an integrated analysis of mirna, lncrna, and mrna expression profiles

Research ArticleAn Integrated Analysis of miRNA lncRNA and mRNAExpression Profiles

Li Guo Yang Zhao Sheng Yang Hui Zhang and Feng Chen

Department of Epidemiology and Biostatistics School of Public Health Nanjing Medical University Nanjing 211166 China

Correspondence should be addressed to Li Guo gl8008163com and Feng Chen fengchennjmueducn

Received 7 March 2014 Revised 24 April 2014 Accepted 25 April 2014 Published 18 June 2014

Academic Editor Jiangning Song

Copyright copy 2014 Li Guo et al This is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Increasing amounts of evidence indicate that noncodingRNAs (ncRNAs) have important roles in various biological processesHeremiRNA lncRNA andmRNAexpression profileswere analyzed in humanHepG2 andL02 cells using high-throughput technologiesAn integrativemethodwas developed to identify possible functional relationships between different RNAmoleculesThe dominantderegulated miRNAs were prone to be downregulated in tumor cells and the most abnormal mRNAs and lncRNAs were alwaysupregulated However the genome-wide analysis of differentially expressed RNA species did not show significant bias between up-and downregulated populations miRNA-mRNA interaction was performed based on their regulatory relationships and miRNA-lncRNA and mRNA-lncRNA interactions were thoroughly surveyed and identified based on their locational distributions andsequence correlations Aberrantly expressed miRNAs were further analyzed based on their multiple isomiRs IsomiR repertoiresand expression patterns were varied across miRNA loci Several specific miRNA loci showed differences between tumor andnormal cells especially with respect to abnormally expressed miRNA species These findings suggest that isomiR repertoires andexpression patterns might contribute to tumorigenesis through different biological roles Systematic and integrative analysis ofdifferent RNA molecules with potential cross-talk may make great contributions to the unveiling of the complex mechanismsunderlying tumorigenesis

1 Introduction

Large-scale genome-wide analyses have indicated that muchof the human genome is transcribed yielding a great manynonexonic transcripts [1 2] These nonribosomal and non-mitochondrial RNAs which are metaphorically consideredribosomal dark matter are quite abundant in cells Thetranscription profile of the entire genome at a specific spaceand time can be obtained using microarray and sequencingtechnologies [3] Noncoding RNAs (ncRNAs) includingmicroRNAs (miRNAs) and long noncoding RNAs (lncR-NAs) can be obtained to attract considerable attention ofresearchers in many fields

miRNAs a class of small ncRNAs (asymp22 nt) are highlyimportant regulatory molecules and they have seen a greatdeal of study [4 5] Posttranscriptional gene regulation viamiRNA is crucial to the regulation of gene expressionThese small single-stranded RNAs negatively regulate gene

expression through partial base-pairing with target messen-ger RNAs (mRNAs) This influences the process of mRNAdegradation or repression of translation [4 6] They havemultiple roles in various biological processes that affect basiccellular functions including cell proliferation differentiationdeath and tumorigenesis [7] Abnormal expression of spe-cific miRNAs has been characterized as a common feature ofhuman diseases especially for malignancies In these casesgenes encoding miRNAs may act as oncogenes oncomiRsor tumor suppressors [7ndash9] Widely concerned lncRNAs arenormally longer than 200 nucleotides Studies have shownthem to be involved in a broad range of important cellularprocesses including chromatin modification RNA process-ing and gene transcription and that they do so throughinteraction with DNA and proteins [10ndash14] LncRNAs arecharacterized as complex diverse ncRNAs They are usuallyinvolved in exons and introns and have 51015840 cap and some of thefeatures of mRNAs [15]The larger ncRNAs have been shown

Hindawi Publishing CorporationBioMed Research InternationalVolume 2014 Article ID 345605 12 pageshttpdxdoiorg1011552014345605

2 BioMed Research International

to regulate gene expression through various mechanismssuch as complementary binding to protein-coding transcriptsin the form of cis-antisense lncRNAs [16ndash20] They alsomodulate transcription factors by acting as coregulators[19 21ndash25] Dysregulation of ncRNAs contributes to manybiological processes by interfering with gene expression Inrecent years this has become a hot research topic as coreregulatory molecules

Although the many biological roles of ncRNAs havedrawn a great deal of concern systematic and integrativeanalyses of many kinds of RNA molecules (including func-tional mRNAs) have been rare An integrated genome-wideanalysis involving many different RNA molecule levels isnecessary if the complex regulatory network and mecha-nisms underlying tumorigenesis are to be understood Weever performed analyses of miRNA-mRNA and miRNA-miRNA interactions using miRNA and mRNA expressionprofiles [26] but it is not enough to further understandthe potential relationships between different RNAmoleculesespecially involving the novel concerned lncRNAs In thepresent study the close relationships between ncRNAs andmRNAs were examined through simultaneously profilingof miRNA lncRNA and mRNA in HepG2 and L02 cellsusing high-throughput technologies An integrative methodof analysis was developed to detect and comprehensivelyanalyze the relationships between RNAmolecules especiallybetween abnormally expressed miRNA lncRNA andmRNAmolecules in tumor cells A systematic analysis of miRNAswas also performed at the isomiR level The results ofthe present study will enrich the genome-wide analysis ofdifferent molecules with potential cross-talk and contributeto further systematic studies of tumorigenesis

2 Materials and Methods

21 Cell Culture and RNA Isolation HepG2 and L02 cellswere obtained from the American Type Tissue CollectionThese were maintained in DMEM containing 10 FBS100UmL benzylpenicillin and 100UmL streptomycin at37∘C in a humidified 95 air 5 CO

2incubator Total RNAs

were isolated using TRIzol reagent (Invitrogen) according tothe manufacturerrsquos protocol

22 Small RNA Sequencing and Microarray ExperimentsTotal RNA from each sample was used to prepare thesmall RNA sequencing library to perform sequencing ona Genome Analyzer IIx which was used in accordancewith the manufacturerrsquos instructions and was prepared formicroarray hybridization The raw small RNA sequencingdata can be available in the Sequence Read Archive (SRA)database (httpwwwncbinlmnihgovsra accession num-ber SRA 1262121) and the mRNA and lncRNA microarraydata can be available in the European Molecular Biology Laboratory-Euro-pean Bioinformatics Institute (EMBL-EBI)database (httpwwwebiacuk)

23 Data Analysis The total raw miRNA sequencing readswere first filtered using a Solexa CHASTITY quality control

filter The remaining sequencing reads were deleted the 31015840adapter sequences and tags shorter than 15 nt were discardedThen the reads were aligned to the known human miRNAprecursors (pre-miRNAs) in the miRBase database (Release180 httpwwwmirbaseorg) using Novoalign software(v20711 httpwwwnovocraftcom) [27] Only one mis-matchwas allowed Readswith counts under 2were discardedwhenmiRNA expression was calculated According to recentreports on multiple isomiRs from a given miRNA locus [28ndash34] isomiRs (including those isomiRs with 31015840 nontemplateadditional nucleotides) were also comprehensively surveyedSequences that matched the pre-miRNAs in the maturemiRNA regionplusmn4 nt (nomore than 1mismatch) were definedas isomiRs The original sequence counts of miRNAs werenormalized to RPM (reads per million) and miRNA expres-sion analysis was performed based on these normalized dataat the miRNA and isomiR level

Images from microarray were analyzed with AgilentFeature Extraction software (version 10731) Raw signalintensities of mRNAs and lncRNAs were normalized usingthe quantile method and the GeneSpring GX v120 softwarepackage (Agilent Technologies) After quantile normalizationof the raw data lncRNAs and mRNAs for which 2 out of 2samples had flags in the present or marginal were chosen forfurther data analysis

Fold change was calculated to assess expressed miRNAprofiles that were differentially expressed between the twosamples atmiRNA and isomiR levels Differentially expressedlncRNAs and mRNAs were also identified through foldchange filtering To obtain abnormal ncRNAmRNA speciesand filter out rare species with lower expression levels foldchange values were assessed by adding an additional lownumber (10 units) based on normalized datasets Hierarchicalclustering was performed using Cluster bb30 and TreeView160 programs (httpranalblgoveisen) [35 36] Experi-mentally validated target mRNAs of aberrantly expressedmiRNAs were collected from the miRTarBase database [37]For miRNAs with few or no validated target mRNAs theputative target mRNAs were integrated using the predic-tion software programs Pictar TargetScan and miRandaprograms [38] The threshold values were simultaneouslycontrolled (eg in TargetScan the threshold of total con-text score was less minus030) The collected target mRNAswere further screened based on abnormally expressedmRNA profiles Then pathway and GO analysis were usedto determine the roles of these differentially expressedmRNAs Using CapitalBioMolecule Annotation SystemV40(MAS httpbioinfocapitalbiocommas3) further func-tional enrichment analysis was performed Functional inter-action networks were constructed using Cytoscape v282Platform [39]

24 Schema for Integrative Analysis of ncRNA-mRNA DatancRNA-mRNA integrative analysis was performed accordingto Figure 1 The approach included three steps First profilesof aberrantly expressed miRNA mRNA and lncRNA inHepG2 cells were comprehensively surveyed using high-throughput datasets A profile of aberrantly expressed

BioMed Research International 3

HepG2 (tumor cell) L02 (normal cell)

miRNA profiling (sequencing)mRNA and lncRNA profiling (microarray)

AbnormalmiRNA profile

AbnormalmRNA profile

AbnormallncRNA profile

Validated or putative targetmRNAs

Potential regulated mRNAsand related miRNAs

Integrative network and expression analysis a robustregulatory network

GOpathwayanalysis

miRNAisomiR level

Figure 1 Integrative analysis of ncRNA-mRNA

isomiRs was obtained at the same time Pathway and GOanalyses were performed for abnormal mRNA and thetarget mRNAs of abnormal miRNAs Second systematicbioinformatic analysis was developed based on possiblefunctional relationships between these molecules miRNAand mRNA were analyzed in an integrated fashion basedon experimentally validated or predicted target mRNAs andon their levels of enrichment Possible internal relationshipsamong lncRNA-mRNA and lncRNA-miRNA were identifiedbased on their locational distributions and the relationshipsbetween their sequences Finally integrative regulatory net-work and expression analyses were performed at differentmolecular levels based on their possible levels of expressionand functional relationships (Figure 1)

