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Methods 44 (2008) 61–72

Computational methods for analysis of cellular functions and pathwayscollectively targeted by differentially expressed microRNA

Yuriy Gusev *

Department of Surgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA

Accepted 12 October 2007

Abstract

This report presents computational methods of analysis of cellular processes, functions, and pathways affected by differentiallyexpressed microRNA, a statistical basis of the gene enrichment analysis method, a modification of enrichment analysis method account-ing for combinatorial targeting of Gene Ontology categories by multiple miRNAs and examples of the global functional profiling ofpredicted targets of differentially expressed miRNAs in cancer. We have also summarized an application of Ingenuity Pathway Analysistools for in depth analysis of microRNA target sets that may be useful for the biological interpretation of microRNA profiling data. Toillustrate the utility of these methods, we report the main results of our recent computational analysis of five published datasets of aber-rantly expressed microRNAs in five human cancers (pancreatic cancer, breast cancer, colon cancer, lung cancer, and lymphoma). Using acombinatorial target prediction algorithm and statistical enrichment analysis, we have determined Gene Ontology categories as well asbiological functions, disease categories, toxicological categories, and signaling pathways that are: targeted by multiple microRNAs; sta-tistically significantly enriched with target genes; and known to be affected in specific cancers. Our recent computational analysis of pre-dicted targets of co-expressed miRNAs in five human cancers suggests that co-expressed miRNAs provide systemic compensatoryresponse to the abnormal phenotypic changes in cancer cells by targeting a broad range of functional categories and signaling pathwaysreportedly affected in a particular cancer.� 2007 Elsevier Inc. All rights reserved.

Keywords: microRNA; microRNA profiling; microRNA targets; Gene Ontology; Enrichment analysis; Pathway analysis; Cancer

1. Introduction

Multiple studies have found aberrant expression profilesof the miRNAome in major human cancers (reviewed in[1]). To date, a relatively small number of target geneswas experimentally identified for some miRNAs in varioustumors [2]. However, the global pattern of cellular func-tions and pathways that are affected by miRNAs in cancerremains largely unknown.

miRNA expression signatures are shown to be specificand allow classification of tumor type as well as differentstages in tumor progression and in some cases predict out-

1046-2023/$ - see front matter � 2007 Elsevier Inc. All rights reserved.

doi:10.1016/j.ymeth.2007.10.005

* Fax: +1 405 271 1194.E-mail address: yuriy-gusev@ouhsc.edu

come of a disease [3]. A number of studies have shown thatexpression of some of the genes affected in cancer was neg-atively correlated with the expression of specific miRNAs.Based on these findings, several groups have hypothesizedthat miRNAs may play important roles in tumorigenesisand tumor progression and could function as oncogenesor tumor suppressor genes [4,5]. However, such generalinterpretation of miRNA expression profiling data is some-what impaired by the lack of high throughput target vali-dation methods and mostly relies upon a relatively smallfraction of miRNA targets that are experimentallyvalidated.

Computational algorithms have played a central role inthe discovery of the majority of miRNAs known to date, aswell as in prediction of their targets (reviewed in [6–8]).However, virtually all existing programs generate some

62 Y. Gusev / Methods 44 (2008) 61–72

level of false positive predictions [9]. Experimental evidencealso suggests that these programs generate some false neg-ative predictions. While bioinformatics methods continueto improve specificity and sensitivity of target predictions,the unresolved challenge still remains to utilize even themost accurate predictions for biologically meaningful inter-pretation of miRNA profiling data. This problem is due tothe fact that the majority of known miRNAs are predictedto target a very large number of transcripts. Each miRNAmight have up to several hundred targets. In addition,many transcripts from protein-coding genes are targetedby more than one miRNA and some transcripts might haveover a hundred target sites for different miRNAs. It hasbeen estimated that nearly 50% of all human gene tran-scripts are regulated by a relatively small number of 474miRNAs that are known to date with average of �200 tar-gets per miRNA [6,7].

This conundrum of target multiplicity has become espe-cially evident with the discovery of a large number of differ-entially expressed miRNAs in many human cancers [4,5]. Asignificant number of miRNAs in a range from 10 to 100were found to be aberrantly expressed in breast cancer,colon cancer, lung cancer, and other common human can-cers with a predicted total number of targets ranging fromseveral hundred to as many as several thousand. This againpresents a problem for global analysis and the biologicalinterpretation of the regulatory impact of miRNAs in can-cer cells. There is a clear need for data reduction methodswhich would allow prioritizing the list of targets and deter-mining cellular processes that are most significantlyaffected by miRNAs in cancer.

The Gene Ontology (GO) enrichment analysis is one ofthe data reduction techniques that could be used to narrowdown a list of targets of a large group of co-expressed miR-NAs and to find biological functions that are most signifi-cantly affected by multiple miRNAs.

2. Description of method

2.1. Statistical framework for enrichment analysis of

microRNA targets

Several high throughput technologies such as micro-arrays, real-time PCR, microbeads and others are nowavailable to screen expression of multiple or all knownmiRNAs. Independent of the platforms and the analysismethods used, the result of a global profiling experimentis a list of differentially expressed miRNAs. A choice of tar-get prediction algorithms can then be used to generate a listof potential target genes (reviewed in [10]). Briefly, for eachmiRNA a list of potential target sites (conserved betweenseveral species) is determined on the 3 0-UTRs of humangene-coding transcripts based on a search for complimen-tary binding sites for the sequence of ‘‘seed’’ regions ofthe 5 0ends of mature miRNA positioned at nucleotides 2–8.

