Qabaja et al. Journal of Cheminformatics 2014, 6:1http://www.jcheminf.com/content/6/1/1
RESEARCH ARTICLE Open Access
Prediction of novel drug indications usingnetwork driven biological data prioritizationand integrationAla Qabaja1,4, Mohammed Alshalalfa1,3*, Eisa Alanazi2,6 and Reda Alhajj1,5
Abstract
Background: With the rapid development of high-throughput genomic technologies and the accumulation ofgenome-wide datasets for gene expression profiling and biological networks, the impact of diseases and drugs ongene expression can be comprehensively characterized. Drug repositioning offers the possibility of reduced risks inthe drug discovery process, thus it is an essential step in drug development.
Results: Computational prediction of drug-disease interactions using gene expression profiling datasets andbiological networks is a new direction in drug repositioning that has gained increasing interest. We developed acomputational framework to build disease-drug networks using drug- and disease-specific subnetworks. Theframework incorporates protein networks to refine drug and disease associated genes and prioritize genes in diseaseand drug specific networks. For each drug and disease we built multiple networks using gene expression profiling andtext mining. Finally a logistic regression model was used to build functional associations between drugs and diseases.
Conclusions: We found that representing drugs and diseases by genes with high centrality degree in gene networksis the most promising representation of drug or disease subnetworks.
Keywords: Disease, Drug, Gene, Protein networks
BackgroundThe development of many methods that enable the iso-lation and study of individual cells and molecules hasrevolutionized the process of drug discovery from beingat the physiological level to more the accurate molecularlevel. This revolution was all due to the genome sequenc-ing project that provides a complete list of genes andgene products and enables the simultaneous monitoringof the expression of the whole genome. Consequently,this technology has shed light on possible computationaltechniques for investigating new therapeutic applicationsfor already approved drugs or other safe drug candidatesin what is called drug repositioning. By definition, drugrepositioning techniques ignore the first testing phases,
*Correspondence: [email protected] of Computer Science, University of Calgary, Calgary, Alberta,Canada3Biotechnology Research Center, Palestine Polytechnic University, Hebron,PalestineFull list of author information is available at the end of the article
that might take a decade and cost more than 1$ billion,and progresses directly to drug applications [1]. This strat-egy certainly has the potential of being the most efficienttechnique for drug discovery since it provides reduceddevelopment costs and shorter paths to approval [2].Computational prediction of drug-disease associations
has become one of the leading approaches to drug-diseasetreatment investigation. Network and systems biologyenable a better understanding for drug discovery by con-sidering a global physiological environment of proteintargets. Thus network biology has played a central role indeveloping efficacious therapies that alter entire pathwaysrather than single proteins, resulting in the potential forfighting complex multifactorial diseases [3]. This findingconfirms that medicine is no exception to the mathemat-ical system theory that states the scale and complexity ofthe solution should match the scale and complexity of theproblem. It seems clear that therapies modulating a sin-gle target yield nothing but minor alteration of a diseasescomplex machinery. Therefore for the past few years the
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focus to fight complex diseases has been on network cen-tric but not gene centric [4] modules. Out of the differentapproaches and data sources that have been used for drugrepositioning, microarrays and text mining have beenthe most prevalent. Gene expression microarrays havebeen broadly and successfully used to study the molecularpathophysiology of diseases [5-8] and drugmode of action[9-12]. Noteworthy that most of these approaches werebased on gene set enrichment (GSEA) statistical tech-niques [13]. For instance, Lamb et al. [10] studied hun-dreds of molecules over different cell lines, drug doses andexposure time slots. This approach has enabled Lamb andcolleagues to create ranked lists of genes for each sampleand finally to use GSEA to build associations from differ-ent molecules. Similarly Iorio et al. [12] used a mergingprocedure to merge all the ranked lists related to a partic-ular drug into one representative ranked list of genes forthe drug. Finally they applied GSEA to build a drug-drugnetwork based on the same concept. On the other hand,there were many attempts to prioritize disease-associatedgenes by integrating microarray expression profiles andnetwork data [14-17]. As described by Wu et al. [17]these techniques can be sorted into three major cate-gories. The first uses microarray data and t-tests to findpossible differentially expressed genes (DEG). Later on ituses a gene network in order to prioritize genes that aresurrounded by DEGs [14]. The second technique consid-ers the dynamic changes in interactions of the candidategene with other genes in the compared samples (normaland disease samples), which has been done by defininghubs from a protein-protein interaction network (PPIN)and checking if the hub and its neighbors are co-expressedtogether in different tissues [15]. The third technique con-siders variations of gene interactions between comparedsamples and their effects on gene expression to prior-itize disease-associated genes [16]. More specifically, itdefines the set of DEG together with a manually curatedset of transcription regulators (TRs). Later, the differencein coexpression between DEGs and TRs is computed inthe compared conditions. This difference is used for com-putation of differential wiring that is going to be used forprioritization purposes.In addition to microarray expression profiles, many
