1 142MRAS 109C9orf19 95LOC339524 93PLEKHG1 92FBLN5 91BOC 91ANKRD35 89FAM107A 83C16orf77 73

Gene Ontology analysis of CYYR1 and its connected neigh-bor genes revealed significant association with extracellularmatrix epithelial tube formation and angiogenesis

42 TraceRNA To identify the regulationnetwork of ceRNAsfor a GoI we developed a web-based application TraceRNApresented earlier in [50] with extension to regulation networkconstructionThe analysis flow chart of TraceRNAwas shownin Figure 6 For a selected GoI the GoI binding miRNAs(GBmiRs) were derived either validated miRNAs from miR-TarBase [51] or predicted miRNAs from SVMicrO [52]ThenmRNAs (other than the given GoI) also targeted by GBmiRswere identified as the candidates of ceRNAs The relevant (ortumor-specific) gene expression data were used to furtherstrengthen relationship between the ceRNA candidates andGoI The candidate ceRNAs which coexpressed with GoIwere reported as putative ceRNAs To construct the generegulation network via GBmiRs we set each ceRNA as thesecondary GoI and the ceRNAs of these secondary GoIswere identified by applying the algorithm recursively Uponidentifying all the ceRNAs the regulation network of ceRNAsof a given GoI was constructed


EDG1GIMAP8

ACVR2A

MEOX1P2RY5

ESAM

KLF6

SOX7GSN

PALMDLYVE1

EGFLAM

OLFML2AGIMAP6

CD93

HSPA12B

SH2D3C

ATP8B4

P2RY14

ELMO3

MED29

GUCY1B3

SPNS2

ELTD1

RGS5

GGTA1

FAAH

KIAA1244

HIVEP2

RBP7

CDR1

GIMAP5

CD36ABCA9

LIMS2FOXO1

CYYR1 EBF3

MALLRSU1

FILIP1

KCNJ2 ROBO4

GIMAP1

CFH

LPPR4

C13orf33

C1QTNF7 EMX2OS

C10orf54

CIDEA

VWF

PODNPALLD

CILP

KGFLP1 LRRC32

OMD

ADH1BPLA2G5

C14orf37

WFWBCL6B

CCIN

ADAMTS16

AANAT

KIR3DL1

TPK1

EMILIN2

PLEKHQ1

ACVRL1F13A1

CCL4 PTGDS

BOC

LAYN

TSHZ2

RARRES1

ENPP6

MMP7

KRT6B

DSC1

ARID5BOLFM4

CX3CL1FLJ45803

GABRPDNM3

KCNMB111FAM3D

CPAMD8

SCPEP1PIK3C2G

BCL6

C12orf54

UTRN

IFNGR1IL33

USHBP1

GPBAR1PTH2R

NDRG2

CASQ2MAB21L1

CCL23CCL15CCCCEMP1

CRTAC1

SKIP

TP63

RRAGC

MMRN1

PLEKHF1

GUCY1A3ADCY4

HSD11B1

EBF2

RASD1 UACA

LGI4

ANKRD6

EGR2

RP5-1054A223ELN

GIMAP7

ADIPOQ

TLR4

HAND2

FAM20A

DPT

APDE1B

HHSOCS3

CD248

LCN6

TLL1

RSPO1

CXorf36

RRAD

CRISPLD2

PLB1

DYSF

RASSF222FAM101A

ST5

HSPG2

FGF1

ITGBL1LGI2

DCHS1

PLXDC2

RCN3DACT3SFRP2SPSB1

KCNJ15SRPX2IGFBP7 COL1A2

OR10A5

ACTA2PRICKLE1

XG

FMO1

C16orf30CLEC14A

TCF4C1S

LMAN1L

FBLN1SH3PXD2B

FMO3

MXRA8D22

HTRA1COL8A2

1DACT1

COL16A1

GLT8D2

LMOD1

SPON2

RFTN2

FILIP1L

MFAP5

MMP23BPRRX1 TNFAIP6

LUMCTSKFIBIN

COL6A3MAFB

SFRP4

KERACLDN5RSPO3FGF7

BHLHB3

C16orf77

BST1

SSPN

COL15A1TIE1

ABCC9

CRYABSV2B

LAMA2

SAA1NKAPL

FAM49AANKRD35CHL1

SAA2

SAV1

FBLN5TCEAL7KIAA0355CCDC80

C2orf40

THSD7APRELP

FRZBMMRN2

ALDH2

RASL12SERPING1

TAGLNLTBP2

CRB3

EPN3

TPD52

AP1M2

KIAA1543

TMC4

TJP3

MB

CDH23ZFP36

CLIC5

KL

FHL5

CCDC24

FAM86A

AVPR1ASTX12

FABP4

PDZRN3

ADH1CPPP1R12B

LASS6

SCARF1

LOC338328

ANKRD47SPARCL1SNOTCH4

JPH2TCF7L2

TM4SF18HECA

BSPRY

RASEF

SHROOM3

PVRL4

SPINT1

PROM2

SH2D3A

CLDN3

FAAH2

RAB25

LOC124220

RBM35A

C2orf15

PKP3

MARVELD3

FAM84B

VIL2

ELF3TACSTD1

PRSS8 C1orf172

C1orf34

OVOL1

ARL6IP1

MAGIX

CDS1

BIK

FXYD3

GSTO2

IGSF9

CAPN13

TRPS1

OCLN

DNASE2

C1orf210

P2RX4

LSR

C9orf7

EPHA10

AVAVAVAVFAM83H

KIAA1688

LRRC8E

FMOD

MMP11 RUNDC3B

CCDC107CLDN7

KRT19

CBLCCGN

C19orf46SLC44A4

KIAA1324

PRRG2

DDR1

NEBL

TMEM125

ATAD4

LRFN2

MARVELD2

IRX5

CLDN4

LRBA

SPDEF

LOC400451

SPINT2

DLG3

CREB3L4GRHL2

TFAP2A

PSD4

IRF6

RGS13

C1orf186

HDC

CPA3

AQP2

LCE1F

CYP11B1

GRAMD2

CLDN8

GPRIN2

TNN

PDGFD

GIMAP4

GJA12

SHANK3

MAGED2

PTCH2

KLK5PIGR

C4orf7

LGR6

SLC34A2

SELP

APOD

KIF19

PACRG

ZNF446

ZNF574

CREB3HHAT

PEX11B

C10orf81

KRT7

PSEN2

SLC25A44

SEC16A

CRHR1CCDC114

SPINK7

LCE3ETMEM95

OTOF

OR4D1

SSTR3

KLK9

OR10H2

ATN1

CDK5R2FLJ32214

KRT3

C1orf175

PROP1GHSR

PLA2G4D

TRGV5

TIGD5

VPS28

KIAA0143

DPY19L4

POLR2K

COMMD5

DYRK1B

PSORS1C2OR5H1

C10orf27

KRTAP11-1

LOC652968

OR2B11

OR5BF1

OR10P1

MGC40574

P2RX2 RASGRP4

Gene

Positive correlation under ER+ modulationNegative correlation under ER+ modulation

Figure 5 ER+ modulated gene regulation network

To identify ceRNA candidates three miRNAs bindingprediction algorithms SiteTest SVMicrO and BCMicrOwere used in TracRNA SiteTest is an algorithm similar toMuTaMe and uses UTR features for target prediction SVMi-crO [52] is an algorithm that uses a large number of sequence-level site as well as UTR features including binding secondarystructure energy and conservation whereas BCMicrO [53]employs a Bayesian approach that integrates predictions from6 popular algorithms including TargetScan miRanda PicTarmirTarget PITA and DIANA-microT Pearson correlationcoefficient was used to test the coexpression between the GoIand the candidate ceRNAs We utilized TCGA breast cancercohort [3] as the expression data by using 60 of GBmiRs

as commonmiRNAs and Pearson correlation coefficient gt09as criteria The final scores of putative ceRNAs (see Table 3last column) were generated by using Bordamergingmethodwhich rerank the sum of ranks from both GBmiR bindingand coexpression 119875 values [54] To illustrate the utility of theTraceRNA algorithm for breast cancer study we also focus onthe genes interacted with the estrogen receptor alpha ESR1with GBmiRs including miR-18a miR-18b miR-193b miR-19a miR-19b miR-206 miR-20b miR-22 miR-221 miR-222miR-29b and miR-302c The regulation network generatedby ESR1 as the initial GoI is shown in Figure 7 and the top18 ceRNAs are provided in Table 3 The TraceRNA algorithmcan be accessed httpcompgenomicsutsaeducerna


The GoI binding miRNA (GBmiRs)

Candidates of ceRNAs

Validated miRNAs from miRTarBaseor predicted by SVMicrO

Gene of interest (GoI)

Putative ceRNAs

Identify mRNAs targeted by the same GBmiRs

Identify ceRNAs with coexpressed patterns

Secondary Gol

Select each ceRNA as a secondary GoI

Regulation network of ceRNAs

Output to TraceRNA website

Figure 6 The analysis flow chart of TraceRNA

CADM1

PAPOLAPTPRD

RAB5B

KIAA1522

SENP1

ZC3H11A

RAP2C

KCNE4

DAG1

KIAA0467CXCL14

MBNL1GFAP

CBFBDAXX

TCF4

FOXA1

KBTBD4

WDFY3

ARID4ARAP1GDS1

LEF1PPP3CB

ZFX

RYBPG3BP2

VAV3

FNIP1SMG1

PKN2

PHTF2TAOK1OTUD4

ZNF654 MED13ZNF800

GRM8PAFAH1B1

SRGAP3POMGNT1

GRIA2FAM120A

BMS1P5

HNRPDLGMFBERBB2IPMAP2K4

TMEM57

COL12A1 COPB2C5orf24

C14orf101PCDHAC1

PCDHAC2C1orf9

TEX2

FNBP1L

BCL2L1

PCDHA1PCDHA5

NAV3

BAZ2BTHBS1

MAP3K1 TP53INP1MYST4

KLF9

MED13L

RNF11

ATP7ANFIA

PCDHA3

CRIM1

PAN3CACNA2D2

RAB8B

PPM1B

JAZF1C20orf194

RAPGEF2

FEM1CPCDHA9SFRS12

TGFBR3

ARRDC3

RPGRIP1L

KIAA0240

MAT2AGLCE

FNDC3AREEP1

RANBP6

TNRC6A

CPEB2

ESRRG

ZFPM2

SOCS5

ELL2

ARPP-19

ZNF516

BACE1

ANTXR2

FAM70A FLRT3

CHD9SLC12A2

CSNK1G3DCUN1D4

ATPBD4

PRPF38BUBE3ARBM12

FMR1

RND3PAK7

CIT

C18orf25MUTED

MEIS2

ZNF608CLK2P

C7orf60BRMS1L

ZDHHC17

CTNND2

ACSL1FUSIP1

PIK3C2A

FAM135A

NR3C2

USP15

PTGFRNC5orf13VEZF1

RAPGEF5

THSD3

SOBP

TMEM11

MBNL2

NARG1NRXN1

NCOA2PPP2CA

MAPRE1

SLC24A3

ZADH2

PPFIA2

TMEM115HOMER1

B4GALT2

FOXP1

GH2

PRM1PML

GGT3P

DMRT2

DST

LOC442245MAP1LC3A

RAB14

HIPK1

PTEN MIER3ZFP91

NPAS3 ALCAM

DACH1 SNORD8

ZNF238

GIT1

GOSR1CPEB4

PPP1R12A

TNRC6CCSH1

PITPNBCOL4A3BP

DLG2ATP2B1

ZNF292

RICTORRUNX2

BTRC

ARX

STK35CYB561D1

PIP4K2A

SLC6A10PLYCAT

CD2BP2

PUM2

RERERSBN1 PDS5B

CPEB3VEGFA

CUL3RAP1B

CAMTA1

SIRT1DCUN1D3

KCNJ2

TSHZ3

DTNA

CCNT2

TARDBP

ZFHX3

CNOT6NOVA1SEMA6A

BAGE5

ATRX

RCAN2KIAA0232

EBF1DNAJA2

SATB2LEMD3

C10orf26

LCOR

ZBTB4

ARHGEF17ST7LQKI

SLC12A5

MMP24

REEP2

WDR47DIXDC1

MEF2C

PRKG1LHX6

ARL4AANK2

NRP2

SCN2A

ZFHX4

ABCD4

MAGI2MAPT

PPP6C

HEYL

CFL2

ABHD13

JAG1

USP47

NBEA

SPTY2D1

BSN NHS

BZRAP1

MAPKBP1

SEMA6D

YPEL5

PHF15 DDX3X

CAPN3

NOPE

SLC38A2

UBL3MAGI1

ZSWIM6 CADPS

SMARCAD1

ESR1

(a)