Computational prediction of targets for clusters of co-expressed miRNAs can be performed using stand alone

open access program (PicTar [7], miRanda [11]), and pub-licly available software suites allowing to combine predic-tions of several popular prediction algorithms (MAMI[12,13]). Commercial software is also available to performthis type of analysis (miRgate [14,15]). Most of these pro-grams generate a union of lists of predicted targets of allindividual miRNAs from a cluster of interest. However acombined list of predicted targets is usually very largeand requires an additional analysis to determine biologicalthemes (functions, pathways, etc.) that are affected by agiven set of differentially expressed miRNAs.

Several automated methods allow functional annotationof gene lists according to existing functional annotation sys-tems, such as Gene Ontology [16] or various databases ofknown signaling pathways (KEGG [17], GeneMAPP [18]).

In order to exclude those categories that were foundsimply by chance, a statistical procedure known as enrich-ment (over-representation) analysis has been developedindependently by several groups (reviewed in [19]).

In many existing software tools the Fisher Exact test [20]is adopted to measure the gene enrichment (over-represen-tation) in annotation terms such as Gene Ontology termsor canonical pathways.

For each annotation term the Fisher Exact probabilityis calculated using the Gaussian hypergeometric probabil-ity distribution that describes sampling without replace-ment from a finite population consisting of two types ofelements [21].

As a hypothetical example, we considered a user list of200 differentially expressed genes which contained fivegenes out of 200 that belong to GO term Apoptosis. Usingthe human genome reference set with 25,000 genes total, wefound that 50 human genes belongs to this GO term. Usinga 2 · 2 contingency table, we can test a hypothesis that 5genes out of 200 is more than could be found by randomchance compared to the human reference set where 50genes were found in this term out of 25,000.

A 2 · 2 contingency table can be built using the abovenumbers:

User genes

Genome

In GO term

5 50 Not in GO term 195 24,950

Calculated Fisher Exact p-value equals to 0.00007549 andsince p-value 6 0.05, this gene list is enriched (over-repre-sented) in Apoptosis GO Term more than by randomchance.

In case of functional annotation of microRNA targets, acalculation of Fisher Exact p-value for each GO term isbased upon a comparison of target genes that belong toparticular GO term with the reference set of all predictedhuman target genes (annotated to any GO terms). A simi-lar approach could be used to determine pathways that areenriched with microRNA targets. A typical flow diagramof data analysis is shown on Fig. 1.

Cluster of differentially

expressedmicroRNA

Target Prediction Algorithm

List of GO categories

enriched with target genes

PathwayEnrichment

Analysisof target genes p ≤

≤

threshold

List of Pathways enriched with target genes

Gene Ontology Enrichment

Analysisof target genes p threshold

Fig. 1. Flow diagram of gene enrichment analysis.

Y. Gusev / Methods 44 (2008) 61–72 63

For example, let us consider a set of 20 miRNAs report-edly over-expressed in colon cancer [5]. Using open accesssoftware tools MAMI [13], we can generate a list of all tar-get genes to these 20 microRNAs which represent a unionof predictions of the four most popular target predictionalgorithms with total of 5170 genes. A functional annota-tion and over-representation analysis of GO BiologicalProcess (GO BP) terms for this gene list can be performedusing an open access program DAVID [22]. As a referenceset we used all human microRNA targets predicted by thesame four algorithms (10,574). For this example a contin-gency table for GO term Apoptosis is built as follows:

All target genesof co-expressedmicroRNAs

All humanmicroRNAtargets

In GO term

197 472 Not in GO term 3494 10,102

Using DAVID software the Fisher Exact p-value wascalculated (p = 0.01235) and since p-value is less than0.05, this miRNA target list is enriched (over-repre-sented) in Apoptosis GO BP term more than by randomchance.

For this example a complete list of enriched GO BPterms contains 176 categories that are significantly enrichedin target genes of 20 over-expressed miRNAs.

Evidently, a large number of enriched functional catego-ries were identified for this dataset. In order to narrowdown the list of GO terms these categories could be rankedaccording to significance of over-presentation (FisherExact p-values), however, the most general GO terms hasa tendency to include a large number of target genes andto be highly significant. These general GO terms can oftenbe found among the top ranked enriched categories. Forcolon cancer, a list of the top 20 most significantly enrichedcategories mostly consists of general GO terms (GO BP_1

and GO BP_2) that are related to the regulation of cell pro-cesses (Supplemental Table 1).

To avoid this problem, the analysis could be restrictedto a lower level of GO hierarchy providing smaller andmore specific gene lists. In our example with colon cancerwe have performed additional analysis restricted to onlythe lowest level (level 5: GO BP_5) of GO terms. Theresulting list of enriched categories shows 36 more specificfunctional categories that are significantly over-representedamong targets of 20 miRNAs (Table 1). Many of these cat-egories, such as ‘‘regulation of progression through cellcycle’’ and ‘‘stress-activated protein kinase signaling path-way’’, are well known to be affected in cancer.

A similar approach could be applied to the enrichmentanalysis of known signaling pathways. In this case a listof genes is compared to a list of genes from a particularpathway while a reference gene list contains all genes froma complete set of known (canonical) signaling pathways.