text mining based tools and biological systems have beensuccessfully developed to connect and prioritize genes,diseases and drugs. Some of these approaches use pattern-based recognition techniques [18] and others inte-grate protein-protein networks for prioritization purposes[19-21]. For instance, Cheng et al. [18] have developeda web-based text mining system called PolySearch forextracting relationships between human diseases, genes,mutations, drugs and metabolites. PolySearch employsa text ranking scheme to score the most relevant sen-tences and abstracts that associate both the query and
match terms with each other. Li et al. [19] proposeda paradigm that integrates molecular interaction net-work mining and text mining techniques. The proposedparadigm starts by incorporating disease-specific seedgenes/proteins derived from prior knowledge. This seedof genes is improved by expanding and re-ranking them inthe functional context by reprioritizing them in disease-related molecular interaction networks. To avoid theproblem of being biased towards the initial set of genes,OzgÃijr et al. [21] developed a framework that integratesa text-mining curated protein-protein network that isrelated to a particular disease with social network analy-sis centrality measures to predict unknown disease-geneassociations. The authors used sentence parsing in orderto build a syntactic parse tree representing the syntacticconstituent structure of a sentence and to build a protein-protein network from this tree. After building the diseasespecific protein-protein network, the authors consideredall the seed genes in addition to their neighbors for furtheranalysis. Finally to prioritize genes related to a particu-lar disease, they used degree, eigenvector, betweennessand closeness network centrality metrics. It is notewor-thy that some chemical structure similarity approacheswere used in drug repositioning in addition to the text-mining andmicroarray based approaches. An outstandingpaper in this field was the one by Gottlieb et al. [22].Their approach was designed to directly predict drug-disease associations including both FDA approved drugsand other molecules in the experimental phase. Theiralgorithm works in three phases: (i) building five drug-drug similarity measures and two disease-disease similar-ity measures; (ii) building classification features and sub-sequent learning classification rule that can distinguishbetween true and false drug-disease associations by usingthese similarity measures; and (iii) applying a logisticregression classifier to predict any new possible drug-disease associations. Thus for a given drug-disease associ-ation from the gold standard (experimentally curated listof drug-disease interactions), the authors computed anassociation score by considering all the other known drug-disease association. Even though this technique attainedhigh sensitivity and specificity in cross-validation experi-ments, it is not without limitations. Firstly, the proposedmethod used 5 different drug similarity measures and3 different disease similarity measures. This makes itbiased to include a drug without having its chemical struc-ture, side effects, target sequence, target PPIN and itstarget gene ontology. The same thing is applicable to dis-eases. Furthermore this method does not consider thesimilarity between drugs molecular actions. It only con-siders the known targets for drugs to define similarities.Sometimes there might be hidden or unknown drug tar-gets that are not considered in this study resulting in abias since drugs trigger their action on target genes and
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have a consequent effect on other off-target genes. Fromthe approaches described above, one can conclude thatmicroarray expression profiles mining, text mining andbiological network analysis are very robust techniqueswhen it comes to connecting biological entities (drugs,diseases and genes). In this work, we will study, analyzeand try to predict drug-disease associations in amore con-textualized view that is provided by network biology. Thisstrategy will propel their association from the classicalempiricism to a pathway-based rational design of globaltherapies. The main goal of this work is to identify a setof genes that are prioritized according to their relevancyto a particular disease or drug and then use these associ-ations to build a drug-disease association network. Thusinstead of identifying disease related or drug related genesfrom an expert-curated source, we will be utilizing textmining and microarray data as genes associated with acomplex