KB

WDFY3

ZFX

G

PAFAH1B1POMGN

FAM120A

ERBB2MAP2K4

NAV3

BAZ2BTHBS1

MYST4

KLF9

MED13L

RNF11

ATP7ANFIA

CRIM1

PAN3CACNA2D2

RAB8B

KIAA0240

MAT2AGLCE

ESR1

FNDC3AREEP1

RANBP6

TNRC6A

CPEB2

ESRRG

ZFPM2

SOCS5

ELL2

ARPP-19

ZNF516

RAB14

HIPK1

PTEN MIER3ZFP91

NPAS3 ALCAM

DACH1

GOSR1TNRC

ZNF292

BT

PUM2RSBN1

CPEB3VEGFA

CUL3

RAP1B

SIRT1DCUN1D3 TSHZ3

DTNA

TARDBP

ZFHX3

CNOT6 NOVA1

SEMA6A

BAGE5

ATRX

F2C

HS

(b)

Figure 7 (a) ceRNA network for gene of interest ESR1 generated using TraceRNA (b) Network graph enlarged at ESR1

5 Conclusions

In this report we attempt to provide a unified concept ofmodulation of gene regulation encompassing earlier mRNAexpression based methods and lately the ceRNAmethod Weexpect the integration of ceRNA concept into the gene-geneinteractions and their modulator identification will further

enhance our understanding in gene interaction and theirsystemic influence Applications provided here also representexamples of our earlier attempt to construct modulated net-works specific to breast cancer studies Further investigationwill be carried out to extend our modeling to provide aunified understanding of genetic regulation in an alteredenvironment


Table 3 Top 18 candidate ceRNAs for ESR1 as GOI obtained from TraceRNA ESR1 is at rank of 174 (not listed in this table)

Gene symbol SVMicrO-based prediction Expression correlationFinal score

Score 119875 value Score 119875 valueFOXP1 1066 00043 0508 0016 1212VEZF1 0942 00060 04868 0020 1179NOVA1 0896 00067 0479 0023 1160CPEB3 0858 00074 0484 0022 1149MAP2K4 0919 00064 0322 0097 1139FAM120A 0885 00069 0341 0082 1130PCDHA3 0983 00054 0170 0215 1125SIRT1 0927 00062 0230 0162 1117PCDHA5 0983 00054 0148 0233 1113PTEN 0898 00067 0221 0168 1104PCDHA1 0983 00054 0140 0239 1103NBEA 0752 00098 0491 0020 1102ZFHX4 0970 00056 0154 0229 1097GLCE 0798 00087 03231 0096 1096MAGI2 0777 00092 0321 0097 1086SATB2 0801 00086 0243 0151 1078LEF1 0753 00098 0291 0112 1065ATPBD4 0819 00082 0170 0215 1060

Authorsrsquo Contribution

MFlores andT-HHsiao are contributed equally to thiswork

Acknowledgments

The authors would like to thank the funding support ofthis work by Qatar National Research Foundation (NPRP09 -874-3-235) to Y Chen and Y Huang National ScienceFoundation (CCF-1246073) to Y Huang The authors alsothank the computational support provided by the UTSAComputational Systems Biology Core Facility (NIH RCMI5G12RR013646-12)

References

[1] M Schena D Shalon R W Davis and P O Brown ldquoQuantita-tive monitoring of gene expression patterns with a complemen-tary DNA microarrayrdquo Science vol 270 no 5235 pp 467ndash4701995

[2] E R Mardis ldquoNext-generation DNA sequencing methodsrdquoAnnual Review of Genomics and Human Genetics vol 9 pp387ndash402 2008

[3] Cancer Genome Atlas Network ldquoComprehensive molecularportraits of human breast tumoursrdquoNature vol 490 pp 61ndash702012

[4] D Bell A Berchuck M Birrer et al ldquoIntegrated genomicanalyses of ovarian carcinomardquo Nature vol 474 no 7353 pp609ndash615 2011

[5] R McLendon A Friedman D Bigner et al ldquoComprehensivegenomic characterization defines human glioblastoma genesand core pathwaysrdquo Nature vol 455 no 7216 pp 1061ndash10682008

[6] C M Perou T Soslashrile M B Eisen et al ldquoMolecular portraits ofhuman breast tumoursrdquoNature vol 406 no 6797 pp 747ndash7522000

[7] J Lapointe C Li J P Higgins et al ldquoGene expression profilingidentifies clinically relevant subtypes of prostate cancerrdquo Pro-ceedings of the National Academy of Sciences of the United Statesof America vol 101 no 3 pp 811ndash816 2004

[8] M B Eisen P T Spellman P O Brown and D Botstein ldquoClus-ter analysis and display of genome-wide expression patternsrdquoProceedings of the National Academy of Sciences of the UnitedStates of America vol 95 no 25 pp 14863ndash14868 1998

[9] T Schlitt and A Brazma ldquoCurrent approaches to gene regula-tory network modellingrdquo BMC Bioinformatics vol 8 supple-ment 6 article S9 2007

[10] H Hache H Lehrach and R Herwig ldquoReverse engineering ofgene regulatory networks a comparative studyrdquo Eurasip Journalon Bioinformatics and Systems Biology vol 2009 Article ID617281 2009

[11] W P Lee and W S Tzou ldquoComputational methods for dis-covering gene networks from expression datardquo Briefings inBioinformatics vol 10 no 4 pp 408ndash423 2009

[12] C Sima J Hua and S Jung ldquoInference of gene regulatorynetworks using time-series data a surveyrdquo Current Genomicsvol 10 no 6 pp 416ndash429 2009

[13] J M Stuart E Segal D Koller and S K Kim ldquoA gene-coexpression network for global discovery of conserved geneticmodulesrdquo Science vol 302 no 5643 pp 249ndash255 2003

[14] A A Margolin I Nemenman K Basso et al ldquoARACNE analgorithm for the reconstruction of gene regulatory networksin a mammalian cellular contextrdquo BMC Bioinformatics vol 7supplement 1 article S7 2006

[15] E R Dougherty S Kim and Y Chen ldquoCoefficient of determi-nation in nonlinear signal processingrdquo Signal Processing vol 80no 10 pp 2219ndash2235 2000


[16] S Kim E R Dougherty Y Chen et al ldquoMultivariate measure-ment of gene expression relationshipsrdquo Genomics vol 67 no 2pp 201ndash209 2000

[17] X Chen M Chen and K Ning ldquoBNArray an R package forconstructing gene regulatory networks from microarray databy using Bayesian networkrdquo Bioinformatics vol 22 no 23 pp2952ndash2954 2006

[18] A V Werhli M Grzegorczyk and D Husmeier ldquoCompar-ative evaluation of reverse engineering gene regulatory net-works with relevance networks graphical gaussian models andbayesian networksrdquo Bioinformatics vol 22 no 20 pp 2523ndash2531 2006

[19] I Shmulevich E R Dougherty S Kim and W Zhang ldquoProb-abilistic Boolean networks a rule-based uncertainty model forgene regulatory networksrdquoBioinformatics vol 18 no 2 pp 261ndash274 2002

[20] P Sumazin X Yang H-S Chiu et al ldquoAn extensiveMicroRNA-mediated network of RNA-RNA interactions regulates estab-lished oncogenic pathways in glioblastomardquo Cell vol 147 no2 pp 370ndash381 2011

[21] Y Tay L Kats L Salmena et al ldquoCoding-independent regula-tion of the tumor suppressor PTEN by competing endogenousmRNAsrdquo Cell vol 147 no 2 pp 344ndash357 2011

[22] D P Bartel ldquoMicroRNAs target recognition and regulatoryfunctionsrdquo Cell vol 136 no 2 pp 215ndash233 2009

[23] D Yue J Meng M Lu C L P Chen M Guo and Y HuangldquoUnderstanding MicroRNA regulation a computational per-spectiverdquo IEEE Signal ProcessingMagazine vol 29 no 1 ArticleID 6105465 pp 77ndash88 2012

[24] MW Jones-Rhoades and D P Bartel ldquoComputational identifi-cation of plant MicroRNAs and their targets including a stress-induced miRNArdquo Molecular Cell vol 14 no 6 pp 787ndash7992004

[25] DHanahan andRAWeinberg ldquoThehallmarks of cancerrdquoCellvol 100 no 1 pp 57ndash70 2000

[26] S Y Chun C Johnson J G Washburn M R Cruz-CorreaD T Dang and L H Dang ldquoOncogenic KRAS modulatesmitochondrial metabolism in human colon cancer cells byinducing HIF-1120572 and HIF-2120572 target genesrdquo Molecular Cancervol 9 article 293 2010

[27] N J Hudson A Reverter and B P Dalrymple ldquoA differentialwiring analysis of expression data correctly identifies the genecontaining the causal mutationrdquo PLoS Computational Biologyvol 5 no 5 Article ID e1000382 2009

[28] I Stelniec-Klotz S Legewie O Tchernitsa et al ldquoReverseengineering a hierarchical regulatory network downstream ofoncogenic KRASrdquoMolecular Systems Biology vol 8 Article ID601 2012

[29] C Shen Y Huang Y Liu et al ldquoA modulated empiricalBayes model for identifying topological and temporal estrogenreceptor 120572 regulatory networks in breast cancerrdquo BMC SystemsBiology vol 5 article 67 2011

[30] C A Wilson and J Dering ldquoRecent translational researchmicroarray expression profiling of breast cancer Beyond classi-fication and prognostic markersrdquo Breast Cancer Research vol6 no 5 pp 192ndash200 2004

[31] H E Cunliffe M Ringner S Bilke et al ldquoThe gene expressionresponse of breast cancer to growth regulators patterns andcorrelation with tumor expression profilesrdquo Cancer Researchvol 63 no 21 pp 7158ndash7166 2003

[32] J Frasor F Stossi J M Danes B Komm C R Lyttle andB S Katzenellenbogen ldquoSelective estrogen receptor modula-tors discrimination of agonistic versus antagonistic activitiesby gene expression profiling in breast cancer cellsrdquo CancerResearch vol 64 no 4 pp 1522ndash1533 2004

[33] L J vanrsquot VeerHDaiM J van deVijver et al ldquoGene expressionprofiling predicts clinical outcome of breast cancerrdquoNature vol415 no 6871 pp 530ndash536 2002

[34] S A Kauffman The Origins of Order Self-Organization andSelection in Evolution Oxford University Press New York NYUSA 1993

[35] J D Allen Y Xie M Chen L Girard and G Xiao ldquoComparingstatistical methods for constructing large scale gene networksrdquoPLoS ONE vol 7 no 1 Article ID e29348 2012