In our example with colon cancer we have also per-formed pathway enrichment analysis using DAVID soft-ware. A collection of KEGG signaling pathways wasanalyzed using the Fisher Exact test. A total of 19 path-ways were found that were enriched with microRNA targetgenes at significance level 0.05 (Table 2). The top rankedpathway (HSA04510: Focal Adhesion) contained 101genes targeted by miRNAs that are over-expressed incolon cancer (p-value = 2.22E�05). Several other path-ways from this list were reported to be affected by canceras well.

This type of enrichment analysis can uncover some com-mon biological themes that are present in a set of miRNAtarget genes, thus providing an investigator with additionalclues for the follow up experiments. However such anapproach has several shortcomings as it generates a widespectrum of many functional categories presumablyaffected by miRNAs which makes it difficult to interpretor validate. In addition, with this method all of the infor-mation related to the targets of specific microRNAs is lost

Table 1Gene Ontology BP_5 terms that are enriched with targets of miRNAsover-expressed in colon cancer, standard enrichment analysis

GO BP_5 term Count p-Value

Regulation of nucleobase, nucleoside, nucleotide andnucleic acid metabolism

772 2.92E�14

Transcription 791 1.36E�13Biopolymer modification 629 1.22E�10Phosphate metabolism 336 4.51E�08Regulation of progression through cell cycle 195 4.46E�06Enzyme linked receptor protein signaling pathway 88 1.71E�04Negative regulation of cellular metabolism 88 2.20E�04Protein kinase cascade 121 2.53E�04Cell growth 69 9.71E�04Regulation of cell size 69 9.71E�04Regulation of protein kinase activity 63 0.0012Small GTPase mediated signal transduction 112 0.001303Negative regulation of progression through cell cycle 68 0.001726Intracellular receptor-mediated signaling pathway 26 0.002467Stress-activated protein kinase signaling pathway 26 0.003842Positive regulation of cellular metabolism 67 0.004638Chromosome organization and biogenesis 103 0.006869Positive regulation of programmed cell death 68 0.007934Vesicle-mediated transport 142 0.015351Regulation of cell motility 17 0.015427Regulation of cell migration 17 0.015427Vacuole organization and biogenesis 9 0.017697Endosome transport 14 0.017848Notch signaling pathway 19 0.020648Apoptosis 197 0.021507Lysosomal transport 6 0.028821Cytoskeleton organization and biogenesis 138 0.033054Cartilage condensation 7 0.033699Regulation of cell shape 17 0.035196Regulation of programmed cell death 126 0.042611Brain development 18 0.045917Protein transport 198 0.047414Lipid modification 13 0.048145Cell migration 42 0.049238Secretory pathway 74 0.049295Regulation of apoptosis 125 0.049678

Table 2KEGG pathways that are enriched with targets of miRNAs over-expressed in colon cancer, standard enrichment analysis

KEGG pathway term Genecount

p-Value

HSA04510:focal adhesion 101 2.22E–05HSA04360:axon guidance 63 7.59E–04HSA04660:T Cell receptor signaling pathway 47 9.99E–04HSA04010:MAPK signaling pathway 113 0.001012HSA04910:insulin signaling pathway 62 0.001414HSA04810:regulation of actin cytoskeleton 87 0.002141HSA04530:tight junction 50 0.002513HSA04670:leukocyte transendothelial migration 49 0.004693HSA04350:TGF-b signaling pathway 40 0.010833HSA04120:ubiquitin mediated proteolysis 24 0.021117HSA04720:long-term potentiation 30 0.021399HSA04930:type II Diabetes mellitus 23 0.023218HSA04710:circadian rhythm 10 0.030728HSA05050:dentatorubropallidoluysian atrophy

(DRPLA)9 0.031649

HSA04070:phosphatidylinositol signaling system 43 0.031708HSA04310:WNT signaling pathway 62 0.03592HSA04210:apoptosis 37 0.036347HSA04540:gap junction 41 0.037909HSA04110:cell cycle 47 0.04661

64 Y. Gusev / Methods 44 (2008) 61–72

and functional categories can not be traced back in orderto determine which miRNAs are involved in regulationof a particular functional term. Also lost is importantinformation on the miRNAs that are collectively targetingthe same genes or the same functional categories.

2.2. A modification of enrichment analysis based on

combinatorial targeting of gene ontology categories by

multiple miRNAs

In this section we discuss modifications of enrichmentanalysis provided in commercial miRNA target analysissoftware (miRgate 2.1 suite, Actigenics/Cepheid [14,15]).Similar to other known algorithms, a list of potential targetsites (conserved between human and mouse) on 3 0-UTRs ofhuman gene-coding transcripts is determined based on asearch for complimentary binding sites for the sequenceof ‘‘seed’’ regions of 5 0ends of mature miRNA positionedat 2–8 nucleotide region. However miRgate provides addi-tional functionality with its functional profiling algorithm

for the Gene Ontology (GO) enrichment analysis that isspecifically designed to take into account informationabout the number of miRNAs that are targeting the sameGO categories i.e. number of miRNA hits per GO category[14,15].