disease pathway are not all identified. Moreover,many of these genes and proteins are still under investiga-tion for potential values as disease biomarkers. The initialset of genes extracted from each source will be furtherextended from a source specific network, once by utiliz-ing a microarray based network and the other by utilizinga text-mining based network, by including their directneighbors for further analysis. By doing this network-based approach, each drug or disease will be representedby a subnetwork where edges represent an interactionand nodes represent the set of seed genes for that par-ticular drug/disease and their direct neighbors. Later on,genes from each subnetwork will be reprioritized accord-ing to their centrality measures in that subnetwork. Finallya lasso regression model is used to predict drug-diseaseassociations by using drug-gene and disease-gene inter-action networks using three different sources: microarraydata, text-mining data and finally an integrative sourcethat combines information from these two sources. A gen-eral description for the proposed method is described inFigure 1.
MethodsDefining the initial set of genesIn an experiment to quantitatively assess the druggablepotential of the human genome, conceivable results indi-cated that only 10% of the genes in human genomeare considered drug targets, 10% are involved in dis-ease pathophysiology and only 5% are both druggableand relevant to disease [23]. We assumed that includ-ing only genes that are related to a drugs mode of actionor a diseases pathophysiology can save processing timeand memory by excluding irrelevant genes from fur-ther analysis. Particularly, we used DrugBank database[24] to include all targets of our drug set and OMIMdatabase to include genes that are involved in diseasepathophysiology.
Refining gene lists using protein networksTo guarantee that we had selected a robust function setof genes for drugs and diseases, we included other func-tionally related genes, thereby extending our understand-ing for drug mode of action or disease pathophysiology.For this purpose we used functional protein interac-tions from Reactome database [25] in order to extractall the other genes that are functionally related (directneighbors) to our seed list of genes. In the context ofthis work, we will refer to these lists as DiseaseExt andDrugExt for the extended lists of diseases and drugs,respectively.
Prioritizing genes using microarray and text mining dataIn this section we describe two directions we followedto prioritize DiseaseExt and DrugExt genes. In the firstapproach, we used microarray expression data of cellstreated with drugs and diseases to rank genes basedon their differential expression capability. In the secondapproach we used text mining techniques to rank genesbased on the frequency of their co-occurrence with dis-eases or drugs.
Prioritizing gene lists based onmicroarray gene expressionTwo different databases were used to generate microar-ray based drug-gene and disease-gene interactions. Fordrugs, we used the Connectivity Map website [26] thatcontains 6100 ranked lists of genes for 1300 chemical sub-stances. Note that ranking scores for genes are based ontheir differential expression between untreated and drugtreated samples. So for a set of n genes the most positivelyexpressed gene was given a rank of 1 and the most nega-tively expressed gene was given a rank of n. We extractedthese ranked lists and merged repeated samples for a par-ticular drug as has been described by Iorio [12]. Thuswe ended up having a representative list of each of theremaining 406 drugs after excluding chemical substancesthat are not recognized in the DrugBank database [24].We extracted the rank values for DrugExt genes and nor-malized rank scores for each gene relevant to a particulardrug according to this list. Finally the 25 lowest and the 25highest ranked genes for each drug were selected to rep-resent the initial set of genes to a build drug-specific genenetwork. We will refer to these sets as Mir-DrugExt. Fordiseases, we used the Gene Expression Omnibus (GEO)repository to generate microarray data for disease samplesand control samples. To select datasets, it was essentialin this experiment to select disease expression profilesthat were generated using Human Affymetrix platformto make it consistent with the experiments generated fordrugs and avoid any possible platform-specific bias. Alsoit was essential to include a set of diseases with .CLEraw files uploaded since we planned to normalize exper-iments with the same normalization algorithm. This set
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Figure 1 General framework for building drug-disease associations. This figure shows the general framework for our proposed paradigm. Steps1 and 2 were used to extract the initial set of genes. Steps 3.1 and 3.2 extracted drug-gene and disease-gene co-occurrences, respectively. Steps 3.3and 3.4 extracted drugmicroarray and diseasemicroarray data respectively. In step 4 we found the ranks of genes related to a specific drug or disease.In step 5 we built the drug-specific and disease-specific gene-gene network. In step 6 we ran prioritization procedures as to launch drug-gene anddisease-gene interaction networks into a lasso regression. In step 7 we used lasso regression model to build drug-disease associations.