3 Results

31 Aberrantly Expressed miRNA and isomiR Profiles Asexpected 22 nt was the most common length (see FigureS1A and Figure S1B in Supplementary Material availableonline at httpdxdoiorg1011552014345605) As found byGuo et al consistent aberrantly expressed miRNAs wereobtained based on the most abundant isomiR and all theisomiRs respectively [34] However despite the consistencyof the dysregulation pattern the fold change (log 2) of somemiRNAs was found to cover a wide range (miR-200b-3p856 and 545 miR-100-5p minus727 and minus488) (Table 1) Half ofthe most dominant isomiR sequences had lengths that weredifferent from those of canonical miRNA sequences (datanot shown) Generally the most dominant isomiR sequencemay be longer or shorter than registered miRNA sequencethrough altering 51015840 and 31015840 ends especially for the 31015840 ends(Figure S1C) These abundantly and abnormally expressedmiRNAs were always located on a few specific chromosomesespecially chromosome 9 (Figure S1D) The distribution biaswas obvious even though multicopy pre-miRNAs were alsoanalyzed Downregulated miRNAs were found to be more

common than upregulated species although the total num-bers remained similar across the whole abnormal miRNAprofiles

IsomiR repertoires and expression profiles in the HepG2and L02 cells were also analyzed Various isomiR repertoiresand expression patterns were detected in different miRNAloci (Figure 2) As found by Guo et al several dominantisomiRs (always 1ndash3) were yielded per miRNA locus dueto alternative and imprecise cleavage of Drosha and Dicer[34 40] Deregulated miRNAs might show abnormal isomiRexpression profiles in tumor cells such asmiR-194-5p (upreg-ulated) and miR-24-3p (downregulated) (Figure 2) Thesefindings indicated inconsistent dominant isomiR sequenceseven though they were always 51015840 isomiRs with the same 51015840ends and seed sequences This phenomenon was detectedprimarily in deregulated miRNAs Generally those stablyexpressed species had similar expression profiles betweentumor and normal cells This was true of miR-26a-5p andmiR-21-5p (Figure 2) Of the dominant isomiRs only miR-15a-5p was involved in 31015840 addition (Figure 2) Although 31015840addition was quite widespread especially for adenine anduracil the presence of type of isomiRs with 31015840 additions sug-gested considerable divergence between miRNAs For exam-ple miR-103a-3p was not detected any modified isomiRseven though 10 isomiRs were obtained while three isomiRswith 31015840 additions were found in miR-194-5p (Figure 2) Atthe miRNA locus these modified isomiRs always possessedlower enrichment levels than dominant isomiR sequencesalthough they might still show high levels of expression

Functional enrichment analysis was performed based ontargets that had been found to be regulated by at least 2abnormal miRNAs The results suggested that these miR-NAs play important roles in essential biological processesincluding the cell cycle Wnt and the MAPK signalingpathway (Table 2) They also contribute to various humandiseases such as chronic myeloid leukemia prostate cancerand bladder cancer According to identified mRNA profilesby using microarray technology these target mRNAs mightbe stably expressed upregulated or downregulated in tumorcells

32 Aberrantly Expressed mRNA and lncRNA Profiles Dom-inant (gt10 of normalized data) and significantly differ-entially expressed (fold change (log 2) gt40 or ltminus40)mRNAs and lncRNAs (the top deregulated species couldbe found in Table 3) were collected Significant divergencewas detected between upregulated and downregulated RNAspecies 8398 of deregulated mRNAs and 9093 dereg-ulated lncRNAs were upregulated in tumor cells Howeverthe analysis of differentially expressed profiles suggested that6206 of mRNAs and 6649 of lncRNAs were downregu-lated Locational distributions of mRNA and lncRNA wereanalyzed Consistent distribution patterns were detectedand no bias was found between deregulated mRNA andlncRNA species (Figures S2A S2B and S2C) Howeverinconsistent distributions were detected between the top 100dominant and deregulated mRNAs and lncRNAs (FiguresS2D and S2E) These abnormal species were prone to locate


Table 1 Differentially expressed abundant miRNA species as indicated by the most abundant isomiR and all isomiRs

miRNA Chr Consistent or inconsistent Fold change (the most) Fold change (all isomiRs) Updownlet-7a-5p 9 11 22 Yes minus235 minus324 Downlet-7f-5p 9 X Yes minus254 minus297 DownmiR-103a-3p 5 20 Yes 233 205 UpmiR-146a-5p 5 Yes 728 552 UpmiR-15a-5p 13 No minus485 minus387 DownmiR-194-5p 1 11 No 726 456 UpmiR-200b-3p 1 No 856 545 UpmiR-23a-3p 19 No minus719 minus502 DownmiR-24-3p 9 19 No minus424 minus214 DownmiR-27a-3p 19 Yes minus661 minus635 DownmiR-27b-3p 9 Yes minus182 minus261 DownmiR-100-5p 11 Yes minus727 minus488 DownmiR-425-5p 3 No 298 200 UpThese miRNAs are abundantly expressed in HepG2 and L02 cellsThey are the top downregulated and upregulated miRNAs in cancer cells (fold change (log 2)gt20 or ltminus20) Chr indicates the genomic locations of the miRNA genes (pre-miRNAs) including multicopy pre-miRNAs let-7a-5p is located on chr9 (let-7a-1) 11 (let-7a-2) and 22 (let-7a-3)The term ldquoconsistentrdquo indicates that the sequence of the most abundant isomiR is the same as that of the reference miRNAsequence in the miRBase database The term ldquomostrdquo indicates the most abundant isomiR from a given locus The term ldquoall isomiRsrdquo indicates total number ofisomiRs from a given locus

Table 2 Pathway enrichment analysis of experimentally validated mRNA targets of dominant deregulated miRNAs

Pathway Number 119875 value Target genes

Cell cycle 18 301119864 minus 30

ATM CCNA2 CCND1 CCND2 CCNE1 CDC25A CDK6 CDKN1ACDKN1B CDKN2A E2F1 E2F2 E2F3 EP300 RB1 RBL2 TP53 WEE1

Chronic myeloid leukemia 15 274119864 minus 27

ACVR1C AKT1 CCND1 CDK6 CDKN1A CDKN1B CDKN2A E2F1E2F2 E2F3 MYC NFKB1 NRAS RB1 TP53

Prostate cancer 15 374119864 minus 26

AKT1 BCL2 CCND1 CCNE1 CDKN1A CDKN1B E2F1 E2F2 E2F3EP300 IGF1R NFKB1 NRAS RB1 TP53

Pancreatic cancer 14 303119864 minus 25

ACVR1C AKT1 CCND1 CDC42 CDK6 CDKN2A E2F1 E2F2 E2F3NFKB1 RAC1 RB1 TP53 VEGFA

Bladder cancer 13 147119864 minus 26

CCND1 CDKN1A CDKN2A E2F1 E2F2 E2F3 FGFR3 MYC NRAS RB1THBS1 TP53 VEGFA

Melanoma 13 321119864 minus 23

AKT1 CCND1 CDK6 CDKN1A CDKN2A E2F1 E2F2 E2F3 IGF1RMET NRAS RB1 TP53



Small-cell lung cancer 13 471119864 minus 22

AKT1 BCL2 CCND1 CCNE1 CDK6 CDKN1B E2F1 E2F2 E2F3 MYCNFKB1 RB1 TP53

Focal adhesion 13 565119864 minus 17

AKT1 BCL2 CCND1 CCND2 CDC42 PAK3 IGF1RMET RAC1 RHOAROCK1 THBS1 VEGFA

Glioma 12 149119864 minus 21

AKT1 CCND1 CDK6 CDKN1A CDKN2A E2F1 E2F2 E2F3 IGF1RNRAS RB1 TP53

Nonsmall cell lung cancer 10 377119864 minus 18 AKT1 CCND1 CDK6 CDKN2A E2F1 E2F2 E2F3 NRAS RB1 TP53Renal cell carcinoma 10 711119864 minus 17 AKT1 CDC42 PAK3 EP300 ETS1 HIF1AMET NRAS RAC1 VEGFA

Axon guidance 10 377119864 minus 14

CDC42 PAK3 CXCL12 CXCR4MET NFAT5 NRAS RAC1 RHOAROCK1

p53 signaling pathway 9 183119864 minus 14 ATM CCND1 CCND2 CCNE1 CDK6 CDKN1A CDKN2A THBS1 TP53Colorectal cancer 9 102119864 minus 13 ACVR1C AKT1 BCL2 CCND1 IGF1RMET MYC RAC1 TP53Wnt signaling pathway 9 188119864 minus 11 CCND1 CCND2 EP300 MYC NFAT5 RAC1 RHOA ROCK1 TP53MAPK signaling pathway 9 268119864 minus 09 ACVR1C AKT1 CDC42 FGFR3 MYC NFKB1 NRAS RAC1 TP53Adherens junction 8 404119864 minus 12 ACVR1C CDC42 EP300 IGF1RMET RAC1 RHOA WASF3These target mRNAs are found to be regulated by at least 2 abnormal miRNAs each Bold type indicates upregulation Underlining indicates downregulationOther fonts indicate stable expression or undetectable levels


0

50

100

1 2 3 4 5 6 7 8 9 100

50

100

1 2 3 4 5 6 7 9 11A U0

50

100

1 4 5 7 8 9 10A U U

0

50

100

1 2 3 5 8 10 11 12 14U U UC U0

50

100

1 2 4 5 6 7 11 12A A C U UA0

50

100

1 2 4 5 7 8 9 11A AA

0

50

100

1 4 5 6A A A0

50

100

1 3 7 9 13 23

AU

A AA

U U

U UG U0

50

100

1 3 5 9 11 13 15 19 21 23 25 27AA

AU A

0

50

100

1 2 3 4 7 9 10 11 12 13 16A AC UU

0

50

100

1 2 3 4 5 8A U0

100

200

1 2 3 4

0

50

100

1 4 7 10 16 19 22 25 28 31 40

AA

A

U

U U UA

G

UA G

0

50

100

1 3 9 11 13 15 17 21A A AUA U

0

50

100

1 2 3 4 5 8 9 10 11UA A

0

50

100

1 2 3 4 5 6 U0

50

1 3 5 7 9 11 13 15 17 19 21 27 29 31 33 35 41 43 49 51 53 55 57 59

100

C U

G

A G

U A

G

C

A

miR-103a-3p (up) miR-146a-5p (up)miR-194-5p (up)

miR-425-5p (up)miR-200b-3p (up) let-7a-5p (down)

miR-15a-5p (down) miR-23a-3p (down)let-7f-5p (down)

miR-24-3p (down)miR-27b-3p (down)

miR-27a-3p (down)

miR-100-5p miR-19b-3pmiR-26a-5p

miR-182-5p miR-21-5p

(down)