In order to determine association between GO categoryand miRNA, the authors of miRgate have adopted hyper-geometric distribution. First, The GO categories are deter-mined for predicted targets of each miRNAs from a group.This set of GO categories is filtered based on significance ofover-representation of ‘‘hits’ by multiple microRNAs usinga selected threshold for p-values of hypergeometric distri-bution [15]. This technique allows excluding those func-tional categories that were found simply by chance. Anadditional filter could then be applied to select only thoseover-represented GO categories which are targeted by atleast a given fraction of co-expressed miRNAs. This allowsobtaining a short list of functional categories that are morelikely to be affected by a given group of co-expressedmiRNAs.

A resulting list of target genes from over-representedGO categories can be subjected to a more detailed func-tional analysis such as pathway analysis using a wide spec-trum of publicly available or commercial software tools toobtain a more refined biological interpretation of affectedgene categories. Importantly, by retaining information onspecific miRNAs that are targeting a particular GO cate-gory, one can selectively look at those functional categoriesthat are targeted by a subset of co-expressed miRNAs.

As an example, we applied this method to the analysis ofthe same set of 20 over-expressed miRNAs from thepublished colon cancer study [5]. We have determined

Y. Gusev / Methods 44 (2008) 61–72 65

over-represented Gene Ontology categories that are tar-geted by multiple miRNAs in the group. Assuming thatthe categories that are targeted by all miRNAs in thegroups are affected the most we have selected only thoseover-represented GO terms that are targeted by 100% ofmiRNAs. The resulting short list of GO BP terms has con-tained only eight categories (Table 3) most of which arewell-known cancer-related biological processes such asinduction of apoptosis (top ranked category), cell division,and cell cycle.

3. Application of combinatorial enrichment method for

analysis of miRNA profiling data in cancer

Several studies have reported the results of computa-tional analysis of functional annotation of genes targetedby single miRNAs [23], all known miRNAs [24,25], orsmall groups of miRNAs that were selected based on highsimilarity of ‘‘seed’’ sequences in the 5 0 region and/or largeoverlap of predicted target sets [26]. However, in case ofexperimentally obtained miRNA profiling data theseapproaches are not very practical when the task is to deter-mine common biological functions and regulatory path-ways that are targeted by experimentally detected groupsof co-expressed miRNAs. Specifically in cancer suchgroups of miRNAs are often found to have fewer commontarget genes and not to share similar ‘‘seed’’ sequences.

In our recent study [27], we addressed this problem ofbiological interpretation of miRNA profiling data usingover-representation analysis of biological processes, dis-ease categories, and signaling pathways that are targetedcollectively by co-expressed miRNAs. Here, we present adetailed description of a computational strategy that wehave applied and a summary of the results from thisstudy.

3.1. Data sets

Five groups of co-expressed miRNAs were selectedfrom the literature for this study: three groups that werereported by Volinia et al. [5] as being over-expressed inbreast cancer (14 miRNAs), colon cancer (20 miRNAs)

Table 3Gene Ontology BP terms that are enriched with targets of miRNAs over-expresof combinatorial enrichment analysis

GO category GO # Observed Expected E(

Induction of apoptosis GO:0006917 146 115.3307 1Cell division GO:0051301 172 143.407 1Homophilic cell adhesion GO:0007156 150 126.327 1Positive regulation of

transcription, DNA-dependentGO:0045893 135 116.0139 1

Ubiquitin cycle GO:0006512 430 380.6077 1Intracellular protein transport GO:0006886 199 176.9164 1Metabolic process GO:0008152 300 269.5714 1Cell cycle GO:0007049 364 329.1725 1

and lung cancer (33 miRNAs). We have also includeda set of miRNAs that we found to be significantlyover-expressed in pancreatic cancer (47 miRNAs) [28].An additional group of seven miRNAs was reportedas being over-expressed in lymphomas [29]. This groupof miRNAs is encoded by a single gene (cistron miR-17–92) and expressed as a single primary transcript.Over-expression of cistron miR-17–92 was found in B-lymphomas [9] and also was shown to have strong cor-relation with T-lymphoma development in an animalmodel [30]. These datasets were selected to representthe whole spectrum of group sizes of co-expressed miR-NAs that are observed in cancers: from a small clusterof co-expressed miRNAs (7 miRNAs, cistron miR-17–92) to a large group of co-expressed miRNAs (47 miR-NAs, pancreatic cancer) to avoid possible bias of samplesize.

3.2. Combinatorial enrichment analysis of the gene ontology

categories

The Gene Ontology (GO) enrichment analysis of biolog-ical processes targeted by each of five groups of miRNAswas performed using the miRgate GO profiling algorithmthat is specifically designed to take in account informationabout the number of miRNAs that are targeting the sameGO categories i.e. number of miRNA ‘‘hits’’ per GO cate-gory [14,15]. The GO categories were determined for pre-dicted targets of each miRNAs from a group. This set ofGO categories was filtered based upon significance ofover-representation of ‘‘hits’’ by multiple microRNAsusing a selected threshold for p-values of hypergeometricdistribution (p 6 0.05). An additional filter was applied toselect only those over-represented GO categories whichare targeted by 100% of miRNAs in a group. The resultinglist of the enriched GO terms categories for four cancerdatasets is presented on Table 4. For all data sets the topranked GO terms that are targeted by 100% of miRNAinclude biological processes that are commonly known tobe associated with various types of cancer includingapoptosis, cell cycle, cell proliferation, as well as more spe-cific categories that were specific for each cancer such as

sed in colon cancer and targeted by 100% of miRNAs in the group. Results

nrichment Significance Target genesnumber

MicroRNA group memberstargeting the same functionObs/exp) p-Value

.27 0.00215 39 100% [20/20]

.2 0.00789 53 100% [20/20]

.19 0.0161 30 100% [20/20]

.16 0.0351 41 100% [20/20]

.13 0.00506 127 100% [20/20]

.12 0.0437 65 100% [20/20]

.11 0.0288 120 100% [20/20]

.11 0.0247 131 100% [20/20]

Table 4Results of combinatorial enrichment analysis of microRNA targets for four human cancers

GO category Significancep-value

Targetgenes No.