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of diseases was selected by manually browsing GEO fordisease experiments that satisfy the mentioned criteria.This browsing process was done by two bioinformati-cians and lasted for two weeks, resulting in a set of 24diseases. CLE files for the 24 diseases were collected inde-pendently and RMA normalization algorithm [27] wasused in order to normalize data. We extracted the geneexpression profiles for the DiseaseExt gene set by find-ing a corresponding probe-set in microarray expressionprofiles. Note that, the average expression profiles for allprobe-sets have been taken for genes that are representedby more than one corresponding probe-set. Later on, weused significant analysis of microarray or SAM technique[28] in order to identify a differential expression score forevery single gene, and the genes were ranked according totheir scores from 1 to n. SAM assigns a score based onchanges related to standard deviations of some randomlygenerated measurements of a particular gene. This scoreranges between a high positive indicating that the genehas been up-regulated upon comparison between healthyand diseased samples, and a high negative score indicatingthat the gene has been down-regulated upon compari-son between healthy and diseased samples. Finally the 25lowest and 25 highest ranked genes for each disease wereselected to represent the initial set of genes to build adisease-specific gene network. We will refer to these setsas Mir-DiseaseExt.
Prioritizing gene lists based on PubMed abstractsTo prioritize genes for each drug and disease, we foundco-occurrences between these biological entities usingPubMed abstracts. More specifically, we queried PubMeddatabase to check the co-occurrences of every singledisease/drug with every single gene in DiseaseExt andDrugExt. It is noteworthy that we considered all possi-ble annotations or MeSH terms for specific disease, drugor gene. Since co-occurrences can be vulnerable to falsepositives, we set to zero any drug-gene or disease-geneco-occurrence that was less than 5. After defining theseco-occurrences we used regularized a log odd ratio con-nectivity measure to reflect the strength of the ties inour drug-gene and disease-gene co-occurrence matrices.The resulting score yielded a positive value for enricheddrug-gene or drug-gene pairs and a negative value forunderrepresented pairs. As described previously [19], theconnectivity between a particular drug or disease D and agene G or ConnectDG can be computed according to thefollowing formula:
ConnectDG = ln(ABSDG∗N+λ)−ln(ABSG∗ABSD+λ)
(1)
Where ABSDG is the total number of abstracts in whichdrug or disease D and gene G were co-mentioned
together. ABSG and ABSD is the number of abstracts inwhich gene G and drug or disease D was mentioned,respectively. N is the size of all tested abstracts. λ is a smallconstant that has been added to avoid out of bound errorsin case any of ABSDG, ABSG, or ABSD values were zero.The only concern with using this formula is that the Nterm in our case is very big (all abstracts in PubMed), thusmaking score biased toward the left hand side of the for-mula. On the other hand, using any small reasonable valueto replace N would make the score biased toward the righthand side of the formula. Therefore we sought to mod-ify both sides to fit our analysis according to the followingformula:
ConnectDG = ln(ABSDG ∗ max(ABSD,ABSG) + λ)
− ln(ABSD + ABSG + λ)
(2)
And we set λ to 1 in all cases. Finally we included allgenes with a positive ConnectDG score relevant to a par-ticular disease or drug. We will refer to these gene setsas Txt-DiseaseExt and Txt-DrugExt for diseases and drugsrespectively.