[36] Y Huang I M Tienda-Luna and Y Wang ldquoReverse engineer-ing gene regulatory networks a survey of statistical modelsrdquoIEEE Signal Processing Magazine vol 26 no 1 pp 76ndash97 2009

[37] F Crick ldquoCentral dogma of molecular biologyrdquoNature vol 227no 5258 pp 561ndash563 1970

[38] A Hamilton and M Piccart ldquoThe contribution of molecularmarkers to the prediction of response in the treatment of breastcancer a review of the literature on HER-2 p53 and BCL-2rdquoAnnals of Oncology vol 11 no 6 pp 647ndash663 2000

[39] C Sotiriou S Y Neo L M McShane et al ldquoBreast cancerclassification and prognosis based on gene expression profilesfrom a population-based studyrdquo Proceedings of the NationalAcademy of Sciences of the United States of America vol 100 no18 pp 10393ndash10398 2003

[40] T Soslashrlie C M Perou R Tibshirani et al ldquoGene expressionpatterns of breast carcinomas distinguish tumor subclasses withclinical implicationsrdquo Proceedings of the National Academy ofSciences of theUnited States of America vol 98 no 19 pp 10869ndash10874 2001

[41] J S Carroll C A Meyer J Song et al ldquoGenome-wide analysisof estrogen receptor binding sitesrdquo Nature Genetics vol 38 no11 pp 1289ndash1297 2006

[42] K Basso A A Margolin G Stolovitzky U Klein R Dalla-Favera and A Califano ldquoReverse engineering of regulatorynetworks in human B cellsrdquo Nature Genetics vol 37 no 4 pp382ndash390 2005

[43] K C Liang andXWang ldquoGene regulatory network reconstruc-tion using conditional mutual informationrdquo Eurasip Journalon Bioinformatics and Systems Biology vol 2008 Article ID253894 2008

[44] K Wang B C Bisikirska M J Alvarez et al ldquoGenome-wideidentification of post-translational modulators of transcriptionfactor activity in human B cellsrdquo Nature Biotechnology vol 27no 9 pp 829ndash837 2009

[45] M Hansen L Everett L Singh and S Hannenhalli ldquoMimosamixture model of co-expression to detect modulators of regu-latory interactionrdquo Algorithms for Molecular Biology vol 5 no1 article 4 2010

[46] O Babur E Demir M Gonen C Sander and U DogrusozldquoDiscovering modulators of gene expressionrdquo Nucleic AcidsResearch vol 38 no 17 Article ID gkq287 pp 5648ndash5656 2010

[47] T Shimamura S Imoto Y Shimada et al ldquoA novel net-work profiling analysis reveals system changes in epithelial-mesenchymal transitionrdquo PLoS ONE vol 6 no 6 Article IDe20804 2011

[48] HYWuet al ldquoAmodulator based regulatory network for ERal-pha signaling pathwayrdquo BMC Genomics vol 13 Supplement 6article S6 2012


[49] K-K Yan W Hwang J Qian et al ldquoConstruction and anal-ysis of an integrated regulatory network derived from High-Throughput sequencing datardquo PLoS Computational Biology vol7 no 11 Article ID e1002190 2011

[50] M Flores and Y Huang ldquoTraceRNA a web based applicationfor ceRNAs predictionrdquo in Proceedings of the IEEE GenomicSignal Processing and Statistics Workshop (GENSIPS rsquo12) 2012

[51] S D Hsu F M Lin W Y Wu et al ldquoMiRTarBase a data-base curates experimentally validated microRNA-target inter-actionsrdquo Nucleic Acids Research vol 39 no 1 pp D163ndashD1692011

[52] H Liu D Yue Y Chen S J Gao andYHuang ldquoImproving per-formance of mammalian microRNA target predictionrdquo BMCBioinformatics vol 11 article 476 2010

[53] Y Dong et al ldquoA Bayesian decision fusion approach formicroRNA target predictionrdquo BMC Genomics vol 13 2012

[54] J A Asm and M Montague ldquoModels for Metasearchrdquo in Pro-ceedings of the 24th annual international ACM SIGIR conferenceon Research and development in information retrieval pp 276ndash284 la New Orleans La USA 2001

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


Interact

Regulator Target

119909 119910

(a)

Regulator

119909

119910

119911

119908

of 119910 and 119908

Regulator of 119911

Target of 119910

Target of 119909

Target of 119909

(b)

Figure 1 Regulator-target pair in genetic regulatory network model (a) basic regulator-target pair and (b) regulator-target complex

on sequence-specific binding sites at target genesrsquo promoterregions In contrast ceRNAs mediate gene regulation viacompeting miRNAs binding sites in target 31015840UTR regionwhich exist in gt50 of mRNAs [22 24] In this study wewill extend the current network construction methods byincorporating regulation via ceRNAs

In tumorigenesis gene mutation is the main cause ofthe cancer [25] The mutation may not directly reflect inthe change at the gene expression level however it willdisrupt gene regulation [26ndash28] In Hudson et al theyfound that mutated myostatin and MYL2 showed differentcoexpressionswhen comparing towild-typemyostatin Chunet al also showed that oncogenic KRAS modulates HIF-1120572 and HIF-2120572 target genes and in turn modulates cancermetabolism Stelniec-Klotz et al presented a complex hierar-chicalmodel ofKRASmodulated network followed by doubleperturbation experiments Shen et al [29] showed a temporalchange of GRNs modulated after the estradiol stimulationindicating important role of estrogen in modulating GRNsFunctionally modulation effect of high expression of ESR1was also reported byWilson andDering [30]where they stud-ied previously published microarray data with cells treatedwith hormone receptor agonists and antagonists [31ndash33] Inthis study a comprehensive review of existing algorithms touncover themodulators was provided Given either mutationor protein expression status was unknown under many ofreported studies the problem of how to partition the diversesamples with different conditions such as active or inactiveoncogene status (and perhaps a combination of multiplemutations) and the prediction of a putative modulator ofgene regulation remains a difficult task

By combining gene regulation obtained from coexpres-sion data and ceRNAs we report here an early attempt tounify two systems mathematically while assuming a knownmodulator estrogen receptor (ER) By employing the TCGA[3] breast tumor gene expressions data and their clinical testresult (ER status) we demonstrate the approach of obtainingGRN via ceRNAs and a new presentation of ER modulationeffects By integrating breast cancer data into our uniqueceRNAs discovery website we are uniquely positioned tofurther explore the ceRNA regulation network and further

develop the discovery algorithms in order to detect potentialmodulators of regulatory interactions

2 Models of Gene Regulation and Modulation

21 Regulation of Gene Expression Thecomplex relationshipsamong genes and their products in a cellular system canbe studied using genetic regulatory networks (GRNs) Thenetworksmodel the different states or phenotypes of a cellularsystem In this model the interactions are commonly mod-eled as regulator-target pairs with edges between regulatorand target pair representing their interaction direction asshown in Figure 1(a) In this model a target gene is a genewhose expression can be altered (activated or suppressed)by a regulator gene This definition of a target gene impliesthat any gene can be at some point a target gene or adirect or indirect regulator depending on its position in thegenetic regulatory network The regulator gene is a gene thatcontrols (activates or suppresses) its target genesrsquo expressionThe consequences of these activated (or suppressed) genessometimes are involved in specific biological functions suchas cell proliferation in cancer Examples of regulator-targetpair in biology are common For example a target geneCDCA7 (cell division cycle-associated protein 7) is a c-Myc(regulator) responsive gene and it is part of c-Myc-mediatedtransformation of lymphoblastoid cells Furthermore asshown in Figure 1(b) a regulator gene can also act as a targetgene if there exists an upstream regulator

If the interaction is modeled after Boolean network (BN)model [34] then

119910119894(119905 + 1) = 119891

119894(1199091198951(119905) 119909

119895119896(119905) 119910119894(119905)) (1)

where each regulator 119909119895isin 0 1 is a binary variable as well

as it is target 119910119894 As described by (1) the target 119910

119894at time

119905 + 1 is completely determined by the values of its regulatorsat time 119905 by means of a Boolean function 119891

119894isin 119865 where

119865 is a collection of Boolean functions Thus the Booleannetwork 119866(119881 119865) is defined as a set of nodes (genes) 119881 =1199091 1199092 119909

119899 and a list of functions (edges or interactions)

119865 = 1198911 1198912 119891

119899 Similarly such relationship can be

defined in the framework of Bayesian network where the


R

Target gene

TF

119910

(a)


ReceptorHormone

(b)

microRNAs

3998400UTR

Target gene 119910

(c)



119875 (119910119894(119905 + 1) 119909

1198951(119905) 119909

119895119896(119905) 119910119894(119905))

= 119875 (119910119894(119905 + 1) | Parents (119910

119894(119905 + 1)))

times 119875 (Parents (119910119894(119905 + 1)))

(2)

where Parents(119910119894(119905 + 1)) = 119909

1198951

(119905) 119909119895119896(119905) 119910119894(119905) is the set


119894(119905 +


119894(119905 + 1))) models the



Modulator

Interact

Regulator Target

119909 119910

119898





119910 = F(119898)

(119909) (3)




119901 (119910 119909 | 119898) = 119901 (119910 | 119909119898) 119901 (119909 | 119898) (4)





119901 (119909119894) =

1

119911

119890minus[sum119873

119894=1120601119894(119909119894)+sum119873

119894119895120601119894119895(119909119894119910119895)] (5)



MI119909119910


119909 119892119910 119892119911) where 119892


119911


MI119909119911lt min (MI

119909119910MI119910119911) if 119909 997888rarr 119910 997888rarr 119911







Δ119868 = 119868 (119909 119910 | 119898 = 1) minus 119868 (119909 119910 | 119898 = 0) (7)



119901 (119909 119910) = 120587119901 (119909 119910 | 119898 = 0) + (1 minus 120587) 119901 (119909 119910119898 = 1) (8)


119901 (119909 119910 | 119898 = 0) =

1

2120587

119890minus(12)(119909

2+1199102) (9a)

119901 (119909 119910 | 119898 = 1)

=

1

2120587radic1 minus 1205722

119890minus(12)(119909

2+1199102+2120572119909119910)(1minus120572

2)

(9b)



MicroRNAs

Modulator

Regulator

Target3998400UTR

3998400UTRmRNA119898

mRNA 119910




119875 (119909 = 1 | 119910119898) = 120572119888+ 120572119898119898 + 120572

119910119910 + 120574119898119910 (10)


119898and 120572











119868 = 119868 (119909 119910 | 119898) minus 119868 (119909 119910) (11)