MicroRNA group memberstargeting the same function (%)

Pancreatic cancer

Transport 0.000388 247 100% [47/47]Regulation of Rho protein signal transduction 0.00133 36 100% [47/47]Small GTPase mediated signal transduction 0.0107 138 100% [47/47]Protein complex assembly 0.0189 61 100% [47/47]Actin cytoskeleton organization and biogenesis 0.0407 54 100% [47/47]Proteolysis 0.0461 171 100% [47/47]Positive regulation of I-jB kinase/NF-jB cascade 0.0488 46 100% [47/47]

Lung cancer

Protein targeting 0.00799 35 94% [31/33]Regulation of cyclin-dependent protein kinase activity 0.0321 19 91% [30/33]Sodium ion transport 0.036 46 100% [33/33]Protein transport 0.0434 157 100% [33/33]Cell proliferation 0.0444 142 100% [33/33]Apoptosis 0.0488 150 97% [32/33]

Colon cancer

Induction of apoptosis 0.00215 39 100% [20/20]Ubiquitin cycle 0.00506 127 100% [20/20]Cell division 0.00789 53 100% [20/20]Homophilic cell adhesion 0.0161 30 100% [20/20]Cell cycle 0.0247 131 100% [20/20]Metabolic process 0.0288 120 100% [20/20]Positive regulation of transcription, DNA-dependent 0.0351 41 100% [20/20]Intracellular protein transport 0.0437 65 100% [20/20]

Breast cancer

Transforming growth factor beta receptor signaling pathway 9.99E�04 13 100% [14/14]Inflammatory response 2.12E�03 51 100% [14/14]Small GTPase mediated signal transduction 5.88E�03 102 100% [14/14]Glycogen metabolic process 8.11E�03 7 100% [14/14]Ubiquitin cycle 9.78E�03 138 100% [14/14]Negative regulation of transcription from RNA polymerase II promoter 1.35E�02 60 100% [14/14]Regulation of translation 2.07E�02 16 100% [14/14]Mitosis 2.41E�02 37 100% [14/14]DNA recombination 2.77E�02 11 100% [14/14]Unfolded protein response 3.40E�02 7 100% [14/14]Cell cycle arrest 3.87E�02 25 100% [14/14]Nervous system development 4.28E�02 123 100% [14/14]Potassium ion transport 4.34E�02 49 100% [14/14]Cell division 4.50E�02 48 100% [14/14]

66 Y. Gusev / Methods 44 (2008) 61–72

regulation of Rho protein signal transduction for pancre-atic cancer [31].

3.3. Ingenuity Systems Analysis of predicted miRNA targets

To further evaluate the specific functional profiles ofgenes from the broad GO categories that are targeted bymiRNAs, we performed more detailed functional analysisusing Ingenuity Pathway Analysis System (IPA 5.0, Inge-nuity Systems, Redwood, CA) [32].

For each set of co-expressed miRNAs we have gener-ated three sets of predicted gene targets using GO ontologyenrichment analysis as a statistical filter:

1. Targets that belong to GO categories targeted by at leastone of the co-expressed miRNAs.

2. Targets that belong to GO categories targeted by at least50% of the co-expressed miRNAs.

3. Targets that belong to GO categories targeted by 100%of the co-expressed miRNAs.

We started with raw lists of all targets predicted for eachof the miRNAs in a range from 2175 genes (cistron miR-17–92) to 5356 genes (pancreatic cancer). Filtering raw setsof predicted targets by miRgate GO enrichment analysisalgorithm provided a significant reduction of target lists ina range from 2.5-fold to over 4-fold (Supplemental Table 2).

Three sets of genes were generated for each of fivegroups of miRNAs and were then analyzed by IngenuityPathway Analysis tools to determine more detailed infor-mation about biological functions, disease categories, tox-icological categories, canonical signaling pathways, and

Cluster of differentially

expressedmicroRNA

CombinatorialTarget

Prediction(miRgate 2.0)

Enrichment Analysis p 0.05

3 sets of GO categories collectively targeted by

multiple miRNAs: 1. by 1 miRNA 2. by 50% miRNAs 3. by 100% miRNAs

Ingenuity Pathway Analysis (IPA 5.0)

Comparison of known cancer genes vs target gene lists

≥

≤

≥

Fig. 2. Flow diagram of combinatorial enrichment analysis of fivedatasets of over-expressed miRNAs in five human cancers.

Y. Gusev / Methods 44 (2008) 61–72 67

drugs associated with each data set. The data analysis dia-gram is shown in Fig. 2.