Generating disease-specific and drug-specific genemodule signaturesAfter we refined the DiseaseExt and DrugExt gene setsusing microarray and text mining techniques, we soughtto find gene subnetworks (gene modules) to representeach drug and disease. As described above, our majorgoal was to utilize the information that is stored in bio-logical networks and thus focus our attention on net-work topological features to predict drug indications. Weintended to generate two subnetworks for every singledrug or disease using two different sources of informa-tion: microarray expression profiles (Mir-DiseaseExt andMir-DrugExt) and text mining data (Txt-DiseaseExt andTxt-DrugExt). To generate the text-mining based subnet-works we first extracted a comprehensive network thatrepresents gene-gene interactions from text papers. Morespecifically we used the whole set of genes we wereworking on to query STRING web server [29]. STRINGserver stores a huge gene-gene network derived fromfour different sources: genomic context, high throughputtechnology, co-expression and text mining. We extractedtext mining based interactions between Txt-DiseaseExtand Txt-DrugExt genes for each disease and each drugrespectively. We will use the terms TxtNet-DiseaseExtand TxtNet-DrugExt to refer to these interactions. Weused a similar methodology in order to generate themicroarray based subnetworks. The only difference waswith generating the comprehensive network that repre-sents the interactions between all genes. Since microar-rays measure the level of expression between genes
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and can be utilized to understand functional relation-ship between genes, we sought to use a functionalgene-gene network to generate microarray based net-work. For this purpose, we extracted the interactionsbetween our set of genes (Mir-DiseaseExt and Mir-DrugExt) both from a functional protein-protein network[25] and a signaling network [30]. We will use MirNet-DiseaseExt and MirNet-DrugExt to refer to functionalprotein interactions specific to each disease and drugrespectively. The whole process of generating disease-specific subnetworks is described in Figure 2. Note thateverything in Figure 2 applies to finding drug-specificsubnetworks.
Using logistic regression to build drug-disease associationsLogistic regression measures the relationship between abinary response variable (Disease-gene network) and oneor more predictor variables (drug-gene networks). Weused logistic regression modeling in this work as theresponse variables, which represent association betweendiseases and genes, are binary. To model this problem asa regression model, we write the disease gene networkas a linear combination of the drug-gene subnetworks. Inother words, we consider that multiple drugs can have aneffect on the genes associated with diseases.
Prioritization of genes in drug and disease specificsubnetworksAfter generating the disease-specific and drug-specificgene-gene networks we ran a prioritization process thatis based on different centrality measures; namely, degreecentrality, closeness centrality and betweenness centrality.The Gephi tool [31] was used to compute these measuresfor all subnetworks generated for the set of diseases anddrugs. Finally for each drug/disease we only consideredgenes that have a centrality score greater than the averagecentrality score among all other genes. Thus we built sixdifferent drug-gene and disease-gene Boolean interactionnetworks by utilizing the two subnetworks (text-miningbased and microarray based) extracted for each drug anddisease. More specifically, for each drug/disease, we useda text-mining based subnetwork to build three drug-geneor disease-gene interaction networks using the three pri-oritization techniques and did the same to build anotherthree microarray based networks. These networks werethen entered into a logistic regression model to producesix different drug-disease interactions as being shown inFigure 3.
Evaluating the performance of the frameworkTo evaluate the performance of the integrative frame-work, we constructed a gold standard disease-drug net-work from PolySearch server [18]. This gold standard
contains 474 positive interactions between 22 diseasesand 406 drugs. To generate negative interactions, weselected 400 interactions between disease and drugs thathave 0 co-occurrence in PubMed abstracts. We com-pared the performance of each drug subnetwork to predictdisease subnetworks with the gold standard. We usedReceiver Operating Characteristics (ROC) curve analysisto produce AUC values to assess the performance of eachsubnetwork.