EDG1GIMAP8

ACVR2A

MEOX1P2RY5

ESAM

KLF6

SOX7GSN

PALMDLYVE1

EGFLAM

OLFML2AGIMAP6

CD93

HSPA12B

SH2D3C

ATP8B4

P2RY14

ELMO3

MED29

GUCY1B3

SPNS2

ELTD1

RGS5

GGTA1

FAAH

KIAA1244

HIVEP2

RBP7

CDR1

GIMAP5

CD36ABCA9

LIMS2FOXO1

CYYR1 EBF3

MALLRSU1

FILIP1

KCNJ2 ROBO4

GIMAP1

CFH

LPPR4

C13orf33

C1QTNF7 EMX2OS

C10orf54

CIDEA

VWF

PODNPALLD

CILP

KGFLP1 LRRC32

OMD

ADH1BPLA2G5

C14orf37

WFWBCL6B

CCIN

ADAMTS16

AANAT

KIR3DL1

TPK1

EMILIN2

PLEKHQ1

ACVRL1F13A1

CCL4 PTGDS

BOC

LAYN

TSHZ2

RARRES1

ENPP6

MMP7

KRT6B

DSC1

ARID5BOLFM4

CX3CL1FLJ45803

GABRPDNM3

KCNMB111FAM3D

CPAMD8

SCPEP1PIK3C2G

BCL6

C12orf54

UTRN

IFNGR1IL33

USHBP1

GPBAR1PTH2R

NDRG2

CASQ2MAB21L1

CCL23CCL15CCCCEMP1

CRTAC1

SKIP

TP63

RRAGC

MMRN1

PLEKHF1

GUCY1A3ADCY4

HSD11B1

EBF2

RASD1 UACA

LGI4

ANKRD6

EGR2

RP5-1054A223ELN

GIMAP7

ADIPOQ

TLR4

HAND2

FAM20A

DPT

APDE1B

HHSOCS3

CD248

LCN6

TLL1

RSPO1

CXorf36

RRAD

CRISPLD2

PLB1

DYSF

RASSF222FAM101A

ST5

HSPG2

FGF1

ITGBL1LGI2

DCHS1

PLXDC2

RCN3DACT3SFRP2SPSB1


OR10A5

ACTA2PRICKLE1

XG

FMO1

C16orf30CLEC14A

TCF4C1S

LMAN1L

FBLN1SH3PXD2B

FMO3

MXRA8D22

HTRA1COL8A2

1DACT1

COL16A1

GLT8D2

LMOD1

SPON2

RFTN2

FILIP1L

MFAP5

MMP23BPRRX1 TNFAIP6

LUMCTSKFIBIN

COL6A3MAFB

SFRP4

KERACLDN5RSPO3FGF7

BHLHB3

C16orf77

BST1

SSPN

COL15A1TIE1

ABCC9

CRYABSV2B

LAMA2

SAA1NKAPL

FAM49AANKRD35CHL1

SAA2

SAV1


C2orf40

THSD7APRELP

FRZBMMRN2

ALDH2

RASL12SERPING1

TAGLNLTBP2

CRB3

EPN3

TPD52

AP1M2

KIAA1543

TMC4

TJP3

MB

CDH23ZFP36

CLIC5

KL

FHL5

CCDC24

FAM86A

AVPR1ASTX12

FABP4

PDZRN3

ADH1CPPP1R12B

LASS6

SCARF1

LOC338328


JPH2TCF7L2

TM4SF18HECA

BSPRY

RASEF

SHROOM3

PVRL4

SPINT1

PROM2

SH2D3A

CLDN3

FAAH2

RAB25

LOC124220

RBM35A

C2orf15

PKP3

MARVELD3

FAM84B

VIL2

ELF3TACSTD1

PRSS8 C1orf172

C1orf34

OVOL1

ARL6IP1

MAGIX

CDS1

BIK

FXYD3

GSTO2

IGSF9

CAPN13

TRPS1

OCLN

DNASE2

C1orf210

P2RX4

LSR

C9orf7

EPHA10

AVAVAVAVFAM83H

KIAA1688

LRRC8E

FMOD

MMP11 RUNDC3B

CCDC107CLDN7

KRT19

CBLCCGN

C19orf46SLC44A4

KIAA1324

PRRG2

DDR1

NEBL

TMEM125

ATAD4

LRFN2

MARVELD2

IRX5

CLDN4

LRBA

SPDEF

LOC400451

SPINT2

DLG3

CREB3L4GRHL2

TFAP2A

PSD4

IRF6

RGS13

C1orf186

HDC

CPA3

AQP2

LCE1F

CYP11B1

GRAMD2

CLDN8

GPRIN2

TNN

PDGFD

GIMAP4

GJA12

SHANK3

MAGED2

PTCH2

KLK5PIGR

C4orf7

LGR6

SLC34A2

SELP

APOD

KIF19

PACRG

ZNF446

ZNF574

CREB3HHAT

PEX11B

C10orf81

KRT7

PSEN2

SLC25A44

SEC16A

CRHR1CCDC114

SPINK7

LCE3ETMEM95

OTOF

OR4D1

SSTR3

KLK9

OR10H2

ATN1

CDK5R2FLJ32214

KRT3

C1orf175

PROP1GHSR

PLA2G4D

TRGV5

TIGD5

VPS28

KIAA0143

DPY19L4

POLR2K

COMMD5

DYRK1B

PSORS1C2OR5H1

C10orf27

KRTAP11-1

LOC652968

OR2B11

OR5BF1

OR10P1

MGC40574

P2RX2 RASGRP4

Gene










Putative ceRNAs



Secondary Gol





CADM1

PAPOLAPTPRD

RAB5B

KIAA1522

SENP1

ZC3H11A

RAP2C

KCNE4

DAG1

KIAA0467CXCL14

MBNL1GFAP

CBFBDAXX

TCF4

FOXA1

KBTBD4

WDFY3

ARID4ARAP1GDS1

LEF1PPP3CB

ZFX

RYBPG3BP2

VAV3

FNIP1SMG1

PKN2

PHTF2TAOK1OTUD4

ZNF654 MED13ZNF800

GRM8PAFAH1B1

SRGAP3POMGNT1

GRIA2FAM120A

BMS1P5


TMEM57


C14orf101PCDHAC1

PCDHAC2C1orf9

TEX2

FNBP1L

BCL2L1

PCDHA1PCDHA5

NAV3

BAZ2BTHBS1


KLF9

MED13L

RNF11

ATP7ANFIA

PCDHA3

CRIM1

PAN3CACNA2D2

RAB8B

PPM1B

JAZF1C20orf194

RAPGEF2

FEM1CPCDHA9SFRS12

TGFBR3

ARRDC3

RPGRIP1L

KIAA0240

MAT2AGLCE

FNDC3AREEP1

RANBP6

TNRC6A

CPEB2

ESRRG

ZFPM2

SOCS5

ELL2

ARPP-19

ZNF516

BACE1

ANTXR2

FAM70A FLRT3

CHD9SLC12A2

CSNK1G3DCUN1D4

ATPBD4

PRPF38BUBE3ARBM12

FMR1

RND3PAK7

CIT

C18orf25MUTED

MEIS2

ZNF608CLK2P

C7orf60BRMS1L

ZDHHC17

CTNND2

ACSL1FUSIP1

PIK3C2A

FAM135A

NR3C2

USP15

PTGFRNC5orf13VEZF1

RAPGEF5

THSD3

SOBP

TMEM11

MBNL2

NARG1NRXN1

NCOA2PPP2CA

MAPRE1

SLC24A3

ZADH2

PPFIA2

TMEM115HOMER1

B4GALT2

FOXP1

GH2

PRM1PML

GGT3P

DMRT2

DST

LOC442245MAP1LC3A

RAB14

HIPK1

PTEN MIER3ZFP91

NPAS3 ALCAM

DACH1 SNORD8

ZNF238

GIT1

GOSR1CPEB4

PPP1R12A

TNRC6CCSH1

PITPNBCOL4A3BP

DLG2ATP2B1

ZNF292

RICTORRUNX2

BTRC

ARX

STK35CYB561D1

PIP4K2A

SLC6A10PLYCAT

CD2BP2

PUM2

RERERSBN1 PDS5B

CPEB3VEGFA

CUL3RAP1B

CAMTA1

SIRT1DCUN1D3

KCNJ2

TSHZ3

DTNA

CCNT2

TARDBP

ZFHX3

CNOT6NOVA1SEMA6A

BAGE5

ATRX

RCAN2KIAA0232

EBF1DNAJA2

SATB2LEMD3

C10orf26

LCOR

ZBTB4

ARHGEF17ST7LQKI

SLC12A5

MMP24

REEP2

WDR47DIXDC1

MEF2C

PRKG1LHX6

ARL4AANK2

NRP2

SCN2A

ZFHX4

ABCD4

MAGI2MAPT

PPP6C

HEYL

CFL2

ABHD13

JAG1

USP47

NBEA

SPTY2D1

BSN NHS

BZRAP1

MAPKBP1

SEMA6D

YPEL5

PHF15 DDX3X

CAPN3

NOPE

SLC38A2

UBL3MAGI1

ZSWIM6 CADPS

SMARCAD1

ESR1

(a)

KB

WDFY3

ZFX

G

PAFAH1B1POMGN

FAM120A

ERBB2MAP2K4

NAV3

BAZ2BTHBS1

MYST4

KLF9

MED13L

RNF11

ATP7ANFIA

CRIM1

PAN3CACNA2D2

RAB8B

KIAA0240

MAT2AGLCE

ESR1

FNDC3AREEP1

RANBP6

TNRC6A

CPEB2

ESRRG

ZFPM2

SOCS5

ELL2

ARPP-19

ZNF516

RAB14

HIPK1

PTEN MIER3ZFP91

NPAS3 ALCAM

DACH1

GOSR1TNRC

ZNF292

BT

PUM2RSBN1

CPEB3VEGFA

CUL3

RAP1B

SIRT1DCUN1D3 TSHZ3

DTNA

TARDBP

ZFHX3

CNOT6 NOVA1

SEMA6A

BAGE5

ATRX

F2C

HS

(b)


5 Conclusions









Acknowledgments


References
































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


R

Target gene

TF

119910

(a)


ReceptorHormone

(b)

microRNAs

3998400UTR

Target gene 119910

(c)



119875 (119910119894(119905 + 1) 119909

1198951(119905) 119909

119895119896(119905) 119910119894(119905))

= 119875 (119910119894(119905 + 1) | Parents (119910

119894(119905 + 1)))

times 119875 (Parents (119910119894(119905 + 1)))

(2)

where Parents(119910119894(119905 + 1)) = 119909

1198951

(119905) 119909119895119896(119905) 119910119894(119905) is the set


119894(119905 +


119894(119905 + 1))) models the



Modulator

Interact

Regulator Target

119909 119910

119898





119910 = F(119898)

(119909) (3)




119901 (119910 119909 | 119898) = 119901 (119910 | 119909119898) 119901 (119909 | 119898) (4)





119901 (119909119894) =

1

119911

119890minus[sum119873

119894=1120601119894(119909119894)+sum119873

119894119895120601119894119895(119909119894119910119895)] (5)



MI119909119910


119909 119892119910 119892119911) where 119892


119911


MI119909119911lt min (MI

119909119910MI119910119911) if 119909 997888rarr 119910 997888rarr 119911







Δ119868 = 119868 (119909 119910 | 119898 = 1) minus 119868 (119909 119910 | 119898 = 0) (7)



119901 (119909 119910) = 120587119901 (119909 119910 | 119898 = 0) + (1 minus 120587) 119901 (119909 119910119898 = 1) (8)


119901 (119909 119910 | 119898 = 0) =

1

2120587

119890minus(12)(119909

2+1199102) (9a)

119901 (119909 119910 | 119898 = 1)

=

1

2120587radic1 minus 1205722

119890minus(12)(119909

2+1199102+2120572119909119910)(1minus120572

2)

(9b)



MicroRNAs

Modulator

Regulator

Target3998400UTR

3998400UTRmRNA119898

mRNA 119910




119875 (119909 = 1 | 119910119898) = 120572119888+ 120572119898119898 + 120572

119910119910 + 120574119898119910 (10)


119898and 120572











119868 = 119868 (119909 119910 | 119898) minus 119868 (119909 119910) (11)