3.4. Reference set of genes known to be affected in cancer

To select the most important functional categories andpathways for analysis and to understand relevance of miR-NA-targeted categories to the specific cancer, we generatedreference sets of genes for each of five cancers by keywordsearch of the Ingenuity Knowledge Base. This search pro-vided us with a very conservative lists of genes that werereported to be affected in a specific cancer by multipleresearch groups and were then manually curated by agroup of expert biologists.

These five reference sets of genes known to be affected inlymphoma, breast cancer, colon cancer, lung cancer, andpancreatic cancer were used in the Ingenuity PathwayAnalysis system to generate sets of over-represented bio-

logical functions, disease categories, and pathways thatare known to be affected in each of these cancers and tocompare it with the categories and pathways predicted tobe affected by co-expressed miRNAs.

3.5. Comparative analysis of biological functions and disease

categories

Using Ingenuity Pathway Analysis system (IPA 5.0) wehave compared target gene sets determined by GO enrich-ment algorithm with the reference groups of genes that areknown to be affected in a specific cancer. This allowed us todetermine which top ranked categories of reference setswould be statistically enriched with miRNA targets. Theresults indicate that many top ranked biological functionsand disease categories as well as toxicological categoriesthat were tissue specific for each specific cancer were alsostatistically significantly over-represented in our target lists.Top biological functions and disease-related categorieswere compared among five groups of data using gene listsgenerated by trimming miRNA collectively targeted genesat the 50% level (Fig. 3).

The top ranked disease category for all five datasets wasCancer (Fig. 3B) with highly significant enrichment(p � 10�10–10�20). Within this top category, we identifieda significant number of miRNA targets that are knownas tissue specific biomarkers of each of five cancers as wellas a large number of miRNA targets that are known to beaffected in other cancers (Table 5).

For example, a list of miRNA targets for colon cancer(Supplemental Table 3) included the APC gene (adenoma-tosis polyposis coli) among other well-known oncogenes.

For pancreatic cancer a list of miRNA targets includedboth k-ras and p53 genes (Supplemental Table 4) that arewell-known biomarkers of pancreatic tumors [33]. Impor-tantly, several ras oncogenes were experimentally validatedas targets of multiple miRNAs from the let-7 family [34].Overall, in our analysis we identified 25 known cancer-related genes that have been already experimentally vali-dated as targets of miRNAs.

According to Ingenuity Systems classification the topranked biological functions included general categoriesof cell cycle, cell death, cell morphology, as well as morespecific post-translational modification and DNA replica-tion, recombination, and repair (Fig. 3A). The detailedresults and statistics of IPA enrichment analysis of func-tional categories for all datasets are published by authorselsewhere [27].

3.6. Comparative analysis of toxicology categories

Using IPA 5.0 we have also analyzed top ranked toxicol-ogy related gene lists for each of the five reference gene listsand compared them with toxicology categories found inour miRNA target lists.

We found that eight top ranked toxicology gene lists foreach cancer were statistically significantly over-represented

Fig. 3. Results of Ingenuity Pathway Analysis. Comparison of top 10 Biological function categories and disease categories enriched with miRNA targetgenes. Five datasets of predicted targets of over-expressed miRNAs were used for analysis.

Table 5Number of microRNA targets from top enriched disease category ‘‘cancer’’ that are known to be affected in specific cancer or other cancer and targeted byknown drugs

microRNA dataset

No. of microRNAtargets associated withspecific cancer

No. of microRNA targets associatedwith specific cancer and targeted byanti-cancer drugs

Total no. of microRNAtargets associated withother cancers

No. of microRNA targets associatedwith other cancer and targeted byanti-cancer drugs

Lymphoma(cistronmiR17–92only)

47 9 236 26

Breast cancer 67 10 179 22Colon cancer 29 8 263 31Lung cancer 37 9 184 24Pancreatic

cancer17 5 162 21

68 Y. Gusev / Methods 44 (2008) 61–72

among miRNA targets. We found it particularly interest-ing that several categories related to oxidative stress andhypoxia were among the top ranked over-representedcategories for miRNA targeted genes (Supplemental Fig-ure 1). These findings are in agreements with recent exper-imental data reporting over-expression of multiplemiRNAs in response to oxidative stress or hypoxia [35]and showing a functional link between hypoxia, a well-known tumor microenvironment factor, and microRNAexpression.

3.7. Enrichment analysis of signaling pathways

To further evaluate the specific functions of genes fromthe broad GO categories that are targeted by miRNAs, weperformed additional, more detailed pathway analysis (IPA

5.0, Ingenuity Systems). We compared gene sets deter-mined by GO enrichment algorithm against known signal-ing pathways to determine which pathways would bestatistically enriched with miRNA targets. We were alsointerested in determining which pathways were affectedthe most by multiple miRNAs from the same co-expressedgroup in each specific cancer.

The results of the pathway analysis were similar for alldatasets. We found that a large fraction of top rankedpathways known to be affected in cancer were also collec-tively targeted by the groups of co-expressed miRNAs(Supplemental Figure 2A). However the sets of signifi-cantly enriched pathways were more specific for each typeof cancer (Supplemental Figure 2B).

In order to understand relevance of affected pathwaysto the specific cancer, we used the same reference sets of

Y. Gusev / Methods 44 (2008) 61–72 69

genes for each of five cancers that were obtained bykeyword search of the Ingenuity Knowledge Base. Thesefive reference sets of genes known to be affected in lym-phomas, breast cancer, colon cancer, lung cancer, andpancreatic cancer were used in the Ingenuity PathwayAnalysis system to compare with the sets of pathwaystargeted by co-expressed miRNAs in each of thesecancers.