Figure 2 Various isomiR profiles These miRNAs are the most abundantly up- (green) or downregulated (purple) miRNAs among thoseexamined here Stably expressed miRNAs are also shown (black) The ordinate axis indicates the relative amount of expression in a specificmiRNA locus and the horizontal axis indicates various types of isomiRs Blue lines indicate isomiRs from HepG2 cells and the red lineindicates isomiRs from L02 cells Some miRNAs with 31015840 additional nucleotides are also highlighted in the horizontal axis Stably expressedmiRNAs show similar patterns of expression and deregulated miRNAs show various patterns of expression in those cells The figure onlylists isomiRs (gt50 for deregulated miRNAs gt100 for stably expressed miRNAs) for which normalized data was available

on chromosomes 6 and 12 (mRNA) and chromosomes 4 and5 (lncRNA)

Significantly deregulated mRNAs and lncRNAs werefound to be prone to be located on chromosomes 1 and 2especially upregulated species (Figures 3(a) 3(b) and 3(c))Upregulation was found to be more common than down-regulation (Figure 3) Generally no significant distributionbias was found between sense and antisense strands (Figures3(a) and 3(b)) Coding and noncoding RNAs indicatedsimilar ratios of downregulated species while they showeddiversity of upregulated species (Figure 3(c)) Inconsistent

locational distribution patterns were detected based on thetotal number of down- and upregulated mRNA and lncRNAspecies (Figure 3(d))

Thepathway andGOanalysis of abnormalmRNAexpres-sion profiles showed various results (Figure 4 and FigureS3) Downregulated mRNAs were prone to be found inpathways of regulation of actin cytoskeleton and pathwaysin cancer and upregulated mRNAs contributed to the bio-logical processes of ribosomes and spliceosomes (Figure 4)Dominant abnormal mRNAs were collected for functionalenrichment analysis Some of them had important roles in


0

200

40

80

120

160

chr1

chr9

chr8

chr7

chr6

chr5

chr4

chr3

chr2

chr22

chr21

chr20

chr19

chr18

chr17

chr16

chr15

chr14

chr13

chr12

chr11

chr10

chrY

chrX

chrM

(b)

chr1

chr9

chr7

chr5

chr3

chr21

chr19

chr17

chr15

chr13

chr11

chrX

chr1

chr9

chr7

chr5

chr3

chr21

chr19

chr17

chr15

chr13

chr11

chrX

chrM

mRNA

0

2

4

6

8

Downregulated Upregulated

(c)

mRNA

024681012

(d)

(a)

chr1

chr8chr7chr6

chr5chr4chr3chr2

chr18chr17chr16chr15chr14chr13chr12chr11chr10

chr9

chrYchrX

chr22chr21chr20chr19

0 15050 100

Sense strandAntisense strandUpregulatedDownregulated

mRN

A

Sense strandAntisense strand

UpregulatedDownregulated

chr1

chr9

chr8

chr7

chr6

chr5

chr4

chr3

chr2

chr22

chr21

chr20

chr19

chr18

chr17

chr16

chr15

chr14

chr13

chr12

chr11

chr10

chrY

chrX

chrM

Perc

enta

ge (

)Pe

rcen

tage

()

lncRNA

lncRNA

lncRNA

Figure 3 Distribution of aberrantly expressed mRNA and lncRNA profiles Location distributions of aberrantly expressed (a) mRNA and(b) lncRNA profiles on human chromosomes The number of deregulated species including detailed up- and downregulated mRNAs andlncRNAs is given with respect to sense and antisense strands respectively ldquochrrdquo chromosome ldquochrMrdquo mitochondrial chromosome (c)Distribution of upregulated and downregulated species (d) Distribution of total deregulated mRNAs and lncRNAs

diverse essential biological processes through involvement inthe pathways including purine metabolism ribosomes thecell cycle glycolysis and gluconeogenesis (Table S1) Theyalso contributed to occurrence and development of somehuman diseases such as Parkinsonrsquos disease and small-celllung cancer

33 ncRNA-mRNA Data Integration and Interactive Regula-tors in Tumorigenesis According to functional enrichmentanalysis of abnormal miRNAs and mRNAs the commonpathways could be obtained using different genes (Table S2)This was mainly attributable to the selected threshold valuesof analyzed miRNA and mRNA species Not all dominantderegulated miRNAs and mRNAs had direct relationships

An analysis of miRNA-mRNA interactions showed acomplex regulatory network (Figure 5) mRNAs that were

regulated by at least 2 abnormally expressed miRNAs werecollected Generally they were prone to form closed net-works with close regulatory relationships Some miRNAssuch as let-7a-5p and miR-15a-5p were located in the cen-tral positions with multiple target mRNAs Although smallregulatory molecules were downregulated or upregulatedtheir target mRNAs might show consistent or inconsistentdysregulation patterns (Figure 5) Locational relationshipsindicated that related lncRNAs were also constructed in theregulatory network Some miRNAs such as miR-24-3p (mir-24-2 gene is located in BX640708) always showed consistentderegulation patterns with their host lncRNAs (Figure 5)Several mRNAs were also found to be related to nearbylncRNAs mRNA and associated lncRNA might be locatedon the same strand or have a senseantisense relationshipwithin a specific genomic region mRNA-lncRNA might


Regulation of actin cytoskeleton

Pathways in cancer

MAPK signaling pathway

Endocytosis

Tuberculosis

Viral myocarditis

Focal adhesion

Axon guidance

Melanoma

Endometrial cancer

Significant pathway

0 1 2 3 4 5 6

Enrichment score (minuslog10(P value))

(a) Down

Ribosome

Spliceosome

Proteasome

Arginine and proline metabolism

Glutathione metabolism

Oxidative phosphorylation

RNA degradation

Parkinsonrsquos disease

Significant pathway

0 2 4 6 8

Protein processing in endoplasmic reticulum

Valine leucine and isoleucine degradation


(b) Up

Significant GO termsVentricular septum development

Cardiac septum morphogenesis

Cardiac septum development

Activation of Janus kinase activity

Cardiac atrium morphogenesis

Cardiac atrium development

Cardioblast differentiation

00 05 10 15 20 25 30

Fold enrichment

differentiation

immune response

Negative regulation of cell-cell adhesion

Regulation of T-helper cell

Regulation of T-helper 1 type

(c) Down

Significant GO terms

00 05 10 15 20 25 30 35

Fold enrichment

Polyamine biosynthetic process

Polyamine metabolic process

Regulation of coenzyme metabolic process

Regulation of cofactor metabolic process

Protein-lipid complex assembly

Plasma lipoprotein particle assembly

Melanosome organization

Regulation of acetyl-CoA biosynthetic process from pyruvate

Dolichol-linked oligosaccharide biosynthetic process

Pteridine-containing compoundbiosynthetic process

Cotranslational protein targeting to membrane

Acetyl-CoA biosynthetic process from pyruvate

(d) Up

Figure 4 Analysis of significant pathways andGO terms regarding biological processes associated with abnormally expressedmRNAprofilesin HepG2 cellsThe 119875 value denotes the significance of the pathway correlated to the conditions and GO term (the recommend 119875 value cutoffis 005)

show consistent (APP amp AP0014392) or inconsistent (E2F2amp AL0211543) deregulation patterns (Figure 5)

To identify the overall patterns of deregulation betweenmRNAsmiRNAs and lncRNAs a comprehensive survey oftheir potential relationships was performed incorporatinginformation regardingmRNAsmiRNAs and lncRNAsMostmRNA-lncRNA pairs had senseantisense relationships andmiRNA-lncRNA pairs were prone to be located on the samestrands (Table S3) Generally these mRNAmiRNA-lncRNApairs could completely or partially overlap (from the samestrands) or show reverse complementarily binding (fromsenseantisense strands) The mRNA and lncRNA couldshow the same or different deregulation patterns but theywere usually the same (Figure 6) Some pairs were up- or

downregulated and their fold change values differed (Figures6(a) and 6(b)) These RNA molecules both coding RNAswhich are functionalmolecules and noncoding RNAs whichare regulatory molecules were prone to be downregulated intumor cells

4 Discussion

Although the recorded values of differences in expressionas defined as the abundance of isomiRs expression levelsof isomiRs may have been influenced by higher sensitivityof next-generation sequencing technology the diversity ofthose expression is mainly attributable to differences in theisomiR profiles and expression patterns in normal and tumor


APP BCL2 CCND2

MYCE2F2

CCNA2

EP300PTPN12

BRCA1 CCNE1

DICER1MMP13 PRDM1

100-5p27b-3p let-7f-5p 24-3p

let-7a-5p

24-3p

15a-5p

194-5p200b-3p

103a-3p146a-5p

BX640708

BX640708

DLEU2

AP0014392

AL0211543

NF120581B1

Figure 5 Interaction between deregulated ncRNA-mRNA in HepG2 cells Each ellipse indicates up- (purple) or downregulated (lightgreen) miRNA Each square indicates up- (red) or downregulated (green) mRNA (stably expressed mRNAs are in white) and each tiltedrectangle indicates downregulated lncRNAs (pale green) These miRNAs are shown in Table 1 A regulatory network was constructed usingexperimentally validated target mRNAs (each mRNA is regulated by at least 2 deregulated miRNAs) Stably expressed mRNAs are shown inwhite squares (BCL2MMP13 and PTPN12) mRNAs not detected in HepG2 and L02 cells are also shown in white squares (DICER1 EP300andMYC)