EDG1GIMAP8

ACVR2A

MEOX1P2RY5

ESAM

KLF6

SOX7GSN

PALMDLYVE1

EGFLAM

OLFML2AGIMAP6

CD93

HSPA12B

SH2D3C

ATP8B4

P2RY14

ELMO3

MED29

GUCY1B3

SPNS2

ELTD1

RGS5

GGTA1

FAAH

KIAA1244

HIVEP2

RBP7

CDR1

GIMAP5

CD36ABCA9

LIMS2FOXO1

CYYR1 EBF3

MALLRSU1

FILIP1

KCNJ2 ROBO4

GIMAP1

CFH

LPPR4

C13orf33

C1QTNF7 EMX2OS

C10orf54

CIDEA

VWF

PODNPALLD

CILP

KGFLP1 LRRC32

OMD

ADH1BPLA2G5

C14orf37

WFWBCL6B

CCIN

ADAMTS16

AANAT

KIR3DL1

TPK1

EMILIN2

PLEKHQ1

ACVRL1F13A1

CCL4 PTGDS

BOC

LAYN

TSHZ2

RARRES1

ENPP6

MMP7

KRT6B

DSC1

ARID5BOLFM4

CX3CL1FLJ45803

GABRPDNM3

KCNMB111FAM3D

CPAMD8

SCPEP1PIK3C2G

BCL6

C12orf54

UTRN

IFNGR1IL33

USHBP1

GPBAR1PTH2R

NDRG2

CASQ2MAB21L1

CCL23CCL15CCCCEMP1

CRTAC1

SKIP

TP63

RRAGC

MMRN1

PLEKHF1

GUCY1A3ADCY4

HSD11B1

EBF2

RASD1 UACA

LGI4

ANKRD6

EGR2

RP5-1054A223ELN

GIMAP7

ADIPOQ

TLR4

HAND2

FAM20A

DPT

APDE1B

HHSOCS3

CD248

LCN6

TLL1

RSPO1

CXorf36

RRAD

CRISPLD2

PLB1

DYSF

RASSF222FAM101A

ST5

HSPG2

FGF1

ITGBL1LGI2

DCHS1

PLXDC2

RCN3DACT3SFRP2SPSB1


OR10A5

ACTA2PRICKLE1

XG

FMO1

C16orf30CLEC14A

TCF4C1S

LMAN1L

FBLN1SH3PXD2B

FMO3

MXRA8D22

HTRA1COL8A2

1DACT1

COL16A1

GLT8D2

LMOD1

SPON2

RFTN2

FILIP1L

MFAP5

MMP23BPRRX1 TNFAIP6

LUMCTSKFIBIN

COL6A3MAFB

SFRP4

KERACLDN5RSPO3FGF7

BHLHB3

C16orf77

BST1

SSPN

COL15A1TIE1

ABCC9

CRYABSV2B

LAMA2

SAA1NKAPL

FAM49AANKRD35CHL1

SAA2

SAV1


C2orf40

THSD7APRELP

FRZBMMRN2

ALDH2

RASL12SERPING1

TAGLNLTBP2

CRB3

EPN3

TPD52

AP1M2

KIAA1543

TMC4

TJP3

MB

CDH23ZFP36

CLIC5

KL

FHL5

CCDC24

FAM86A

AVPR1ASTX12

FABP4

PDZRN3

ADH1CPPP1R12B

LASS6

SCARF1

LOC338328


JPH2TCF7L2

TM4SF18HECA

BSPRY

RASEF

SHROOM3

PVRL4

SPINT1

PROM2

SH2D3A

CLDN3

FAAH2

RAB25

LOC124220

RBM35A

C2orf15

PKP3

MARVELD3

FAM84B

VIL2

ELF3TACSTD1

PRSS8 C1orf172

C1orf34

OVOL1

ARL6IP1

MAGIX

CDS1

BIK

FXYD3

GSTO2

IGSF9

CAPN13

TRPS1

OCLN

DNASE2

C1orf210

P2RX4

LSR

C9orf7

EPHA10

AVAVAVAVFAM83H

KIAA1688

LRRC8E

FMOD

MMP11 RUNDC3B

CCDC107CLDN7

KRT19

CBLCCGN

C19orf46SLC44A4

KIAA1324

PRRG2

DDR1

NEBL

TMEM125

ATAD4

LRFN2

MARVELD2

IRX5

CLDN4

LRBA

SPDEF

LOC400451

SPINT2

DLG3

CREB3L4GRHL2

TFAP2A

PSD4

IRF6

RGS13

C1orf186

HDC

CPA3

AQP2

LCE1F

CYP11B1

GRAMD2

CLDN8

GPRIN2

TNN

PDGFD

GIMAP4

GJA12

SHANK3

MAGED2

PTCH2

KLK5PIGR

C4orf7

LGR6

SLC34A2

SELP

APOD

KIF19

PACRG

ZNF446

ZNF574

CREB3HHAT

PEX11B

C10orf81

KRT7

PSEN2

SLC25A44

SEC16A

CRHR1CCDC114

SPINK7

LCE3ETMEM95

OTOF

OR4D1

SSTR3

KLK9

OR10H2

ATN1

CDK5R2FLJ32214

KRT3

C1orf175

PROP1GHSR

PLA2G4D

TRGV5

TIGD5

VPS28

KIAA0143

DPY19L4

POLR2K

COMMD5

DYRK1B

PSORS1C2OR5H1

C10orf27

KRTAP11-1

LOC652968

OR2B11

OR5BF1

OR10P1

MGC40574

P2RX2 RASGRP4

Gene










Putative ceRNAs



Secondary Gol





CADM1

PAPOLAPTPRD

RAB5B

KIAA1522

SENP1

ZC3H11A

RAP2C

KCNE4

DAG1

KIAA0467CXCL14

MBNL1GFAP

CBFBDAXX

TCF4

FOXA1

KBTBD4

WDFY3

ARID4ARAP1GDS1

LEF1PPP3CB

ZFX

RYBPG3BP2

VAV3

FNIP1SMG1

PKN2

PHTF2TAOK1OTUD4

ZNF654 MED13ZNF800

GRM8PAFAH1B1

SRGAP3POMGNT1

GRIA2FAM120A

BMS1P5


TMEM57


C14orf101PCDHAC1

PCDHAC2C1orf9

TEX2

FNBP1L

BCL2L1

PCDHA1PCDHA5

NAV3

BAZ2BTHBS1


KLF9

MED13L

RNF11

ATP7ANFIA

PCDHA3

CRIM1

PAN3CACNA2D2

RAB8B

PPM1B

JAZF1C20orf194

RAPGEF2

FEM1CPCDHA9SFRS12

TGFBR3

ARRDC3

RPGRIP1L

KIAA0240

MAT2AGLCE

FNDC3AREEP1

RANBP6

TNRC6A

CPEB2

ESRRG

ZFPM2

SOCS5

ELL2

ARPP-19

ZNF516

BACE1

ANTXR2

FAM70A FLRT3

CHD9SLC12A2

CSNK1G3DCUN1D4

ATPBD4

PRPF38BUBE3ARBM12

FMR1

RND3PAK7

CIT

C18orf25MUTED

MEIS2

ZNF608CLK2P

C7orf60BRMS1L

ZDHHC17

CTNND2

ACSL1FUSIP1

PIK3C2A

FAM135A

NR3C2

USP15

PTGFRNC5orf13VEZF1

RAPGEF5

THSD3

SOBP

TMEM11

MBNL2

NARG1NRXN1

NCOA2PPP2CA

MAPRE1

SLC24A3

ZADH2

PPFIA2

TMEM115HOMER1

B4GALT2

FOXP1

GH2

PRM1PML

GGT3P

DMRT2

DST

LOC442245MAP1LC3A

RAB14

HIPK1

PTEN MIER3ZFP91

NPAS3 ALCAM

DACH1 SNORD8

ZNF238

GIT1

GOSR1CPEB4

PPP1R12A

TNRC6CCSH1

PITPNBCOL4A3BP

DLG2ATP2B1

ZNF292

RICTORRUNX2

BTRC

ARX

STK35CYB561D1

PIP4K2A

SLC6A10PLYCAT

CD2BP2

PUM2

RERERSBN1 PDS5B

CPEB3VEGFA

CUL3RAP1B

CAMTA1

SIRT1DCUN1D3

KCNJ2

TSHZ3

DTNA

CCNT2

TARDBP

ZFHX3

CNOT6NOVA1SEMA6A

BAGE5

ATRX

RCAN2KIAA0232

EBF1DNAJA2

SATB2LEMD3

C10orf26

LCOR

ZBTB4

ARHGEF17ST7LQKI

SLC12A5

MMP24

REEP2

WDR47DIXDC1

MEF2C

PRKG1LHX6

ARL4AANK2

NRP2

SCN2A

ZFHX4

ABCD4

MAGI2MAPT

PPP6C

HEYL

CFL2

ABHD13

JAG1

USP47

NBEA

SPTY2D1

BSN NHS

BZRAP1

MAPKBP1

SEMA6D

YPEL5

PHF15 DDX3X

CAPN3

NOPE

SLC38A2

UBL3MAGI1

ZSWIM6 CADPS

SMARCAD1

ESR1

(a)

KB

WDFY3

ZFX

G

PAFAH1B1POMGN

FAM120A

ERBB2MAP2K4

NAV3

BAZ2BTHBS1

MYST4

KLF9

MED13L

RNF11

ATP7ANFIA

CRIM1

PAN3CACNA2D2

RAB8B

KIAA0240

MAT2AGLCE

ESR1

FNDC3AREEP1

RANBP6

TNRC6A

CPEB2

ESRRG

ZFPM2

SOCS5

ELL2

ARPP-19

ZNF516

RAB14

HIPK1

PTEN MIER3ZFP91

NPAS3 ALCAM

DACH1

GOSR1TNRC

ZNF292

BT

PUM2RSBN1

CPEB3VEGFA

CUL3

RAP1B

SIRT1DCUN1D3 TSHZ3

DTNA

TARDBP

ZFHX3

CNOT6 NOVA1

SEMA6A

BAGE5

ATRX

F2C

HS

(b)


5 Conclusions









Acknowledgments


References
































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



119901 (119910 119909 | 119898) = 119901 (119910 | 119909119898) 119901 (119909 | 119898) (4)





119901 (119909119894) =

1

119911

119890minus[sum119873

119894=1120601119894(119909119894)+sum119873

119894119895120601119894119895(119909119894119910119895)] (5)



MI119909119910


119909 119892119910 119892119911) where 119892


119911


MI119909119911lt min (MI

119909119910MI119910119911) if 119909 997888rarr 119910 997888rarr 119911







Δ119868 = 119868 (119909 119910 | 119898 = 1) minus 119868 (119909 119910 | 119898 = 0) (7)



119901 (119909 119910) = 120587119901 (119909 119910 | 119898 = 0) + (1 minus 120587) 119901 (119909 119910119898 = 1) (8)


119901 (119909 119910 | 119898 = 0) =

1

2120587

119890minus(12)(119909

2+1199102) (9a)

119901 (119909 119910 | 119898 = 1)

=

1

2120587radic1 minus 1205722

119890minus(12)(119909

2+1199102+2120572119909119910)(1minus120572

2)

(9b)



MicroRNAs

Modulator

Regulator

Target3998400UTR

3998400UTRmRNA119898

mRNA 119910




119875 (119909 = 1 | 119910119898) = 120572119888+ 120572119898119898 + 120572

119910119910 + 120574119898119910 (10)


119898and 120572











119868 = 119868 (119909 119910 | 119898) minus 119868 (119909 119910) (11)