For each of the five datasets, we performed analysisof all canonical signaling pathways known to be affectedby each cancer to reveal pathways that are targeted bymiRNAs from our datasets for each particular cancer.We used Fisher’s exact test to select pathways that werestatistically significantly enriched with miRNA targets(p 6 0.05).

For each type of cancer a reference set of genes wascompared with three target sets: a complete list of genesfrom enriched GO categories and two sets trimmed byexcluding genes from those GO categories that are targetedby less than 50 percent or less than 100 percent of miRNAsin a group.

For example, Supplemental Figure 3A demonstrates theresults of comparing the top 12 pathways known to beaffected by colon cancer and targeted by miRNAs thatare over-expressed in colon cancer. Bar color representsfour data sets: dark blue—reference gene list for colon can-cer generated by Ingenuity Knowledge Base; medium blue,light blue, and black—three gene lists that were obtainedfrom GO enrichment analysis; a complete list (all targets)and two sets trimmed by excluding genes from GO catego-ries targeted by less than 50% or less than 100% of miR-NAs in the group. The same type of comparison of top12 pathways for pancreatic cancer is shown on Supplemen-tal Figure 3B.

Fig. 4. Diagrams of p38 signaling pathway. (A) Genes known to be affected incategories that are targeted by at least 50% of over-expressed miRNAs from csignaling pathway are outlined in dark blue. Drug targets are outlined in light bis referred to the web version of the article.)

The detailed inspection of pathway diagrams revealedan interesting pattern of genes targeted by miRNAs. Inthe majority of inspected over-represented pathways someof the miRNA targets were different from the genes knownto be affected by cancer and were found among the genesthat are directly downstream and/or upstream of the can-cer-related genes in the same branches of signaling cascades(Figs. 4 and 5).

On both figures the pathway diagrams on the right sideshow reference genes that are known to be affected in canceras highlighted in gray color (Figs. 4A and 5A). The miRNAtarget genes are highlighted in gray color on the pathwaysdiagrams shown on the left side (Figs. 4B and 5B).

Two examples are shown are for colon cancer (Fig. 4)and for pancreatic cancer (Fig. 5).

In many pathways we found that miRNAs target multi-ple kinases that are important mediators of signal transduc-tion pathways and are often targeted by anti-cancer drugsknown as kinase inhibitors (specific discussion is providedin the next section). Since all miRNAs in this study wereover-expressed in cancer, our findings suggest that theiroverall effect would be to down-regulate many or, some-times, all of the abnormally activated alternative signaltransduction cascades in many of the pathways known tobe affected by a particular cancer. For those pathwayswhere multiple kinases are targeted by miRNAs (see forexample Fig. 4) such an effect would be comparable withthe effect of several kinase inhibitor drugs combined.

3.8. Anti-cancer drugs and microRNA targets

Using Ingenuity Knowledge Base we have analyzedknown anti-cancer drugs and found that several drugsare targeting the same cancer-related genes that are tar-

colon cancer are shown in gray. (B) Genes from significantly enriched GOolon cancer dataset are shown in gray. Drug that are known to target thislue. (For interpretation of color mentioned in this figure legend the reader

Fig. 5. Diagrams of Apoptosis Signaling Pathway. (A) Genes known to be affected in pancreatic cancer are shown in gray. (B) Genes from significantlyenriched GO categories that are targeted by at least 50% of over-expressed miRNAs from pancreatic cancer dataset are shown in gray. Drug that areknown to target genes from this signaling pathway are outlined in dark blue. Drug targets are outlined in light blue. (For interpretation of color mentionedin this figure legend the reader is referred to the web version of the article.)

70 Y. Gusev / Methods 44 (2008) 61–72

geted by the miRNAs. In some instances that included sev-eral experimentally validated microRNA targets.

One of the most common groups of drugs sharing thesame targets with miRNAs was a relatively new class ofkinase inhibitors that are designed to inhibit abnormallyactivated kinases in signal transduction pathways in cancercells. Analysis of pathway diagrams has shown that manyof these kinase inhibitors target the same kinases as domiRNAs.

For example, we found that multiple genes from the p38signaling pathway (Fig. 4) are affected in colon cancer andthat the kinase inhibitor drug SCIO targets all four iso-forms of the p38 MAP kinase super-family and effectivelydown regulates all branches of this signal transductionpathway (Fig. 4A). Interestingly, three of these genes arealso predicted targets of miRNAs that are co-expressedin colon cancer (Fig. 4B). The p38 MAP kinase pathwayplays an important function in the cellular response afterinfection by pathogens or inflammatory stimulation andhas been also implicated in breast, colon and other typesof cancer [36,37].

We found several similar examples of other pathwayswith kinases that are targeted by both the anti-cancer drugsand miRNAs.

We have also found other types of genes that are tar-geted by anti-cancer drugs and miRNAs within the samepathways. For instance the apoptosis signaling pathwayhas many genes affected in pancreatic cancer includingtwo critical regulators of apoptosis: Bcl-2 and Caspase-3(Fig. 5A). Bcl-2 is targeted by the recently developed drugOblimesen which is an antisense synthetic oligonucleotide-

based anti-cancer (pro-apoptotic) drug effectively silencingBcl-2 transcripts (Fig. 5A).