Table 3 Differentially expressed abundant mRNA and lncRNA species

mRNAlncRNA Gene symbol Chr (plusmn) HepG2 (Nor) L02 (Nor) Fold change UpdownmRNA RBP4 chr10 (minus) 1641 490 1151 UpmRNA APOA1 chr11 (minus) 1507 544 964 UpmRNA ALB chr4 (+) 1419 465 954 UpmRNA ID2 chr2 (+) 1352 416 935 UpmRNA TFPI chr2 (minus) 1326 429 897 UpmRNA SERPINA3 chr14 (+) 1415 542 873 UpmRNA SRGN chr10 (+) 526 1359 minus833 DownmRNA CD81 chr11 (+) 466 1299 minus833 DownmRNA FOLR1 chr11 (+) 500 1450 minus950 DownmRNA NNMT chr11 (+) 374 1328 minus954 DownmRNA C11orf86 chr11 (+) 426 1516 minus1090 DownmRNA BASP1 chr5 (+) 553 1706 minus1153 DownlncRNA RP11-113C121 chr12 (minus) 1271 543 728 UplncRNA D28359 chr13 (+) 1395 663 731 UplncRNA MGC12916 chr17 (+) 1125 391 733 UplncRNA ABCC6P1 chr16 (+) 1228 489 739 UplncRNA lincRNA-HEY1 chr8 (minus) 1212 470 742 UplncRNA HSPEP1 chr20 (minus) 1322 560 761 UplncRNA TMSL6 chr20 (minus) 825 1608 minus783 DownlncRNA RP11-163G103 chr1 (minus) 818 1592 minus774 DownlncRNA AC0109073 chr2 (minus) 744 1507 minus764 DownlncRNA BC106081 chr8 (minus) 370 1081 minus711 DownlncRNA nc-HOXA11-86 chr7 (+) 383 1065 minus682 DownlncRNA AK054970 chr13 (+) 601 1264 minus662 DownThe table only lists the top 6 up- and downregulated mRNAs and lncRNAs based on the fold change values (log 2)These mRNAs and lncRNAs are dominantlyexpressed ldquoChr (plusmn)rdquo indicates genomic location on sense or antisense strands of human chromosomes ldquoNorrdquo indicates the normalized data


mRNA ALB (954)5998400 lncRNA AF130077 (318)

mRNA CD81 (minus833)

5998400

Chr4

5998400

Chr11

5998400 lncRNA AC1299295 (minus102)

Position 74 269 971-74 287 127

Position 74 270 077-74 286 973

Position 2 398 546-2 418 649

Position 2 349 978-2 399 219

(a)

Chr175998400 lncRNA MGC12916 (733)

mRNA HS3ST3B1 (minus163)

mRNA COLEC11 (minus109)Position 3 642 636-3 692 045

Position 3 665 137-3 665 3475998400 lncRNA AC0109073 (minus764)

5998400

Chr2

Position (lncRNA) 14 207056-14 209 061

Position (mRNA) 14 204505-14 249 492

(b)

0200400600800

1000

Down Up Down UpmRNA lncRNA

(c)

0

200

400

600

800

Consistent Inconsistent

(d)

0100200300400500600

Up-up

mRNA-lncRNA

Down-down

Down-up

Up-down

(e)

05

10152025

Down Up Down UpmiRNA lncRNA

The-sameThe-different

(f)

0

5

10

15

20



(g)

0

5

10

15

miRNA-lncRNA

Up-up Down-down

Down-up

Up-down


(h)

Figure 6 Integrative expression analysis ofmRNA-lncRNA andmiRNA-lncRNAbased on their locations ((a)-(b)) Schematic representationof expression of mRNA-lncRNA (Table 3) Some of these mRNA genes and lncRNA genes are always located on senseantisense strands inspecific genomic regionsOthers are located in the same genomic region but are of different lengths Some show the samederegulation patternswith different fold changes (log 2) (upregulation red downregulation blue) and others showdifferent deregulation patterns ((c)ndash(e))mRNA-lncRNA integrative analysis based on genomic locationThe ordinate axis indicates the number of deregulated mRNAs or lncRNAsThe termldquothe-samerdquo indicates that mRNA and lncRNA are located on the same strandThe term ldquothe-differentrdquo indicates that mRNA and lncRNA arelocated on senseantisense strands ((f)ndash(h)) miRNA-lncRNA integrative analysis based on genomic location The ordinate axis indicates thenumber of deregulated miRNAs or lncRNAs The term ldquothe-samerdquo indicates that miRNA and lncRNA are located on the same strand Theterm ldquothe-differentrdquo indicates that miRNA and lncRNA are located on senseantisense strands

cells (Figure S4) Indeed the most dominant isomiRs arenot always canonical miRNA sequences The wide rangeof these inconsistent sequences was found to contribute tovarious isomiR repertoires leading to differences in expres-sion between the most abundant isomiR and other isomiRsHerein 31015840 addition is detected in many places but it is notalways present in the dominant sequences though it mayshow high level of expression (Figure 2 and Figure S4) Stablyexpressed miRNAs always show similar isomiR patternsand deregulated miRNA species are prone to show differentisomiR repertoires (Figure 2) [34] Generally isomiR profilesremain stable in different tissues [31 33 34] Deviant isomiRexpression profiles should not be considered random eventsThese results strongly suggest that the isomiR repertoiresand their patterns of expression might contribute to tumori-genesis through playing biological roles [34] Collectively

the detailed isomiR repertoires might serve as markers andprovide information regarding the regulatory mechanisms ofsmall noncoding active molecules

miRNAs are small negative regulatory molecules Theycan suppress gene expression via mRNA degradation orrepression of translation [4 6] However integrative anal-ysis shows both consistent and inconsistent deregulationpatterns indicating complex regulatory networks containingboth noncoding RNAs and mRNAs (Figure 5) mRNAs arealways regulated by multiple miRNAs and vice versa Thedynamic expression patterns between miRNAs and mRNAsare more complex than had been believed Even thoughmultiple target mRNAs can be detected miRNA may reg-ulate specific mRNAs at specific times and at specific sitesCompetitive interactions between miRNA and mRNA maybe dynamic and involve complex regulatory mechanisms


in a specific microenvironment Dominant selection mayexist in miRNA and mRNA Selective dynamic flexibleinteractions may produce robust coding-noncoding RNAregulatory networks especially networks involving lncRNAsThe robust regulatory patterns contribute to normal biolog-ical processes Abnormal regulatory networks may produceaberrant pathways and even disease Specifically let-7-5p hasbeen experimentally validated as crucial regulatory moleculein hepatocellular carcinoma They can negative regulate Bcl-xL expression and strengthen sorafenib-induced apoptosis[41] and can contribute to protecting human hepatocytesfrom oxidant injury through regulating Bach1 [42] Commonpathways can be produced through enrichment analysis ofmiRNAs andmRNAs (Table S2) Significantly up- and down-regulated mRNAs show various pathways and GO terms(Figure 4 and Figure S3) The large number and variety ofbiological roles and their positions in pathways and networksindicate that theymay contribute to tumorigenesis Assessingthe actual interactionnetworks can be difficult in vivobecauseof dynamic expression althoughhigh-throughput techniquescan be used to track and construct whole-expression profiles

Both dominant and deregulated miRNAs are prone tobe located on specific chromosomes (Figure S1D) The biasmight implicate active transcription of specific regions orchromosomes However abnormal species do not showdistribution biases (Figure 3 and Figure S2) Among domi-nant aberrantly expressed miRNA downregulation is quitecommon Among mRNAs and lncRNAs upregulation ismore common All of these findings suggest consistent orcoexpression patterns shared bymRNAs and lncRNAsTheseare mainly derived from original transcription from genomicDNA sequences miRNAs and mRNAslncRNAs tended toshow opposite deregulation patterns Evidence suggests thatmiRNA can regulate lncRNA through methylation Forexample miR-29 can regulate the long noncoding geneMEG3 in hepatocellular cancer through promoter hyper-methylation [43] Moreover some miRNAs are encoded byexons of long noncoding transcripts [44 45] These miRNAsand their host gene lncRNAs may be cotranscribed andcoregulated These would include miR-31 and its host genelncRNA LOC554202 which in triple-negative breast cancerare regulated through promoter hypermethylation [46]

Noncoding RNA molecules especially miRNAs andlncRNAs are very prevalent regulatory molecules Theyhave been shown to play versatile roles in many biologicalprocesses These usually involve transcriptional regulationandmodulation of protein function [23] In the present studythe nearness or separation of ncRNAs and mRNAs on thechromosome is used to perform a comprehensive analysisResults show that mRNA-lncRNA pairs always have con-sistent or inconsistent deregulation patterns (Figures 5 and6 and Table S3) Although some pairs have senseantisenserelationships the same trends can be detected even atdifferences in fold change of far greater magnitude (log 2)(Figures 6(a) and 6(b)) The various fold change indicatesdifferent degrees of up- or downregulation between mRNAsand lncRNAs This information might be used to determinethe method of regulation The two members of each mRNA-lncRNA pair can overlap completely or partially (on the

same strand) Some of them can also form duplexes throughreverse complementary binding (from the senseantisensestrands) which may facilitate interactions between differentRNA molecules The pronounced divergence with respect tothe degree of deregulation might be attributable to differentregulatory methods although other complex mechanismsmay also be involved The mRNA and lncRNA from thesame strands sometimes show opposite deregulation trends(Figure 6(b)) although the fact that they are cotranscribedfrom the same genomic DNA sequence with similar originalexpression levels Complex negative regulatory networksespecially those involved in noncoding miRNAs and lncR-NAs contribute to the diversity of final relative expressionlevels in vivo Abnormal regulation in the coding-noncodingRNA network may be pivotal to tumorigenesis

miRNA-lncRNA pairs with locational relationships arealso surveyed The miRNA and lncRNA in these pairs aremore prone to be located on the same strand with completeoverlap than paired mRNA and lncRNA molecules are(Figure 6 and Table S3) They may show either consistent orinconsistent deregulation patterns but consistent patterns aremore common (Figure 6)The different levels of final enrich-ment may be attributable to degradation and regulatorymechanisms Diversity of abnormal half-life for specific RNAmolecules may cause the development of diseases Howeveralthough molecules may have different regulatory relation-ships a robust regulatory network can be detected especiallydue to multiple targets of each molecule Alternative reg-ulatory pathways particularly flexible candidate regulatorypathways and functional pathways indicate that the coding-noncoding RNA regulatory network is more complex thanhad been believed especially in different space and timeThepossible flexible relationships between molecules in variousplaces and at various times are crucial to determining themechanism underlying tumorigenesis