EDG1GIMAP8

ACVR2A

MEOX1P2RY5

ESAM

KLF6

SOX7GSN

PALMDLYVE1

EGFLAM

OLFML2AGIMAP6

CD93

HSPA12B

SH2D3C

ATP8B4

P2RY14

ELMO3

MED29

GUCY1B3

SPNS2

ELTD1

RGS5

GGTA1

FAAH

KIAA1244

HIVEP2

RBP7

CDR1

GIMAP5

CD36ABCA9

LIMS2FOXO1

CYYR1 EBF3

MALLRSU1

FILIP1

KCNJ2 ROBO4

GIMAP1

CFH

LPPR4

C13orf33

C1QTNF7 EMX2OS

C10orf54

CIDEA

VWF

PODNPALLD

CILP

KGFLP1 LRRC32

OMD

ADH1BPLA2G5

C14orf37

WFWBCL6B

CCIN

ADAMTS16

AANAT

KIR3DL1

TPK1

EMILIN2

PLEKHQ1

ACVRL1F13A1

CCL4 PTGDS

BOC

LAYN

TSHZ2

RARRES1

ENPP6

MMP7

KRT6B

DSC1

ARID5BOLFM4

CX3CL1FLJ45803

GABRPDNM3

KCNMB111FAM3D

CPAMD8

SCPEP1PIK3C2G

BCL6

C12orf54

UTRN

IFNGR1IL33

USHBP1

GPBAR1PTH2R

NDRG2

CASQ2MAB21L1

CCL23CCL15CCCCEMP1

CRTAC1

SKIP

TP63

RRAGC

MMRN1

PLEKHF1

GUCY1A3ADCY4

HSD11B1

EBF2

RASD1 UACA

LGI4

ANKRD6

EGR2

RP5-1054A223ELN

GIMAP7

ADIPOQ

TLR4

HAND2

FAM20A

DPT

APDE1B

HHSOCS3

CD248

LCN6

TLL1

RSPO1

CXorf36

RRAD

CRISPLD2

PLB1

DYSF

RASSF222FAM101A

ST5

HSPG2

FGF1

ITGBL1LGI2

DCHS1

PLXDC2

RCN3DACT3SFRP2SPSB1


OR10A5

ACTA2PRICKLE1

XG

FMO1

C16orf30CLEC14A

TCF4C1S

LMAN1L

FBLN1SH3PXD2B

FMO3

MXRA8D22

HTRA1COL8A2

1DACT1

COL16A1

GLT8D2

LMOD1

SPON2

RFTN2

FILIP1L

MFAP5

MMP23BPRRX1 TNFAIP6

LUMCTSKFIBIN

COL6A3MAFB

SFRP4

KERACLDN5RSPO3FGF7

BHLHB3

C16orf77

BST1

SSPN

COL15A1TIE1

ABCC9

CRYABSV2B

LAMA2

SAA1NKAPL

FAM49AANKRD35CHL1

SAA2

SAV1


C2orf40

THSD7APRELP

FRZBMMRN2

ALDH2

RASL12SERPING1

TAGLNLTBP2

CRB3

EPN3

TPD52

AP1M2

KIAA1543

TMC4

TJP3

MB

CDH23ZFP36

CLIC5

KL

FHL5

CCDC24

FAM86A

AVPR1ASTX12

FABP4

PDZRN3

ADH1CPPP1R12B

LASS6

SCARF1

LOC338328


JPH2TCF7L2

TM4SF18HECA

BSPRY

RASEF

SHROOM3

PVRL4

SPINT1

PROM2

SH2D3A

CLDN3

FAAH2

RAB25

LOC124220

RBM35A

C2orf15

PKP3

MARVELD3

FAM84B

VIL2

ELF3TACSTD1

PRSS8 C1orf172

C1orf34

OVOL1

ARL6IP1

MAGIX

CDS1

BIK

FXYD3

GSTO2

IGSF9

CAPN13

TRPS1

OCLN

DNASE2

C1orf210

P2RX4

LSR

C9orf7

EPHA10

AVAVAVAVFAM83H

KIAA1688

LRRC8E

FMOD

MMP11 RUNDC3B

CCDC107CLDN7

KRT19

CBLCCGN

C19orf46SLC44A4

KIAA1324

PRRG2

DDR1

NEBL

TMEM125

ATAD4

LRFN2

MARVELD2

IRX5

CLDN4

LRBA

SPDEF

LOC400451

SPINT2

DLG3

CREB3L4GRHL2

TFAP2A

PSD4

IRF6

RGS13

C1orf186

HDC

CPA3

AQP2

LCE1F

CYP11B1

GRAMD2

CLDN8

GPRIN2

TNN

PDGFD

GIMAP4

GJA12

SHANK3

MAGED2

PTCH2

KLK5PIGR

C4orf7

LGR6

SLC34A2

SELP

APOD

KIF19

PACRG

ZNF446

ZNF574

CREB3HHAT

PEX11B

C10orf81

KRT7

PSEN2

SLC25A44

SEC16A

CRHR1CCDC114

SPINK7

LCE3ETMEM95

OTOF

OR4D1

SSTR3

KLK9

OR10H2

ATN1

CDK5R2FLJ32214

KRT3

C1orf175

PROP1GHSR

PLA2G4D

TRGV5

TIGD5

VPS28

KIAA0143

DPY19L4

POLR2K

COMMD5

DYRK1B

PSORS1C2OR5H1

C10orf27

KRTAP11-1

LOC652968

OR2B11

OR5BF1

OR10P1

MGC40574

P2RX2 RASGRP4

Gene










Putative ceRNAs



Secondary Gol





CADM1

PAPOLAPTPRD

RAB5B

KIAA1522

SENP1

ZC3H11A

RAP2C

KCNE4

DAG1

KIAA0467CXCL14

MBNL1GFAP

CBFBDAXX

TCF4

FOXA1

KBTBD4

WDFY3

ARID4ARAP1GDS1

LEF1PPP3CB

ZFX

RYBPG3BP2

VAV3

FNIP1SMG1

PKN2

PHTF2TAOK1OTUD4

ZNF654 MED13ZNF800

GRM8PAFAH1B1

SRGAP3POMGNT1

GRIA2FAM120A

BMS1P5


TMEM57


C14orf101PCDHAC1

PCDHAC2C1orf9

TEX2

FNBP1L

BCL2L1

PCDHA1PCDHA5

NAV3

BAZ2BTHBS1


KLF9

MED13L

RNF11

ATP7ANFIA

PCDHA3

CRIM1

PAN3CACNA2D2

RAB8B

PPM1B

JAZF1C20orf194

RAPGEF2

FEM1CPCDHA9SFRS12

TGFBR3

ARRDC3

RPGRIP1L

KIAA0240

MAT2AGLCE

FNDC3AREEP1

RANBP6

TNRC6A

CPEB2

ESRRG

ZFPM2

SOCS5

ELL2

ARPP-19

ZNF516

BACE1

ANTXR2

FAM70A FLRT3

CHD9SLC12A2

CSNK1G3DCUN1D4

ATPBD4

PRPF38BUBE3ARBM12

FMR1

RND3PAK7

CIT

C18orf25MUTED

MEIS2

ZNF608CLK2P

C7orf60BRMS1L

ZDHHC17

CTNND2

ACSL1FUSIP1

PIK3C2A

FAM135A

NR3C2

USP15

PTGFRNC5orf13VEZF1

RAPGEF5

THSD3

SOBP

TMEM11

MBNL2

NARG1NRXN1

NCOA2PPP2CA

MAPRE1

SLC24A3

ZADH2

PPFIA2

TMEM115HOMER1

B4GALT2

FOXP1

GH2

PRM1PML

GGT3P

DMRT2

DST

LOC442245MAP1LC3A

RAB14

HIPK1

PTEN MIER3ZFP91

NPAS3 ALCAM

DACH1 SNORD8

ZNF238

GIT1

GOSR1CPEB4

PPP1R12A

TNRC6CCSH1

PITPNBCOL4A3BP

DLG2ATP2B1

ZNF292

RICTORRUNX2

BTRC

ARX

STK35CYB561D1

PIP4K2A

SLC6A10PLYCAT

CD2BP2

PUM2

RERERSBN1 PDS5B

CPEB3VEGFA

CUL3RAP1B

CAMTA1

SIRT1DCUN1D3

KCNJ2

TSHZ3

DTNA

CCNT2

TARDBP

ZFHX3

CNOT6NOVA1SEMA6A

BAGE5

ATRX

RCAN2KIAA0232

EBF1DNAJA2

SATB2LEMD3

C10orf26

LCOR

ZBTB4

ARHGEF17ST7LQKI

SLC12A5

MMP24

REEP2

WDR47DIXDC1

MEF2C

PRKG1LHX6

ARL4AANK2

NRP2

SCN2A

ZFHX4

ABCD4

MAGI2MAPT

PPP6C

HEYL

CFL2

ABHD13

JAG1

USP47

NBEA

SPTY2D1

BSN NHS

BZRAP1

MAPKBP1

SEMA6D

YPEL5

PHF15 DDX3X

CAPN3

NOPE

SLC38A2

UBL3MAGI1

ZSWIM6 CADPS

SMARCAD1

ESR1

(a)

KB

WDFY3

ZFX

G

PAFAH1B1POMGN

FAM120A

ERBB2MAP2K4

NAV3

BAZ2BTHBS1

MYST4

KLF9

MED13L

RNF11

ATP7ANFIA

CRIM1

PAN3CACNA2D2

RAB8B

KIAA0240

MAT2AGLCE

ESR1

FNDC3AREEP1

RANBP6

TNRC6A

CPEB2

ESRRG

ZFPM2

SOCS5

ELL2

ARPP-19

ZNF516

RAB14

HIPK1

PTEN MIER3ZFP91

NPAS3 ALCAM

DACH1

GOSR1TNRC

ZNF292

BT

PUM2RSBN1

CPEB3VEGFA

CUL3

RAP1B

SIRT1DCUN1D3 TSHZ3

DTNA

TARDBP

ZFHX3

CNOT6 NOVA1

SEMA6A

BAGE5

ATRX

F2C

HS

(b)


5 Conclusions









Acknowledgments


References
































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


MicroRNAs

Modulator

Regulator

Target3998400UTR

3998400UTRmRNA119898

mRNA 119910




119875 (119909 = 1 | 119910119898) = 120572119888+ 120572119898119898 + 120572

119910119910 + 120574119898119910 (10)


119898and 120572











119868 = 119868 (119909 119910 | 119898) minus 119868 (119909 119910) (11)