ResultsSelecting a robust set of genes for drugs and diseasesWe first selected 571 genes that are targeted by at leastone drug in the DrugBank database. We also extracted820 genes that are associated with a disease according tothe OMIM database. To refine these two sets of genes,we incorporated protein networks at this stage. There-fore the final list contained 2343 genes. To prioritizethese lists of genes for each drug and disease, we fol-lowed two approaches. The first approach prioritized thegenes based on their differential expression behavior incells treated with the drug or in disease samples comparedto normal samples. Only the top 50 (25 most upregu-lated and 25 most downregulated) were selected at thisstage. The second prioritization method was based on co-occurrence rate between drugs and genes or disease andgenes in PubMed abstracts. Drug-gene and disease-genepairs with high co-occurrence rate were filtered at the nextstage.
Constructing disease-gene and drug-gene interactionsTo predict interactions between diseases and drugs, wefirst built functional interactions between diseases andgenes from one side and drugs and genes from anotherside. To build each network we followed a systematicintegrative approach that incorporates protein networksat several steps in the methodology. We built drug-geneinteractions using both text mining (TxtNet-DrugExt)and microarray data (MirNet-DrugExt). Similarly, webuilt disease-gene interactions using text-mining (TxtNet-DiseaseExt) and microarray (MirNet-DiseaseExt). For themicroarray based networks, we incorporated functionalprotein networks to extract the gene interacting with thetop 50 genes representing each drug and disease. As aresult, for each drug and disease, we obtained a list offunctionally interacting genes to represent drug or dis-ease subnetworks. For the text mining based networks,we incorporated a gene-gene network extracted fromSTRING database, and then extracted genes linked withthe genes which? co-occurred with the drugs or diseases.Finally, for each of these networks, we calculated threecentrality measures (degree, betweenness, closeness) ofthe genes in each network and then selected the genes
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Figure 2 Generating a disease-specific and drug-specific gene-gene network. This figure shows the process of generating the drug-specificand disease-specific gene-gene network. The process starts by finding all possible interactions between the initial set of genes in steps 2.1 and 2.2both from a text-mining source and a functional PPI source, respectively. The initial list of genes for each disease and each drug are then used toquery the extracted network in a data source specific manner. Finally the interactions between these genes and their direct neighbors would beconsidered as a disease-specific or drug-specific gene-gene network.
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Figure 3 Building six different drug-disease association networks. This figure shows the process of building six different drug-diseaseassociation networks. Starting with drug-specific and disease-specific subnetworks in step1, we have imported these networks into the Gephi tool tocheck different centrality measures for every single gene in step 2. In step 3, for each drug/disease we have selected genes with a centrality measure(degree, closeness and betweenness) that is higher than the average centrality measures for all genes. These selected genes have been used to builddrug-gene and disease-gene Boolean networks. This step has been independently repeated for the three centrality measures and for the two datasources. Thus we ended up having six different drug-gene and disease-gene networks that have been used to train the regression model in step 4.
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with high centrality measures as described in the previ-ous section. As a result, we ended up with 12 networks:6 for drugs and 6 for diseases that were used in the logis-tic regression model. The resulting disease-gene networksare a matrix of 2343 genes and 22 diseases, and the drug-gene network is a matrix of 2343 genes and 406 drugs.Table 1 summarizes the number of the interactions in eachmatrix.
Performance assessment of different strategiesAfter constructing the drug-gene and disease- gene net-works, we used logistic regression to predict associa-tions between drugs and diseases, and then assessedthe performance of the resulting interaction against thegold standard using AUC. Disease networks were usedas response variables and Drug networks were used aspredictive variables. Figure 4 shows the AUC values ofthe six networks described in Table 1. Results showthat selecting genes based on their centrality degree inthe drug or diseases specific network outperforms othercentrality measures. We then combined the text basedand microarray based networks for each centrality mea-sure. The results showed that combining text mining andmicroarray data improves the performance of AUC.Whenwe used protein-based networks to compare the perfor-mance of the networks generated to networks that donot incorporate protein, we found that incorporating pro-tein networks improves the AUC as well. This findingalso reflects the robustness of networks in revealing somehidden information that can be utilized for predictionpurposes.