Abbreviations

ncRNA Noncoding RNAsmiRNA MicroRNAisomiR miRNA variantlncRNA Long noncoding RNAmRNA Messenger RNApre-miRNA Precursor miRNARPM Reads per million

Conflict of Interests

The authors declare no potential conflict of interests withrespect to the authorship andor publication of this paper

Authorsrsquo Contribution

Li Guo wrote the main paper text Li Guo and Feng Chendesigned the study Li Guo and Yang Zhao analyzed originaldatasets and Li Guo Sheng Yang and Hui Zhang preparedtables


Acknowledgments

This work was supported by the National Natural ScienceFoundation of China (nos 61301251 81072389 and 81373102)the Research Fund for the Doctoral Program of HigherEducation of China (nos 211323411002 and 20133234120009)the China Postdoctoral Science Foundation funded project(no 2012M521100) the key Grant of the Natural ScienceFoundation of the Jiangsu Higher Education Institutionsof China (no 10KJA33034) the National Natural ScienceFoundation of Jiangsu (no BK20130885) the Natural ScienceFoundation of the Jiangsu Higher Education Institutions(nos 12KJB310003 and 13KJB330003) the Jiangsu PlannedProjects for Postdoctoral Research Funds (no 1201022B) theScience and Technology Development Fund Key Project ofNanjing Medical University (no 2012NJMU001) and thePriority Academic Program Development of Jiangsu HigherEducation Institutions (PAPD)

References

[1] P Kapranov G St Laurent T Raz et al ldquoThe majority of totalnuclear-encoded non-ribosomal RNA in a human cell is ldquodarkmatterrdquo un-annotated RNArdquo BMC Biology vol 8 article 1492010

[2] H Jia M Osak G K Bogu L W Stanton R Johnson andL Lipovich ldquoGenome-wide computational identification andmanual annotation of human long noncoding RNA genesrdquoRNA vol 16 no 8 pp 1478ndash1487 2010

[3] P Kapranov J Cheng S Dike et al ldquoRNA maps reveal newRNA classes and a possible function for pervasive transcrip-tionrdquo Science vol 316 no 5830 pp 1484ndash1488 2007

[4] D P Bartel ldquoMicroRNAs genomics biogenesis mechanismand functionrdquo Cell vol 116 no 2 pp 281ndash297 2004

[5] D P Bartel andC Z Chen ldquoMicromanagers of gene expressionthe potentially widespread influence of metazoan microRNAsrdquoNature Reviews Genetics vol 5 no 5 pp 396ndash400 2004

[6] V Ambros ldquoThe functions of animal microRNAsrdquo Nature vol431 no 7006 pp 350ndash355 2004

[7] H W Hwang and J T Mendell ldquoMicroRNAs in cell prolif-eration cell death and tumorigenesisrdquo The British Journal ofCancer vol 94 no 6 pp 776ndash780 2006

[8] G A Calin M Ferracin A Cimmino et al ldquoA MicroRNAsignature associated with prognosis and progression in chroniclymphocytic leukemiardquo The New England Journal of Medicinevol 355 p 533 2006

[9] C Caldas and J D Brenton ldquoSizing up miRNAs as cancergenesrdquo Nature Medicine vol 11 no 7 pp 712ndash714 2005

[10] R A Gupta N Shah K C Wang et al ldquoLong non-codingRNA HOTAIR reprograms chromatin state to promote cancermetastasisrdquo Nature vol 464 no 7291 pp 1071ndash1076 2010

[11] K V Morris and P K Vogt ldquoLong antisense non-coding RNAsand their role in transcription and oncogenesisrdquo Cell Cycle vol9 no 13 pp 2544ndash2547 2010

[12] K C Wang and H Y Chang ldquoMolecular mechanisms of longnoncoding RNAsrdquo Molecular Cell vol 43 no 6 pp 904ndash9142011

[13] M Huarte M Guttman D Feldser et al ldquoA large intergenicnoncoding RNA induced by p53 mediates global gene repres-sion in the p53 responserdquo Cell vol 142 no 3 pp 409ndash419 2010

[14] A M Khalil M Guttman M Huarte et al ldquoMany humanlarge intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expressionrdquoProceedings ofthe National Academy of Sciences of the United States of Americavol 106 no 28 pp 11667ndash11672 2009

[15] P Carninci T Kasukawa S Katayama et al ldquoThe transcrip-tional landscape of the mammalian genomerdquo Science vol 309no 5740 pp 1559ndash1563 2005

[16] E Pasmant I Laurendeau D Heron M Vidaud D Vidaudand I Bieche ldquoCharacterization of a germ-line deletion includ-ing the entire INK4ARF locus in a melanoma-neural systemtumor family identification of ANRIL an antisense noncodingRNA whose expression coclusters with ARFrdquo Cancer Researchvol 67 no 8 pp 3963ndash3969 2007

[17] Y Kotake T Nakagawa K Kitagawa et al ldquoLong non-codingRNA ANRIL is required for the PRC2 recruitment to andsilencing of p15 INK4B tumor suppressor generdquo Oncogene vol30 no 16 pp 1956ndash1962 2011

[18] S Tochitani andYHayashizaki ldquoNkx22 antisenseRNAoverex-pression enhanced oligodendrocytic differentiationrdquo Biochem-ical and Biophysical Research Communications vol 372 no 4pp 691ndash696 2008

[19] J Feng C Bi B S Clark R Mady P Shah and J D KohtzldquoThe Evf-2 noncoding RNA is transcribed from the Dlx-56ultraconserved region and functions as a Dlx-2 transcriptionalcoactivatorrdquo Genes and Development vol 20 no 11 pp 1470ndash1484 2006

[20] W Yu D Gius P Onyango et al ldquoEpigenetic silencing oftumour suppressor gene p15 by its antisense RNArdquo Nature vol451 no 7175 pp 202ndash206 2008

[21] X Wang S Arai X Song et al ldquoInduced ncRNAs allostericallymodify RNA-binding proteins in cis to inhibit transcriptionrdquoNature vol 454 no 7200 pp 126ndash130 2008

[22] I Shamovsky M Ivannikov E S Kandel D Gershon and ENudler ldquoRNA-mediated response to heat shock in mammaliancellsrdquo Nature vol 440 no 7083 pp 556ndash560 2006

[23] A T Willingham A P Orth S Batalov et al ldquoA strategy forprobing the function of noncoding RNAs finds a repressor ofNFATrdquo Science vol 309 no 5740 pp 1570ndash1573 2005

[24] Z Yang Q Zhu K Luo and Q Zhou ldquoThe 7SK smallnuclear RNA inhibits the CDK9cyclin T1 kinase to controltranscriptionrdquo Nature vol 414 no 6861 pp 317ndash322 2001

[25] V T Nguyen T Kiss A A Michels and O Bensaudeldquo7SK small nuclear RNA binds to and inhibits the activity ofCDK9cyclin T complexesrdquo Nature vol 414 no 6861 pp 322ndash325 2001

[26] L Guo Y Zhao S Yang H Zhang and F Chen ldquoIntegrativeanalysis of miRNA-mRNA and miRNA-miRNA interactionsrdquoBioMed Research International vol 2014 Article ID 907420 8pages 2014

[27] A Kozomara and S Griffiths-Jones ldquomiRBase integratingmicroRNA annotation and deep-sequencing datardquo NucleicAcids Research vol 39 no 1 pp D152ndashD157 2011

[28] F Kuchenbauer R D Morin B Argiropoulos et al ldquoIn-depthcharacterization of the microRNA transcriptome in a leukemia


progression modelrdquo Genome Research vol 18 no 11 pp 1787ndash1797 2008

[29] R D Morin M D OrsquoConnor M Griffith et al ldquoApplicationof massively parallel sequencing to microRNA profiling anddiscovery in human embryonic stem cellsrdquo Genome Researchvol 18 no 4 pp 610ndash621 2008

[30] J G Ruby C Jan C Player et al ldquoLarge-scale sequencingreveals 21U-RNAs and additional microRNAs and endogenoussiRNAs in C elegansrdquo Cell vol 127 no 6 pp 1193ndash1207 2006

[31] S L Fernandez-Valverde R J Taft and J S Mattick ldquoDynamicisomiR regulation inDrosophila developmentrdquoRNA vol 16 no10 pp 1881ndash1888 2010

[32] L W Lee S Zhang A Etheridge et al ldquoComplexity of themicroRNA repertoire revealed by next-generation sequencingrdquoRNA vol 16 no 11 pp 2170ndash2180 2010

[33] A M Burroughs Y Ando M J L de Hoon et al ldquoAcomprehensive survey of 31015840 animal miRNAmodification eventsand a possible role for 31015840 adenylation in modulating miRNAtargeting effectivenessrdquo Genome Research vol 20 no 10 pp1398ndash1410 2010

[34] L Guo Q Yang J Lu et al ldquoA comprehensive survey of miRNArepertoire and 31015840 addition events in the placentas of patientswith pre-eclampsia from high-throughput sequencingrdquo PLoSONE vol 6 no 6 Article ID e21072 2011

[35] 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

[36] D Y Chiang P O Brown and M B Eisen ldquoVisualizingassociations between genome sequences and gene expressiondata using genome-mean expression profilesrdquo Bioinformaticsvol 17 no 1 pp S49ndashS55 2001

[37] S-D Hsu F Lin W Wu et al ldquomiRTarBase a databasecurates experimentally validated microRNA-target interac-tionsrdquoNucleic Acids Research vol 39 no 1 pp D163ndashD169 2011

[38] B John A J Enright A Aravin T Tuschl C Sander and D SMarks ldquoHumanmicroRNA targetsrdquo PLoS Biology vol 2 no 11article e363 2004

[39] M E Smoot K Ono J Ruscheinski P Wang and T IdekerldquoCytoscape 28 new features for data integration and networkvisualizationrdquo Bioinformatics vol 27 no 3 pp 431ndash432 2011