Capase-3 is targeted by IDN-6556, an anti-apoptoticdrug (caspase inhibitor) which is indicated for hepatitisC. Interestingly, both of these genes are also predicted tar-gets of miRNAs that are over-expressed in pancreatic can-cer (Fig. 5B).

Bcl-2 encodes an integral mitochondrial outer mem-brane protein that blocks the apoptotic death of some cellssuch as lymphocytes. Constitutive expression of Bcl-2 isthought to be the cause of some types of cancer [38]. There-fore down regulation of Bcl-2 by over-expressed miRNAscould have pro-apoptotic anti-cancer effects similar to theeffect of oblimesen.

Importantly, Bcl-2 is one of the few microRNA targetsthat were confirmed experimentally to be targeted collec-tively by at least two miRNAs. It has been recently shownby Cimmino et al. [38] that miR-15a and miR-16-1 expres-sion is inversely correlated with Bcl-2 expression in CLLand that both miRNAs negatively regulate Bcl-2. It hasalso been shown that repression of Bcl-2 by these miRNAsinduces apoptosis in leukemia cell lines. The authors of thisstudy have proposed that miR-15 and miR-16 are naturalantisense Bcl-2 regulators that could be used for cancertherapy of some tumors.

Paradoxically, down regulation of caspase-3 by miR-NAs in the same pathway (Fig. 5B) would have an oppositeanti-apoptotic effect similar to the effect of IDN-6556 andwould be beneficial for cancer cell survival. This counterin-tuitive mode of miRNA regulation has been recently dis-cussed in the literature in a context of relevant

Y. Gusev / Methods 44 (2008) 61–72 71

experimental observations showing that miR-34a hastumor suppressor activity when ectopically expressed inNB cell lines through induction of caspase 3/7 apoptoticpathway [39]. miR-34a may have a pro-apoptotic effect,in part, through targeting the E2F3 transcription factor.Recently, it has been shown that miR-17-5p and miR-20aalso act as tumor suppressors by targeting and reducingE2F1 levels [40]. Similar to miR-34a, the chromosomeregion with the miR-17 cluster is deleted in some humantumors. This same region, however, is amplified in diffuselarge B-cell lymphoma [38]. Thus the same miRNAs mayhave a tumor suppressor or oncogenic effects dependingupon the cell type in which they are expressed. It is alsoimportant to keep in mind that when multiple miRNAsare co-expressed within the same tumor cells a systemicregulatory impact should be considered in a context ofmany regulatory targets that are affected simultaneously.

In the case of pancreatic cancer, we have found thatE2F1 is a predicted microRNA target as well as caspase3.E2F1 has been also validated experimentally as a target ofmultiple miRNAs [40]. In this regard, it is interesting tonote that similar complex regulatory responses were previ-ously reported for the members of the E2F family of tran-scription factors that can also have cell proliferationpromoting or pro-apoptotic effects in different cellularand regulatory contexts [41].

4. Concluding remarks

In this study we have addressed the problem of identify-ing major biological processes and signaling pathways thatare collectively targeted by co-expressed miRNAs in cancercells.

A statistical enrichment analysis of association of miR-NAs and GO categories allows one to determine specificcategories that are enriched with targets of co-expressedmiRNAs. This approach allowed us to analyze miRNAexpression profiling data by reducing a very large raw listof predicted target genes to a smaller subset of target genesfrom significantly enriched GO categories.

We have tested an idea that additional trimming of theover-represented GO categories on the total number of hitsby multiple miRNAs would allow determination of thosebiological functions that were affected the most by a groupof differentially expressed miRNAs and that were morespecific for each particular cancer.

Using a combinatorial target prediction algorithm wehave found GO categories as well as biological functions,disease categories, toxicological categories, and regulatorypathways that are: targeted by multiple miRNAs; statisti-cally significantly enriched with target genes; and knownto be affected in specific cancers. Importantly, severalwell-known cancer-related genes such as k-ras, Bcl-2, andE2F1 that we have identified in our analysis, have beenalready validated in wet lab experiments and were reportedby others as targets of multiple miRNAs [34,38,40]. Over-all, in our analysis we identified 25 known cancer-related

genes that have been already experimentally validated astargets of miRNAs.

Our pathway analysis suggests that co-expressed miR-NAs seem to collectively target a broad range of down-stream signaling cascades and down regulating expressionof genes in abnormally activated pathways. Such computa-tionally inspired hypothesis could be tested experimentallyby comparing microRNA expression data with mRNAexpression data and protein expression data from the shortlist of predicted pathways.

Our analysis of predicted miRNA targets in five humancancers demonstrate that over-expressed miRNAs mightcollectively provide systemic compensatory response tothe abnormal functional and phenotypic changes in cancercells by targeting a broad range of functional categoriesand abnormally activated pathways known to be affectedin a particular cancer.

It is important to note that while our conclusions arebased solely on results of computational analysis andrequire further experimental validation, the computationalmethods presented in this report provide useful tools thatcan aid in functional profiling and biological interpretationof miRNA expression data.

Competing interests

Author declares that he has no competing interests.

Acknowledgment

We thank Daniel Brackett for critical discussions andencouraging support of this research project.

Appendix A. Supplementary data

Supplementary data associated with this article can befound, in the online version, at doi:10.1016/j.ymeth.2007.10.005.

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