Drug-disease networkA full list of interacting drug-disease networks is availablein Additional file 1, which shows the drug-disease networkusing microarray based networks and using degree cen-trality. 374 interactions between 22 diseases and 183drugs are predicted using our proposed regression model.We used the Gephi tool to build a visualized version ofthese interactions as shown in Figure 5. In the Discussionsection we focus on some prostate cancer-drug interac-tions that were predicted using our proposed paradigm.
Prostate cancer genesWe further assessed the genes found to be relevant toprostate cancer. Based on the microarray-based network,98 genes were associated with prostate cancer, and basedon text mining, 133 genes were associated with prostatecancer; 34 of them were identified with both procedures.We used the Expression2kinase tool to predict drugstargeting those 34 genes, and several were found: e.g.,tichostatin, betazole, scriptaid, troglitazone, and felodip-ine. Unfortunately, none of them was predicted in ourapproach, due to lack of expression data for these drugsexcept troglitazone. When we characterized the functionof the 34 prostate genes, we found they were significantlyassociated with BCR free survival (Figure 6) and to multi-ple cancer pathway genes (Figure 7). These results suggestthat the integrative approach we followed to define diseasesubnetworks to represent each disease can efficiently pre-dict disease related genes. This result provides evidencethat predicting drugs that can in effect counteract the 34genes could be a significant milestone toward reducingprostate cancer risk.
Table 1 Summary of the networks we generated for each drug and diseases using different centrality measures
Drug-gene networks
Name Source Size Number of links Centraility measure
TxtNet-DrugExt-D Text mining 2343x406 14375 Degree
TxtNet-DrugExt-B Text mining 2343x406 21443 Betweenness
TxtNet-DrugExt-C Text mining 2343x406 19890 Closeness
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Figure 4 Performance assessment of different approaches. This figure shows the performance assessment of multiple approaches to predictdisease-drug interactions. Networks that have been generated with genes with a high degree of centrailty have the highest AUC values. Mir-degreeis the AUC prediction of using MirNet-DrugExt-D as the predictive variable and MirNet-DiseaseExt-D as the response variable.
Figure 5 Predicted drug-disease interaction network. This figure shows the resulting drug-disease interaction network. Note that differentcolors represent different drug-disease communities using modularity function in Gephi tool.
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Figure 6 Kaplan-Meier curve of the 34 prostate genes. This figure shows the Kaplan-Meier curve of the 34 prostate cancer related genes. Fromthe figure, it can be seen that alteration in these genes is significantly associated with high risk of BCR recurrence.
DiscussionDrug repositioning is one of the most important tech-niques that is being used to improve drug discoveryprocess. Drug repositioning most attractive feature isits ability to reduce costs and provide shorter paths toapproval compared to the daunting traditional techniques.Most of the proposed techniques for drug repositioningtend to use a specific source of data to predict drug-disease interactions. In this work we integrated data fromthree major sources into a single paradigm to predictsome novel drug-disease interactions. More precisely,microarray expression profiles, text-mining and biolog-ical networks were all integrated to build a drug-diseasenetwork. Comparing the proposed paradigm with a drug-disease gold standard demonstrated the robustness of theintegrative paradigm in predicting drug-disease interac-tions. More specifically, the AUC showed that selectinghub genes from combined network, microarray and textmining, would be more representative than selectingthese genes from either of these sources independently.These findings, shown in Figure 4, were validated withconsidering hub genes in three centrality contexts: degree,betweenness and closeness. The results were consistent inall these centrality measures; hub genes using a combinednetwork were more representative than hub genes usinga single data source network. Finally we wanted to checkfor biological meaning for some of the predicted asso-ciations. More specifically we focused on some prostate