[40] L Guo H Li J Lu et al ldquoTrackingmiRNAprecursormetabolicproducts and processing sites through completely analyzinghigh-throughput sequencing datardquo Molecular Biology Reportsvol 39 no 2 pp 2031ndash2038 2012

[41] S Shimizu T Takehara H Hikita et al ldquoThe let-7 fam-ily of microRNAs inhibits Bcl-xL expression and potentiatessorafenib-induced apoptosis in human hepatocellular carci-nomardquo Journal of Hepatology vol 52 no 5 pp 698ndash704 2010

[42] W Hou Q Tian N M Steuerwald L W Schrum and H LBonkovsky ldquoThe let-7 microRNA enhances heme oxygenase-1by suppressing Bach1 and attenuates oxidant injury in humanhepatocytesrdquo Biochimica et Biophysica ActamdashGene RegulatoryMechanisms vol 1819 no 11-12 pp 1113ndash1122 2012

[43] C Braconi T Kogure N Valeri et al ldquomicroRNA-29 canregulate expression of the long non-coding RNA geneMEG3 inhepatocellular cancerrdquoOncogene vol 30 no 47 pp 4750ndash47562011

[44] A Rodriguez S Griffiths-Jones J L Ashurst and A BradleyldquoIdentification of mammalian microRNA host genes and tran-scription unitsrdquo Genome Research vol 14 no 10 A pp 1902ndash1910 2004

[45] V A Erdmann M Szymanski A Hochberg N De Groot andJ Barciszewski ldquoNon-codingmRNA-like RNAs database Y2KrdquoNucleic Acids Research vol 28 no 1 pp 197ndash200 2000

[46] K Augoff B McCue E F Plow and K Sossey-Alaoui ldquoMiR-31 and its host gene lncRNA LOC554202 are regulated bypromoter hypermethylation in triple-negative breast cancerrdquoMolecular Cancer vol 11 article 5 2012

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


to regulate gene expression through various mechanismssuch as complementary binding to protein-coding transcriptsin the form of cis-antisense lncRNAs [16ndash20] They alsomodulate transcription factors by acting as coregulators[19 21ndash25] Dysregulation of ncRNAs contributes to manybiological processes by interfering with gene expression Inrecent years this has become a hot research topic as coreregulatory molecules

Although the many biological roles of ncRNAs havedrawn a great deal of concern systematic and integrativeanalyses of many kinds of RNA molecules (including func-tional mRNAs) have been rare An integrated genome-wideanalysis involving many different RNA molecule levels isnecessary if the complex regulatory network and mecha-nisms underlying tumorigenesis are to be understood Weever performed analyses of miRNA-mRNA and miRNA-miRNA interactions using miRNA and mRNA expressionprofiles [26] but it is not enough to further understandthe potential relationships between different RNAmoleculesespecially involving the novel concerned lncRNAs In thepresent study the close relationships between ncRNAs andmRNAs were examined through simultaneously profilingof miRNA lncRNA and mRNA in HepG2 and L02 cellsusing high-throughput technologies An integrative methodof analysis was developed to detect and comprehensivelyanalyze the relationships between RNAmolecules especiallybetween abnormally expressed miRNA lncRNA andmRNAmolecules in tumor cells A systematic analysis of miRNAswas also performed at the isomiR level The results ofthe present study will enrich the genome-wide analysis ofdifferent molecules with potential cross-talk and contributeto further systematic studies of tumorigenesis

2 Materials and Methods

21 Cell Culture and RNA Isolation HepG2 and L02 cellswere obtained from the American Type Tissue CollectionThese were maintained in DMEM containing 10 FBS100UmL benzylpenicillin and 100UmL streptomycin at37∘C in a humidified 95 air 5 CO

2incubator Total RNAs

were isolated using TRIzol reagent (Invitrogen) according tothe manufacturerrsquos protocol

22 Small RNA Sequencing and Microarray ExperimentsTotal RNA from each sample was used to prepare thesmall RNA sequencing library to perform sequencing ona Genome Analyzer IIx which was used in accordancewith the manufacturerrsquos instructions and was prepared formicroarray hybridization The raw small RNA sequencingdata can be available in the Sequence Read Archive (SRA)database (httpwwwncbinlmnihgovsra accession num-ber SRA 1262121) and the mRNA and lncRNA microarraydata can be available in the European Molecular Biology Laboratory-Euro-pean Bioinformatics Institute (EMBL-EBI)database (httpwwwebiacuk)

23 Data Analysis The total raw miRNA sequencing readswere first filtered using a Solexa CHASTITY quality control

filter The remaining sequencing reads were deleted the 31015840adapter sequences and tags shorter than 15 nt were discardedThen the reads were aligned to the known human miRNAprecursors (pre-miRNAs) in the miRBase database (Release180 httpwwwmirbaseorg) using Novoalign software(v20711 httpwwwnovocraftcom) [27] Only one mis-matchwas allowed Readswith counts under 2were discardedwhenmiRNA expression was calculated According to recentreports on multiple isomiRs from a given miRNA locus [28ndash34] isomiRs (including those isomiRs with 31015840 nontemplateadditional nucleotides) were also comprehensively surveyedSequences that matched the pre-miRNAs in the maturemiRNA regionplusmn4 nt (nomore than 1mismatch) were definedas isomiRs The original sequence counts of miRNAs werenormalized to RPM (reads per million) and miRNA expres-sion analysis was performed based on these normalized dataat the miRNA and isomiR level

Images from microarray were analyzed with AgilentFeature Extraction software (version 10731) Raw signalintensities of mRNAs and lncRNAs were normalized usingthe quantile method and the GeneSpring GX v120 softwarepackage (Agilent Technologies) After quantile normalizationof the raw data lncRNAs and mRNAs for which 2 out of 2samples had flags in the present or marginal were chosen forfurther data analysis

Fold change was calculated to assess expressed miRNAprofiles that were differentially expressed between the twosamples atmiRNA and isomiR levels Differentially expressedlncRNAs and mRNAs were also identified through foldchange filtering To obtain abnormal ncRNAmRNA speciesand filter out rare species with lower expression levels foldchange values were assessed by adding an additional lownumber (10 units) based on normalized datasets Hierarchicalclustering was performed using Cluster bb30 and TreeView160 programs (httpranalblgoveisen) [35 36] Experi-mentally validated target mRNAs of aberrantly expressedmiRNAs were collected from the miRTarBase database [37]For miRNAs with few or no validated target mRNAs theputative target mRNAs were integrated using the predic-tion software programs Pictar TargetScan and miRandaprograms [38] The threshold values were simultaneouslycontrolled (eg in TargetScan the threshold of total con-text score was less minus030) The collected target mRNAswere further screened based on abnormally expressedmRNA profiles Then pathway and GO analysis were usedto determine the roles of these differentially expressedmRNAs Using CapitalBioMolecule Annotation SystemV40(MAS httpbioinfocapitalbiocommas3) further func-tional enrichment analysis was performed Functional inter-action networks were constructed using Cytoscape v282Platform [39]

24 Schema for Integrative Analysis of ncRNA-mRNA DatancRNA-mRNA integrative analysis was performed accordingto Figure 1 The approach included three steps First profilesof aberrantly expressed miRNA mRNA and lncRNA inHepG2 cells were comprehensively surveyed using high-throughput datasets A profile of aberrantly expressed










GOpathwayanalysis

miRNAisomiR level



3 Results





























Glioma 12 149119864 minus 21







0

50

100

1 2 3 4 5 6 7 8 9 100

50

100

1 2 3 4 5 6 7 9 11A U0

50

100

1 4 5 7 8 9 10A U U

0

50

100

1 2 3 5 8 10 11 12 14U U UC U0

50

100

1 2 4 5 6 7 11 12A A C U UA0

50

100

1 2 4 5 7 8 9 11A AA

0

50

100

1 4 5 6A A A0

50

100

1 3 7 9 13 23

AU

A AA

U U

U UG U0

50

100

1 3 5 9 11 13 15 19 21 23 25 27AA

AU A

0

50

100

1 2 3 4 7 9 10 11 12 13 16A AC UU

0

50

100

1 2 3 4 5 8A U0

100

200

1 2 3 4

0

50

100

1 4 7 10 16 19 22 25 28 31 40

AA

A

U

U U UA

G

UA G

0

50

100

1 3 9 11 13 15 17 21A A AUA U

0

50

100

1 2 3 4 5 8 9 10 11UA A

0

50

100

1 2 3 4 5 6 U0

50

1 3 5 7 9 11 13 15 17 19 21 27 29 31 33 35 41 43 49 51 53 55 57 59

100

C U

G

A G

U A

G

C

A





miR-27a-3p (down)



(down)







0

200

40

80

120

160

chr1

chr9

chr8

chr7

chr6

chr5

chr4

chr3

chr2

chr22

chr21

chr20

chr19

chr18

chr17

chr16

chr15

chr14

chr13

chr12

chr11

chr10

chrY

chrX

chrM

(b)

chr1

chr9

chr7

chr5

chr3

chr21

chr19

chr17

chr15

chr13

chr11

chrX

chr1

chr9

chr7

chr5

chr3

chr21

chr19

chr17

chr15

chr13

chr11

chrX

chrM

mRNA

0

2

4

6

8


(c)

mRNA

024681012

(d)

(a)

chr1

chr8chr7chr6

chr5chr4chr3chr2


chr9

chrYchrX


0 15050 100


mRN

A



chr1

chr9

chr8

chr7

chr6

chr5

chr4

chr3

chr2

chr22

chr21

chr20

chr19

chr18

chr17

chr16

chr15

chr14

chr13

chr12

chr11

chr10

chrY

chrX

chrM

Perc

enta

ge (

)Pe

rcen

tage

()

lncRNA

lncRNA

lncRNA








Pathways in cancer


Endocytosis

Tuberculosis

Viral myocarditis

Focal adhesion

Axon guidance

Melanoma

Endometrial cancer

Significant pathway

0 1 2 3 4 5 6


(a) Down

Ribosome

Spliceosome

Proteasome




RNA degradation


Significant pathway

0 2 4 6 8




(b) Up








00 05 10 15 20 25 30

Fold enrichment

differentiation

immune response




(c) Down


00 05 10 15 20 25 30 35

Fold enrichment













(d) Up





4 Discussion



APP BCL2 CCND2

MYCE2F2

CCNA2

EP300PTPN12

BRCA1 CCNE1

DICER1MMP13 PRDM1

100-5p27b-3p let-7f-5p 24-3p

let-7a-5p

24-3p

15a-5p

194-5p200b-3p

103a-3p146a-5p

BX640708

BX640708

DLEU2

AP0014392

AL0211543

NF120581B1







5998400

Chr4

5998400

Chr11

5998400 lncRNA AC1299295 (minus102)