EDG1GIMAP8

ACVR2A

MEOX1P2RY5

ESAM

KLF6

SOX7GSN

PALMDLYVE1

EGFLAM

OLFML2AGIMAP6

CD93

HSPA12B

SH2D3C

ATP8B4

P2RY14

ELMO3

MED29

GUCY1B3

SPNS2

ELTD1

RGS5

GGTA1

FAAH

KIAA1244

HIVEP2

RBP7

CDR1

GIMAP5

CD36ABCA9

LIMS2FOXO1

CYYR1 EBF3

MALLRSU1

FILIP1

KCNJ2 ROBO4

GIMAP1

CFH

LPPR4

C13orf33

C1QTNF7 EMX2OS

C10orf54

CIDEA

VWF

PODNPALLD

CILP

KGFLP1 LRRC32

OMD

ADH1BPLA2G5

C14orf37

WFWBCL6B

CCIN

ADAMTS16

AANAT

KIR3DL1

TPK1

EMILIN2

PLEKHQ1

ACVRL1F13A1

CCL4 PTGDS

BOC

LAYN

TSHZ2

RARRES1

ENPP6

MMP7

KRT6B

DSC1

ARID5BOLFM4

CX3CL1FLJ45803

GABRPDNM3

KCNMB111FAM3D

CPAMD8

SCPEP1PIK3C2G

BCL6

C12orf54

UTRN

IFNGR1IL33

USHBP1

GPBAR1PTH2R

NDRG2

CASQ2MAB21L1

CCL23CCL15CCCCEMP1

CRTAC1

SKIP

TP63

RRAGC

MMRN1

PLEKHF1

GUCY1A3ADCY4

HSD11B1

EBF2

RASD1 UACA

LGI4

ANKRD6

EGR2

RP5-1054A223ELN

GIMAP7

ADIPOQ

TLR4

HAND2

FAM20A

DPT

APDE1B

HHSOCS3

CD248

LCN6

TLL1

RSPO1

CXorf36

RRAD

CRISPLD2

PLB1

DYSF

RASSF222FAM101A

ST5

HSPG2

FGF1

ITGBL1LGI2

DCHS1

PLXDC2

RCN3DACT3SFRP2SPSB1


OR10A5

ACTA2PRICKLE1

XG

FMO1

C16orf30CLEC14A

TCF4C1S

LMAN1L

FBLN1SH3PXD2B

FMO3

MXRA8D22

HTRA1COL8A2

1DACT1

COL16A1

GLT8D2

LMOD1

SPON2

RFTN2

FILIP1L

MFAP5

MMP23BPRRX1 TNFAIP6

LUMCTSKFIBIN

COL6A3MAFB

SFRP4

KERACLDN5RSPO3FGF7

BHLHB3

C16orf77

BST1

SSPN

COL15A1TIE1

ABCC9

CRYABSV2B

LAMA2

SAA1NKAPL

FAM49AANKRD35CHL1

SAA2

SAV1


C2orf40

THSD7APRELP

FRZBMMRN2

ALDH2

RASL12SERPING1

TAGLNLTBP2

CRB3

EPN3

TPD52

AP1M2

KIAA1543

TMC4

TJP3

MB

CDH23ZFP36

CLIC5

KL

FHL5

CCDC24

FAM86A

AVPR1ASTX12

FABP4

PDZRN3

ADH1CPPP1R12B

LASS6

SCARF1

LOC338328


JPH2TCF7L2

TM4SF18HECA

BSPRY

RASEF

SHROOM3

PVRL4

SPINT1

PROM2

SH2D3A

CLDN3

FAAH2

RAB25

LOC124220

RBM35A

C2orf15

PKP3

MARVELD3

FAM84B

VIL2

ELF3TACSTD1

PRSS8 C1orf172

C1orf34

OVOL1

ARL6IP1

MAGIX

CDS1

BIK

FXYD3

GSTO2

IGSF9

CAPN13

TRPS1

OCLN

DNASE2

C1orf210

P2RX4

LSR

C9orf7

EPHA10

AVAVAVAVFAM83H

KIAA1688

LRRC8E

FMOD

MMP11 RUNDC3B

CCDC107CLDN7

KRT19

CBLCCGN

C19orf46SLC44A4

KIAA1324

PRRG2

DDR1

NEBL

TMEM125

ATAD4

LRFN2

MARVELD2

IRX5

CLDN4

LRBA

SPDEF

LOC400451

SPINT2

DLG3

CREB3L4GRHL2

TFAP2A

PSD4

IRF6

RGS13

C1orf186

HDC

CPA3

AQP2

LCE1F

CYP11B1

GRAMD2

CLDN8

GPRIN2

TNN

PDGFD

GIMAP4

GJA12

SHANK3

MAGED2

PTCH2

KLK5PIGR

C4orf7

LGR6

SLC34A2

SELP

APOD

KIF19

PACRG

ZNF446

ZNF574

CREB3HHAT

PEX11B

C10orf81

KRT7

PSEN2

SLC25A44

SEC16A

CRHR1CCDC114

SPINK7

LCE3ETMEM95

OTOF

OR4D1

SSTR3

KLK9

OR10H2

ATN1

CDK5R2FLJ32214

KRT3

C1orf175

PROP1GHSR

PLA2G4D

TRGV5

TIGD5

VPS28

KIAA0143

DPY19L4

POLR2K

COMMD5

DYRK1B

PSORS1C2OR5H1

C10orf27

KRTAP11-1

LOC652968

OR2B11

OR5BF1

OR10P1

MGC40574

P2RX2 RASGRP4

Gene










Putative ceRNAs



Secondary Gol





CADM1

PAPOLAPTPRD

RAB5B

KIAA1522

SENP1

ZC3H11A

RAP2C

KCNE4

DAG1

KIAA0467CXCL14

MBNL1GFAP

CBFBDAXX

TCF4

FOXA1

KBTBD4

WDFY3

ARID4ARAP1GDS1

LEF1PPP3CB

ZFX

RYBPG3BP2

VAV3

FNIP1SMG1

PKN2

PHTF2TAOK1OTUD4

ZNF654 MED13ZNF800

GRM8PAFAH1B1

SRGAP3POMGNT1

GRIA2FAM120A

BMS1P5


TMEM57


C14orf101PCDHAC1

PCDHAC2C1orf9

TEX2

FNBP1L

BCL2L1

PCDHA1PCDHA5

NAV3

BAZ2BTHBS1


KLF9

MED13L

RNF11

ATP7ANFIA

PCDHA3

CRIM1

PAN3CACNA2D2

RAB8B

PPM1B

JAZF1C20orf194

RAPGEF2

FEM1CPCDHA9SFRS12

TGFBR3

ARRDC3

RPGRIP1L

KIAA0240

MAT2AGLCE

FNDC3AREEP1

RANBP6

TNRC6A

CPEB2

ESRRG

ZFPM2

SOCS5

ELL2

ARPP-19

ZNF516

BACE1

ANTXR2

FAM70A FLRT3

CHD9SLC12A2

CSNK1G3DCUN1D4

ATPBD4

PRPF38BUBE3ARBM12

FMR1

RND3PAK7

CIT

C18orf25MUTED

MEIS2

ZNF608CLK2P

C7orf60BRMS1L

ZDHHC17

CTNND2

ACSL1FUSIP1

PIK3C2A

FAM135A

NR3C2

USP15

PTGFRNC5orf13VEZF1

RAPGEF5

THSD3

SOBP

TMEM11

MBNL2

NARG1NRXN1

NCOA2PPP2CA

MAPRE1

SLC24A3

ZADH2

PPFIA2

TMEM115HOMER1

B4GALT2

FOXP1

GH2

PRM1PML

GGT3P

DMRT2

DST

LOC442245MAP1LC3A

RAB14

HIPK1

PTEN MIER3ZFP91

NPAS3 ALCAM

DACH1 SNORD8

ZNF238

GIT1

GOSR1CPEB4

PPP1R12A

TNRC6CCSH1

PITPNBCOL4A3BP

DLG2ATP2B1

ZNF292

RICTORRUNX2

BTRC

ARX

STK35CYB561D1

PIP4K2A

SLC6A10PLYCAT

CD2BP2

PUM2

RERERSBN1 PDS5B

CPEB3VEGFA

CUL3RAP1B

CAMTA1

SIRT1DCUN1D3

KCNJ2

TSHZ3

DTNA

CCNT2

TARDBP

ZFHX3

CNOT6NOVA1SEMA6A

BAGE5

ATRX

RCAN2KIAA0232

EBF1DNAJA2

SATB2LEMD3

C10orf26

LCOR

ZBTB4

ARHGEF17ST7LQKI

SLC12A5

MMP24

REEP2

WDR47DIXDC1

MEF2C

PRKG1LHX6

ARL4AANK2

NRP2

SCN2A

ZFHX4

ABCD4

MAGI2MAPT

PPP6C

HEYL

CFL2

ABHD13

JAG1

USP47

NBEA

SPTY2D1

BSN NHS

BZRAP1

MAPKBP1

SEMA6D

YPEL5

PHF15 DDX3X

CAPN3

NOPE

SLC38A2

UBL3MAGI1

ZSWIM6 CADPS

SMARCAD1

ESR1

(a)

KB

WDFY3

ZFX

G

PAFAH1B1POMGN

FAM120A

ERBB2MAP2K4

NAV3

BAZ2BTHBS1

MYST4

KLF9

MED13L

RNF11

ATP7ANFIA

CRIM1

PAN3CACNA2D2

RAB8B

KIAA0240

MAT2AGLCE

ESR1

FNDC3AREEP1

RANBP6

TNRC6A

CPEB2

ESRRG

ZFPM2

SOCS5

ELL2

ARPP-19

ZNF516

RAB14

HIPK1

PTEN MIER3ZFP91

NPAS3 ALCAM

DACH1

GOSR1TNRC

ZNF292

BT

PUM2RSBN1

CPEB3VEGFA

CUL3

RAP1B

SIRT1DCUN1D3 TSHZ3

DTNA

TARDBP

ZFHX3

CNOT6 NOVA1

SEMA6A

BAGE5

ATRX

F2C

HS

(b)