cancer-drug associations and browsed the scientific lit-erature for biological sense. Azacitidine is a pyrimidinenucleoside analogue that inhibits DNA methyltrans-ferase, impairing DNA methylation [24]. Azacitidine isused for treatment of patients with myelodysplastic syn-drome subtypes; refractory anemia with ringed or excessblasts or acute myleogenous leukemia [24]. Azacitidineis believed to exert its effect by causing hypomethy-lation of DNA on abnormal hematopoitic cells in thebone marrow. According to our study, Azacitidine wasfound to have a role in prostate cancer treatment. In anexperiment to study the effect of Azacitidine in aggres-sive prostate cancer models, it improved the anti-tumoreffect of Docetaxel and cisplatin drugs. The authorssuggested using Azacitidine as a chemosensitizing agentin chemoresistant tumors [32]. In another experiment[33], Azacitidine was found to have anti-proliferativeactivities when administrated chronically. This treatmentresulted in a marked decrease in tumor cell proliferationwith significant increases in androgen and PSA proteinlevels. Another interesting association predicted by oursuggested model was Berberine and prostate cancer. Inmany experiments Berberine was found to have anti-tumor activities on prostate cancer cell lines [34,35], andfound to induce G1 arrest at low concentration [34]. Inaddition, at high concentration it has been found to effi-ciently abrogate G2/M arrest. The results suggest thatcombined administration of Berberine and caffeine may
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Figure 7 Protein network of the 34 prostate genes and cancer genes. This figure shows the functional protein network of the 34 prostategenes predicted by our model and other cancer related protein partners. Most of the genes in this network are oncogenes and tumor suppressorsin addition to other keys players in cancer development.
accelerate the killing of cancer cells. Berberine suppressesAR, which is known to be activated in cancer signalingand suggests that Berberine presents a promising agentfor the prevention and/or treatment of prostate cancer[35]. Paclitaxel is an antineoplastic agent indicated as afirst-line and subsequent therapy for the treatment ofadvanced carcinoma of the ovary and other various can-cers including breast cancer [24]. According to our model,Paclitaxel was found to have a strong association scorewith prostate cancer. Indeed, the anti-neoplastic activityof Paclitaxel on prostate cancer was detected in manyexperiments [36,37]. The findings suggest that Paclitaxelinduces nuclear translocation and activation of PKC-Ît’,which in turn causes Golgi-Cdk1 activation. Golgi-mediated signaling cascades facilitate mitochondriainvolved apoptotic pathways, the thing that might explain
the anti-tumor activity of Paclitaxel. Surface modifiedtumor cells may have potential clinical benefit for patientswith prostate cancer when it is combined with paclitaxel[35]. With the consideration that immunochemotherapymust depend on careful selection of paclitaxel dosage andthe sequence of paclitaxel/vaccine administration.
ConclusionThe presented results in this work demonstrates thatdefining robust gene signatures for diseases and drugsfrom expression profiles and literature and using proteinnetworks to refine and prioritize genes is valuable andhave potential in clinical pharmacogenomics research.The results can significantly accelerate the translation intothe clinics of known compounds for novel therapeuticuses.
Qabaja et al. Journal of Cheminformatics 2014, 6:1 Page 13 of 14http://www.jcheminf.com/content/6/1/1
Competing interestsThe authors declare that they have no competing interests.
Authors’ contributionsAQ conceived and designed the experiments, analyzed the data and wrotethe initial manuscript. MA participated in study design, data and resultsanalysis, and wrote initial manuscript. EA participated in protein networkanalysis. RA participated in study design and data analysis. All authors readand approved the final manuscript.
AcknowledgementsMohammed Alshalalfa and Reda Ahajj would like to thank iCORE (AlbertaInnovates) and NSERC for funding. The authors would like to thank Prof.BonnieKaplan for proof reading the manuscript. Eisa Alanazi’s research is supportedby Saudi Cultural Bureau in Canada.
Author details1Department of Computer Science, University of Calgary, Calgary, Alberta,Canada. 2Department of Computer Science, University of Regina, Regina,Canada. 3Biotechnology Research Center, Palestine Polytechnic University,Hebron, Palestine. 4Biotechnology Department, An-Najah University, Nablus,Palestine. 5Computer Science Department, Global University, Beirut, Lebanon.6Department of Computer Science, Umm Al-Qura University, Makkah, SaudiArabia.
Received: 23 July 2013 Accepted: 28 November 2013Published: 7 January 2014
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