Position 74 269 971-74 287 127

Position 74 270 077-74 286 973

Position 2 398 546-2 418 649

Position 2 349 978-2 399 219

(a)

Chr175998400 lncRNA MGC12916 (733)




5998400

Chr2

Position (lncRNA) 14 207056-14 209 061

Position (mRNA) 14 204505-14 249 492

(b)

0200400600800

1000


(c)

0

200

400

600

800


(d)

0100200300400500600

Up-up

mRNA-lncRNA

Down-down

Down-up

Up-down

(e)

05

10152025



(f)

0

5

10

15

20



(g)

0

5

10

15

miRNA-lncRNA

Up-up Down-down

Down-up

Up-down


(h)











Abbreviations







Acknowledgments


References
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology










GOpathwayanalysis

miRNAisomiR level



3 Results





























Glioma 12 149119864 minus 21







0

50

100

1 2 3 4 5 6 7 8 9 100

50

100

1 2 3 4 5 6 7 9 11A U0

50

100

1 4 5 7 8 9 10A U U

0

50

100

1 2 3 5 8 10 11 12 14U U UC U0

50

100

1 2 4 5 6 7 11 12A A C U UA0

50

100

1 2 4 5 7 8 9 11A AA

0

50

100

1 4 5 6A A A0

50

100

1 3 7 9 13 23

AU

A AA

U U

U UG U0

50

100

1 3 5 9 11 13 15 19 21 23 25 27AA

AU A

0

50

100

1 2 3 4 7 9 10 11 12 13 16A AC UU

0

50

100

1 2 3 4 5 8A U0

100

200

1 2 3 4

0

50

100

1 4 7 10 16 19 22 25 28 31 40

AA

A

U

U U UA

G

UA G

0

50

100

1 3 9 11 13 15 17 21A A AUA U

0

50

100

1 2 3 4 5 8 9 10 11UA A

0

50

100

1 2 3 4 5 6 U0

50

1 3 5 7 9 11 13 15 17 19 21 27 29 31 33 35 41 43 49 51 53 55 57 59

100

C U

G

A G

U A

G

C

A





miR-27a-3p (down)



(down)







0

200

40

80

120

160

chr1

chr9

chr8

chr7

chr6

chr5

chr4

chr3

chr2

chr22

chr21

chr20

chr19

chr18

chr17

chr16

chr15

chr14

chr13

chr12

chr11

chr10

chrY

chrX

chrM

(b)

chr1

chr9

chr7

chr5

chr3

chr21

chr19

chr17

chr15

chr13

chr11

chrX

chr1

chr9

chr7

chr5

chr3

chr21

chr19

chr17

chr15

chr13

chr11

chrX

chrM

mRNA

0

2

4

6

8


(c)

mRNA

024681012

(d)

(a)

chr1

chr8chr7chr6

chr5chr4chr3chr2


chr9

chrYchrX


0 15050 100


mRN

A



chr1

chr9

chr8

chr7

chr6

chr5

chr4

chr3

chr2

chr22

chr21

chr20

chr19

chr18

chr17

chr16

chr15

chr14

chr13

chr12

chr11

chr10

chrY

chrX

chrM

Perc

enta

ge (

)Pe

rcen

tage

()

lncRNA

lncRNA

lncRNA








Pathways in cancer


Endocytosis

Tuberculosis

Viral myocarditis

Focal adhesion

Axon guidance

Melanoma

Endometrial cancer

Significant pathway

0 1 2 3 4 5 6


(a) Down

Ribosome

Spliceosome

Proteasome




RNA degradation


Significant pathway

0 2 4 6 8




(b) Up








00 05 10 15 20 25 30

Fold enrichment

differentiation

immune response




(c) Down


00 05 10 15 20 25 30 35

Fold enrichment













(d) Up





4 Discussion



APP BCL2 CCND2

MYCE2F2

CCNA2

EP300PTPN12

BRCA1 CCNE1

DICER1MMP13 PRDM1

100-5p27b-3p let-7f-5p 24-3p

let-7a-5p

24-3p

15a-5p

194-5p200b-3p

103a-3p146a-5p

BX640708

BX640708

DLEU2

AP0014392

AL0211543

NF120581B1







5998400

Chr4

5998400

Chr11

5998400 lncRNA AC1299295 (minus102)

Position 74 269 971-74 287 127

Position 74 270 077-74 286 973

Position 2 398 546-2 418 649

Position 2 349 978-2 399 219

(a)

Chr175998400 lncRNA MGC12916 (733)




5998400

Chr2

Position (lncRNA) 14 207056-14 209 061

Position (mRNA) 14 204505-14 249 492

(b)

0200400600800

1000


(c)

0

200

400

600

800


(d)

0100200300400500600

Up-up

mRNA-lncRNA

Down-down

Down-up

Up-down

(e)

05

10152025



(f)

0

5

10

15

20



(g)

0

5

10

15

miRNA-lncRNA

Up-up Down-down

Down-up

Up-down


(h)











Abbreviations







Acknowledgments


References
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology
























Glioma 12 149119864 minus 21







0

50

100

1 2 3 4 5 6 7 8 9 100

50

100

1 2 3 4 5 6 7 9 11A U0

50

100

1 4 5 7 8 9 10A U U

0

50

100

1 2 3 5 8 10 11 12 14U U UC U0

50

100

1 2 4 5 6 7 11 12A A C U UA0

50

100

1 2 4 5 7 8 9 11A AA

0

50

100

1 4 5 6A A A0

50

100

1 3 7 9 13 23

AU

A AA

U U

U UG U0

50

100

1 3 5 9 11 13 15 19 21 23 25 27AA

AU A

0

50

100

1 2 3 4 7 9 10 11 12 13 16A AC UU

0

50

100

1 2 3 4 5 8A U0

100

200

1 2 3 4

0

50

100

1 4 7 10 16 19 22 25 28 31 40

AA

A

U

U U UA

G

UA G

0

50

100

1 3 9 11 13 15 17 21A A AUA U

0

50

100

1 2 3 4 5 8 9 10 11UA A

0

50

100

1 2 3 4 5 6 U0

50

1 3 5 7 9 11 13 15 17 19 21 27 29 31 33 35 41 43 49 51 53 55 57 59

100

C U

G

A G

U A

G

C

A





miR-27a-3p (down)



(down)







0

200

40

80

120

160

chr1

chr9

chr8

chr7

chr6

chr5

chr4

chr3

chr2

chr22

chr21

chr20

chr19

chr18

chr17

chr16

chr15

chr14

chr13

chr12

chr11

chr10

chrY

chrX

chrM

(b)

chr1

chr9

chr7

chr5

chr3

chr21

chr19

chr17

chr15

chr13

chr11

chrX

chr1

chr9

chr7

chr5

chr3

chr21

chr19

chr17

chr15

chr13

chr11

chrX

chrM

mRNA

0

2

4

6

8


(c)

mRNA

024681012

(d)

(a)

chr1

chr8chr7chr6

chr5chr4chr3chr2


chr9

chrYchrX


0 15050 100


mRN

A



chr1

chr9

chr8

chr7

chr6

chr5

chr4

chr3

chr2

chr22

chr21

chr20

chr19

chr18

chr17

chr16

chr15

chr14

chr13

chr12

chr11

chr10

chrY

chrX

chrM

Perc

enta

ge (

)Pe

rcen

tage

()

lncRNA

lncRNA

lncRNA








Pathways in cancer


Endocytosis

Tuberculosis

Viral myocarditis

Focal adhesion

Axon guidance

Melanoma

Endometrial cancer

Significant pathway

0 1 2 3 4 5 6


(a) Down

Ribosome

Spliceosome

Proteasome




RNA degradation


Significant pathway

0 2 4 6 8




(b) Up








00 05 10 15 20 25 30

Fold enrichment

differentiation

immune response




(c) Down


00 05 10 15 20 25 30 35

Fold enrichment













(d) Up





4 Discussion



APP BCL2 CCND2

MYCE2F2

CCNA2

EP300PTPN12

BRCA1 CCNE1

DICER1MMP13 PRDM1

100-5p27b-3p let-7f-5p 24-3p

let-7a-5p

24-3p

15a-5p

194-5p200b-3p

103a-3p146a-5p

BX640708

BX640708

DLEU2

AP0014392

AL0211543

NF120581B1







5998400

Chr4

5998400

Chr11

5998400 lncRNA AC1299295 (minus102)

Position 74 269 971-74 287 127

Position 74 270 077-74 286 973

Position 2 398 546-2 418 649

Position 2 349 978-2 399 219

(a)

Chr175998400 lncRNA MGC12916 (733)




5998400

Chr2

Position (lncRNA) 14 207056-14 209 061

Position (mRNA) 14 204505-14 249 492

(b)

0200400600800

1000


(c)

0

200

400

600

800


(d)

0100200300400500600

Up-up

mRNA-lncRNA

Down-down

Down-up

Up-down

(e)

05

10152025



(f)

0

5

10

15

20



(g)

0

5

10

15

miRNA-lncRNA

Up-up Down-down

Down-up

Up-down


(h)











Abbreviations







Acknowledgments


References
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


0

50

100

1 2 3 4 5 6 7 8 9 100

50

100

1 2 3 4 5 6 7 9 11A U0

50

100

1 4 5 7 8 9 10A U U

0

50

100

1 2 3 5 8 10 11 12 14U U UC U0

50

100

1 2 4 5 6 7 11 12A A C U UA0

50

100

1 2 4 5 7 8 9 11A AA

0

50

100

1 4 5 6A A A0

50

100

1 3 7 9 13 23

AU

A AA

U U

U UG U0

50

100

1 3 5 9 11 13 15 19 21 23 25 27AA

AU A

0

50

100

1 2 3 4 7 9 10 11 12 13 16A AC UU

0

50

100

1 2 3 4 5 8A U0

100

200

1 2 3 4

0

50

100

1 4 7 10 16 19 22 25 28 31 40

AA

A

U

U U UA

G

UA G

0

50

100

1 3 9 11 13 15 17 21A A AUA U

0

50

100

1 2 3 4 5 8 9 10 11UA A

0

50

100

1 2 3 4 5 6 U0

50

1 3 5 7 9 11 13 15 17 19 21 27 29 31 33 35 41 43 49 51 53 55 57 59

100

C U

G

A G

U A

G

C

A





miR-27a-3p (down)