5 Conclusions









Acknowledgments


References
































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



















EDG1GIMAP8

ACVR2A

MEOX1P2RY5

ESAM

KLF6

SOX7GSN

PALMDLYVE1

EGFLAM

OLFML2AGIMAP6

CD93

HSPA12B

SH2D3C

ATP8B4

P2RY14

ELMO3

MED29

GUCY1B3

SPNS2

ELTD1

RGS5

GGTA1

FAAH

KIAA1244

HIVEP2

RBP7

CDR1

GIMAP5

CD36ABCA9

LIMS2FOXO1

CYYR1 EBF3

MALLRSU1

FILIP1

KCNJ2 ROBO4

GIMAP1

CFH

LPPR4

C13orf33

C1QTNF7 EMX2OS

C10orf54

CIDEA

VWF

PODNPALLD

CILP

KGFLP1 LRRC32

OMD

ADH1BPLA2G5

C14orf37

WFWBCL6B

CCIN

ADAMTS16

AANAT

KIR3DL1

TPK1

EMILIN2

PLEKHQ1

ACVRL1F13A1

CCL4 PTGDS

BOC

LAYN

TSHZ2

RARRES1

ENPP6

MMP7

KRT6B

DSC1

ARID5BOLFM4

CX3CL1FLJ45803

GABRPDNM3

KCNMB111FAM3D

CPAMD8

SCPEP1PIK3C2G

BCL6

C12orf54

UTRN

IFNGR1IL33

USHBP1

GPBAR1PTH2R

NDRG2

CASQ2MAB21L1

CCL23CCL15CCCCEMP1

CRTAC1

SKIP

TP63

RRAGC

MMRN1

PLEKHF1

GUCY1A3ADCY4

HSD11B1

EBF2

RASD1 UACA

LGI4

ANKRD6

EGR2

RP5-1054A223ELN

GIMAP7

ADIPOQ

TLR4

HAND2

FAM20A

DPT

APDE1B

HHSOCS3

CD248

LCN6

TLL1

RSPO1

CXorf36

RRAD

CRISPLD2

PLB1

DYSF

RASSF222FAM101A

ST5

HSPG2

FGF1

ITGBL1LGI2

DCHS1

PLXDC2

RCN3DACT3SFRP2SPSB1


OR10A5

ACTA2PRICKLE1

XG

FMO1

C16orf30CLEC14A

TCF4C1S

LMAN1L

FBLN1SH3PXD2B

FMO3

MXRA8D22

HTRA1COL8A2

1DACT1

COL16A1

GLT8D2

LMOD1

SPON2

RFTN2

FILIP1L

MFAP5

MMP23BPRRX1 TNFAIP6

LUMCTSKFIBIN

COL6A3MAFB

SFRP4

KERACLDN5RSPO3FGF7

BHLHB3

C16orf77

BST1

SSPN

COL15A1TIE1

ABCC9

CRYABSV2B

LAMA2

SAA1NKAPL

FAM49AANKRD35CHL1

SAA2

SAV1


C2orf40

THSD7APRELP

FRZBMMRN2

ALDH2

RASL12SERPING1

TAGLNLTBP2

CRB3

EPN3

TPD52

AP1M2

KIAA1543

TMC4

TJP3

MB

CDH23ZFP36

CLIC5

KL

FHL5

CCDC24

FAM86A

AVPR1ASTX12

FABP4

PDZRN3

ADH1CPPP1R12B

LASS6

SCARF1

LOC338328


JPH2TCF7L2

TM4SF18HECA

BSPRY

RASEF

SHROOM3

PVRL4

SPINT1

PROM2

SH2D3A

CLDN3

FAAH2

RAB25

LOC124220

RBM35A

C2orf15

PKP3

MARVELD3

FAM84B

VIL2

ELF3TACSTD1

PRSS8 C1orf172

C1orf34

OVOL1

ARL6IP1

MAGIX

CDS1

BIK

FXYD3

GSTO2

IGSF9

CAPN13

TRPS1

OCLN

DNASE2

C1orf210

P2RX4

LSR

C9orf7

EPHA10

AVAVAVAVFAM83H

KIAA1688

LRRC8E

FMOD

MMP11 RUNDC3B

CCDC107CLDN7

KRT19

CBLCCGN

C19orf46SLC44A4

KIAA1324

PRRG2

DDR1

NEBL

TMEM125

ATAD4

LRFN2

MARVELD2

IRX5

CLDN4

LRBA

SPDEF

LOC400451

SPINT2

DLG3

CREB3L4GRHL2

TFAP2A

PSD4

IRF6

RGS13

C1orf186

HDC

CPA3

AQP2

LCE1F

CYP11B1

GRAMD2

CLDN8

GPRIN2

TNN

PDGFD

GIMAP4

GJA12

SHANK3

MAGED2

PTCH2

KLK5PIGR

C4orf7

LGR6

SLC34A2

SELP

APOD

KIF19

PACRG

ZNF446

ZNF574

CREB3HHAT

PEX11B

C10orf81

KRT7

PSEN2

SLC25A44

SEC16A

CRHR1CCDC114

SPINK7

LCE3ETMEM95

OTOF

OR4D1

SSTR3

KLK9

OR10H2

ATN1

CDK5R2FLJ32214

KRT3

C1orf175

PROP1GHSR

PLA2G4D

TRGV5

TIGD5

VPS28

KIAA0143

DPY19L4

POLR2K

COMMD5

DYRK1B

PSORS1C2OR5H1

C10orf27

KRTAP11-1

LOC652968

OR2B11

OR5BF1

OR10P1

MGC40574

P2RX2 RASGRP4

Gene










Putative ceRNAs



Secondary Gol





CADM1

PAPOLAPTPRD

RAB5B

KIAA1522

SENP1

ZC3H11A

RAP2C

KCNE4

DAG1

KIAA0467CXCL14

MBNL1GFAP

CBFBDAXX

TCF4

FOXA1

KBTBD4

WDFY3

ARID4ARAP1GDS1

LEF1PPP3CB

ZFX

RYBPG3BP2

VAV3

FNIP1SMG1

PKN2

PHTF2TAOK1OTUD4

ZNF654 MED13ZNF800

GRM8PAFAH1B1

SRGAP3POMGNT1

GRIA2FAM120A

BMS1P5


TMEM57


C14orf101PCDHAC1

PCDHAC2C1orf9

TEX2

FNBP1L

BCL2L1

PCDHA1PCDHA5

NAV3

BAZ2BTHBS1


KLF9

MED13L

RNF11

ATP7ANFIA

PCDHA3

CRIM1

PAN3CACNA2D2

RAB8B

PPM1B

JAZF1C20orf194

RAPGEF2

FEM1CPCDHA9SFRS12

TGFBR3

ARRDC3

RPGRIP1L

KIAA0240

MAT2AGLCE

FNDC3AREEP1

RANBP6

TNRC6A

CPEB2

ESRRG

ZFPM2

SOCS5

ELL2

ARPP-19

ZNF516

BACE1

ANTXR2

FAM70A FLRT3

CHD9SLC12A2

CSNK1G3DCUN1D4

ATPBD4

PRPF38BUBE3ARBM12

FMR1

RND3PAK7

CIT

C18orf25MUTED

MEIS2

ZNF608CLK2P

C7orf60BRMS1L

ZDHHC17

CTNND2

ACSL1FUSIP1

PIK3C2A

FAM135A

NR3C2

USP15

PTGFRNC5orf13VEZF1

RAPGEF5

THSD3

SOBP

TMEM11

MBNL2

NARG1NRXN1

NCOA2PPP2CA

MAPRE1

SLC24A3

ZADH2

PPFIA2

TMEM115HOMER1

B4GALT2

FOXP1

GH2

PRM1PML

GGT3P

DMRT2

DST

LOC442245MAP1LC3A

RAB14

HIPK1

PTEN MIER3ZFP91

NPAS3 ALCAM

DACH1 SNORD8

ZNF238

GIT1

GOSR1CPEB4

PPP1R12A

TNRC6CCSH1

PITPNBCOL4A3BP

DLG2ATP2B1

ZNF292

RICTORRUNX2

BTRC

ARX

STK35CYB561D1

PIP4K2A

SLC6A10PLYCAT

CD2BP2

PUM2

RERERSBN1 PDS5B

CPEB3VEGFA

CUL3RAP1B

CAMTA1

SIRT1DCUN1D3

KCNJ2

TSHZ3

DTNA

CCNT2

TARDBP

ZFHX3

CNOT6NOVA1SEMA6A

BAGE5

ATRX

RCAN2KIAA0232

EBF1DNAJA2

SATB2LEMD3

C10orf26

LCOR

ZBTB4

ARHGEF17ST7LQKI

SLC12A5

MMP24

REEP2

WDR47DIXDC1

MEF2C

PRKG1LHX6

ARL4AANK2

NRP2

SCN2A

ZFHX4

ABCD4

MAGI2MAPT

PPP6C

HEYL

CFL2

ABHD13

JAG1

USP47

NBEA

SPTY2D1

BSN NHS

BZRAP1

MAPKBP1

SEMA6D

YPEL5

PHF15 DDX3X

CAPN3

NOPE

SLC38A2

UBL3MAGI1

ZSWIM6 CADPS

SMARCAD1

ESR1

(a)

KB

WDFY3

ZFX

G

PAFAH1B1POMGN

FAM120A

ERBB2MAP2K4

NAV3

BAZ2BTHBS1

MYST4

KLF9

MED13L

RNF11

ATP7ANFIA

CRIM1

PAN3CACNA2D2

RAB8B

KIAA0240

MAT2AGLCE

ESR1

FNDC3AREEP1

RANBP6

TNRC6A

CPEB2

ESRRG

ZFPM2

SOCS5

ELL2

ARPP-19

ZNF516

RAB14

HIPK1

PTEN MIER3ZFP91

NPAS3 ALCAM

DACH1

GOSR1TNRC

ZNF292

BT

PUM2RSBN1

CPEB3VEGFA

CUL3

RAP1B

SIRT1DCUN1D3 TSHZ3

DTNA

TARDBP

ZFHX3

CNOT6 NOVA1

SEMA6A

BAGE5

ATRX

F2C

HS

(b)


5 Conclusions









Acknowledgments


References
































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






Putative ceRNAs



Secondary Gol





CADM1

PAPOLAPTPRD

RAB5B

KIAA1522

SENP1

ZC3H11A

RAP2C

KCNE4

DAG1

KIAA0467CXCL14

MBNL1GFAP

CBFBDAXX

TCF4

FOXA1

KBTBD4

WDFY3

ARID4ARAP1GDS1

LEF1PPP3CB

ZFX

RYBPG3BP2

VAV3

FNIP1SMG1

PKN2

PHTF2TAOK1OTUD4

ZNF654 MED13ZNF800

GRM8PAFAH1B1

SRGAP3POMGNT1

GRIA2FAM120A

BMS1P5


TMEM57


C14orf101PCDHAC1

PCDHAC2C1orf9

TEX2

FNBP1L

BCL2L1

PCDHA1PCDHA5

NAV3

BAZ2BTHBS1


KLF9

MED13L

RNF11

ATP7ANFIA

PCDHA3

CRIM1

PAN3CACNA2D2

RAB8B

PPM1B

JAZF1C20orf194

RAPGEF2

FEM1CPCDHA9SFRS12

TGFBR3

ARRDC3

RPGRIP1L

KIAA0240

MAT2AGLCE

FNDC3AREEP1

RANBP6

TNRC6A

CPEB2

ESRRG

ZFPM2

SOCS5

ELL2

ARPP-19

ZNF516

BACE1

ANTXR2

FAM70A FLRT3

CHD9SLC12A2

CSNK1G3DCUN1D4

ATPBD4

PRPF38BUBE3ARBM12

FMR1

RND3PAK7

CIT

C18orf25MUTED

MEIS2

ZNF608CLK2P

C7orf60BRMS1L

ZDHHC17

CTNND2

ACSL1FUSIP1

PIK3C2A

FAM135A

NR3C2

USP15

PTGFRNC5orf13VEZF1

RAPGEF5

THSD3

SOBP

TMEM11

MBNL2

NARG1NRXN1

NCOA2PPP2CA

MAPRE1

SLC24A3

ZADH2

PPFIA2

TMEM115HOMER1

B4GALT2

FOXP1

GH2

PRM1PML

GGT3P

DMRT2

DST

LOC442245MAP1LC3A

RAB14

HIPK1

PTEN MIER3ZFP91

NPAS3 ALCAM

DACH1 SNORD8

ZNF238

GIT1

GOSR1CPEB4

PPP1R12A

TNRC6CCSH1

PITPNBCOL4A3BP

DLG2ATP2B1

ZNF292

RICTORRUNX2

BTRC

ARX

STK35CYB561D1

PIP4K2A

SLC6A10PLYCAT

CD2BP2

PUM2

RERERSBN1 PDS5B

CPEB3VEGFA

CUL3RAP1B

CAMTA1

SIRT1DCUN1D3

KCNJ2

TSHZ3

DTNA

CCNT2

TARDBP

ZFHX3

CNOT6NOVA1SEMA6A

BAGE5

ATRX

RCAN2KIAA0232

EBF1DNAJA2

SATB2LEMD3

C10orf26

LCOR

ZBTB4

ARHGEF17ST7LQKI

SLC12A5

MMP24

REEP2

WDR47DIXDC1

MEF2C

PRKG1LHX6

ARL4AANK2

NRP2

SCN2A

ZFHX4

ABCD4

MAGI2MAPT

PPP6C

HEYL

CFL2

ABHD13

JAG1

USP47

NBEA

SPTY2D1

BSN NHS

BZRAP1

MAPKBP1

SEMA6D

YPEL5

PHF15 DDX3X

CAPN3

NOPE

SLC38A2

UBL3MAGI1

ZSWIM6 CADPS

SMARCAD1

ESR1

(a)

KB

WDFY3

ZFX

G

PAFAH1B1POMGN

FAM120A

ERBB2MAP2K4

NAV3

BAZ2BTHBS1

MYST4

KLF9

MED13L

RNF11

ATP7ANFIA

CRIM1

PAN3CACNA2D2

RAB8B

KIAA0240

MAT2AGLCE

ESR1

FNDC3AREEP1

RANBP6

TNRC6A

CPEB2

ESRRG

ZFPM2

SOCS5

ELL2

ARPP-19

ZNF516

RAB14

HIPK1

PTEN MIER3ZFP91

NPAS3 ALCAM

DACH1

GOSR1TNRC

ZNF292

BT

PUM2RSBN1

CPEB3VEGFA

CUL3

RAP1B

SIRT1DCUN1D3 TSHZ3

DTNA

TARDBP

ZFHX3

CNOT6 NOVA1

SEMA6A

BAGE5

ATRX

F2C

HS

(b)


5 Conclusions









Acknowledgments


References
































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology







Acknowledgments


References
































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

research article gene regulation, modulation, and their...

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