(down)







0

200

40

80

120

160

chr1

chr9

chr8

chr7

chr6

chr5

chr4

chr3

chr2

chr22

chr21

chr20

chr19

chr18

chr17

chr16

chr15

chr14

chr13

chr12

chr11

chr10

chrY

chrX

chrM

(b)

chr1

chr9

chr7

chr5

chr3

chr21

chr19

chr17

chr15

chr13

chr11

chrX

chr1

chr9

chr7

chr5

chr3

chr21

chr19

chr17

chr15

chr13

chr11

chrX

chrM

mRNA

0

2

4

6

8


(c)

mRNA

024681012

(d)

(a)

chr1

chr8chr7chr6

chr5chr4chr3chr2


chr9

chrYchrX


0 15050 100


mRN

A



chr1

chr9

chr8

chr7

chr6

chr5

chr4

chr3

chr2

chr22

chr21

chr20

chr19

chr18

chr17

chr16

chr15

chr14

chr13

chr12

chr11

chr10

chrY

chrX

chrM

Perc

enta

ge (

)Pe

rcen

tage

()

lncRNA

lncRNA

lncRNA








Pathways in cancer


Endocytosis

Tuberculosis

Viral myocarditis

Focal adhesion

Axon guidance

Melanoma

Endometrial cancer

Significant pathway

0 1 2 3 4 5 6


(a) Down

Ribosome

Spliceosome

Proteasome




RNA degradation


Significant pathway

0 2 4 6 8




(b) Up








00 05 10 15 20 25 30

Fold enrichment

differentiation

immune response




(c) Down


00 05 10 15 20 25 30 35

Fold enrichment













(d) Up





4 Discussion



APP BCL2 CCND2

MYCE2F2

CCNA2

EP300PTPN12

BRCA1 CCNE1

DICER1MMP13 PRDM1

100-5p27b-3p let-7f-5p 24-3p

let-7a-5p

24-3p

15a-5p

194-5p200b-3p

103a-3p146a-5p

BX640708

BX640708

DLEU2

AP0014392

AL0211543

NF120581B1







5998400

Chr4

5998400

Chr11

5998400 lncRNA AC1299295 (minus102)

Position 74 269 971-74 287 127

Position 74 270 077-74 286 973

Position 2 398 546-2 418 649

Position 2 349 978-2 399 219

(a)

Chr175998400 lncRNA MGC12916 (733)




5998400

Chr2

Position (lncRNA) 14 207056-14 209 061

Position (mRNA) 14 204505-14 249 492

(b)

0200400600800

1000


(c)

0

200

400

600

800


(d)

0100200300400500600

Up-up

mRNA-lncRNA

Down-down

Down-up

Up-down

(e)

05

10152025



(f)

0

5

10

15

20



(g)

0

5

10

15

miRNA-lncRNA

Up-up Down-down

Down-up

Up-down


(h)











Abbreviations







Acknowledgments


References
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


0

200

40

80

120

160

chr1

chr9

chr8

chr7

chr6

chr5

chr4

chr3

chr2

chr22

chr21

chr20

chr19

chr18

chr17

chr16

chr15

chr14

chr13

chr12

chr11

chr10

chrY

chrX

chrM

(b)

chr1

chr9

chr7

chr5

chr3

chr21

chr19

chr17

chr15

chr13

chr11

chrX

chr1

chr9

chr7

chr5

chr3

chr21

chr19

chr17

chr15

chr13

chr11

chrX

chrM

mRNA

0

2

4

6

8


(c)

mRNA

024681012

(d)

(a)

chr1

chr8chr7chr6

chr5chr4chr3chr2


chr9

chrYchrX


0 15050 100


mRN

A



chr1

chr9

chr8

chr7

chr6

chr5

chr4

chr3

chr2

chr22

chr21

chr20

chr19

chr18

chr17

chr16

chr15

chr14

chr13

chr12

chr11

chr10

chrY

chrX

chrM

Perc

enta

ge (

)Pe

rcen

tage

()

lncRNA

lncRNA

lncRNA








Pathways in cancer


Endocytosis

Tuberculosis

Viral myocarditis

Focal adhesion

Axon guidance

Melanoma

Endometrial cancer

Significant pathway

0 1 2 3 4 5 6


(a) Down

Ribosome

Spliceosome

Proteasome




RNA degradation


Significant pathway

0 2 4 6 8




(b) Up








00 05 10 15 20 25 30

Fold enrichment

differentiation

immune response




(c) Down


00 05 10 15 20 25 30 35

Fold enrichment













(d) Up





4 Discussion



APP BCL2 CCND2

MYCE2F2

CCNA2

EP300PTPN12

BRCA1 CCNE1

DICER1MMP13 PRDM1

100-5p27b-3p let-7f-5p 24-3p

let-7a-5p

24-3p

15a-5p

194-5p200b-3p

103a-3p146a-5p

BX640708

BX640708

DLEU2

AP0014392

AL0211543

NF120581B1







5998400

Chr4

5998400

Chr11

5998400 lncRNA AC1299295 (minus102)

Position 74 269 971-74 287 127

Position 74 270 077-74 286 973

Position 2 398 546-2 418 649

Position 2 349 978-2 399 219

(a)

Chr175998400 lncRNA MGC12916 (733)




5998400

Chr2

Position (lncRNA) 14 207056-14 209 061

Position (mRNA) 14 204505-14 249 492

(b)

0200400600800

1000


(c)

0

200

400

600

800


(d)

0100200300400500600

Up-up

mRNA-lncRNA

Down-down

Down-up

Up-down

(e)

05

10152025



(f)

0

5

10

15

20



(g)

0

5

10

15

miRNA-lncRNA

Up-up Down-down

Down-up

Up-down


(h)











Abbreviations







Acknowledgments


References
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



Pathways in cancer


Endocytosis

Tuberculosis

Viral myocarditis

Focal adhesion

Axon guidance

Melanoma

Endometrial cancer

Significant pathway

0 1 2 3 4 5 6


(a) Down

Ribosome

Spliceosome

Proteasome




RNA degradation


Significant pathway

0 2 4 6 8




(b) Up








00 05 10 15 20 25 30

Fold enrichment

differentiation

immune response




(c) Down


00 05 10 15 20 25 30 35

Fold enrichment













(d) Up





4 Discussion



APP BCL2 CCND2

MYCE2F2

CCNA2

EP300PTPN12

BRCA1 CCNE1

DICER1MMP13 PRDM1

100-5p27b-3p let-7f-5p 24-3p

let-7a-5p

24-3p

15a-5p

194-5p200b-3p

103a-3p146a-5p

BX640708

BX640708

DLEU2

AP0014392

AL0211543

NF120581B1







5998400

Chr4

5998400

Chr11

5998400 lncRNA AC1299295 (minus102)

Position 74 269 971-74 287 127

Position 74 270 077-74 286 973

Position 2 398 546-2 418 649

Position 2 349 978-2 399 219

(a)

Chr175998400 lncRNA MGC12916 (733)




5998400

Chr2

Position (lncRNA) 14 207056-14 209 061

Position (mRNA) 14 204505-14 249 492

(b)

0200400600800

1000


(c)

0

200

400

600

800


(d)

0100200300400500600

Up-up

mRNA-lncRNA

Down-down

Down-up

Up-down

(e)

05

10152025



(f)

0

5

10

15

20



(g)

0

5

10

15

miRNA-lncRNA

Up-up Down-down

Down-up

Up-down


(h)











Abbreviations







Acknowledgments


References
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


APP BCL2 CCND2

MYCE2F2

CCNA2

EP300PTPN12

BRCA1 CCNE1

DICER1MMP13 PRDM1

100-5p27b-3p let-7f-5p 24-3p

let-7a-5p

24-3p

15a-5p

194-5p200b-3p

103a-3p146a-5p

BX640708

BX640708

DLEU2

AP0014392

AL0211543

NF120581B1







5998400

Chr4

5998400

Chr11

5998400 lncRNA AC1299295 (minus102)

Position 74 269 971-74 287 127

Position 74 270 077-74 286 973

Position 2 398 546-2 418 649

Position 2 349 978-2 399 219

(a)

Chr175998400 lncRNA MGC12916 (733)




5998400

Chr2

Position (lncRNA) 14 207056-14 209 061

Position (mRNA) 14 204505-14 249 492

(b)

0200400600800

1000


(c)

0

200

400

600

800


(d)

0100200300400500600

Up-up

mRNA-lncRNA

Down-down

Down-up

Up-down

(e)

05

10152025



(f)

0

5

10

15

20



(g)

0

5

10

15

miRNA-lncRNA

Up-up Down-down

Down-up

Up-down


(h)











Abbreviations







Acknowledgments


References
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




5998400

Chr4

5998400

Chr11

5998400 lncRNA AC1299295 (minus102)

Position 74 269 971-74 287 127

Position 74 270 077-74 286 973

Position 2 398 546-2 418 649

Position 2 349 978-2 399 219

(a)

Chr175998400 lncRNA MGC12916 (733)




5998400

Chr2

Position (lncRNA) 14 207056-14 209 061

Position (mRNA) 14 204505-14 249 492

(b)

0200400600800

1000


(c)

0

200

400

600

800


(d)

0100200300400500600

Up-up

mRNA-lncRNA

Down-down

Down-up

Up-down

(e)

05

10152025



(f)

0

5

10

15

20



(g)

0

5

10

15

miRNA-lncRNA

Up-up Down-down

Down-up

Up-down


(h)











Abbreviations







Acknowledgments


References
























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology







Abbreviations







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

an integrated analysis of mirna, lncrna, and mrna expression profiles

Documents