identifying dysregulated lncrna-associated cerna network

Research ArticleIdentifying Dysregulated lncRNA-Associated ceRNA NetworkBiomarkers in CML Based on Dynamical Network Biomarkers

Junhua Xu Min Wu Yichen Sun Hongqian Zhao Yujie Wang and Jie Gao

School of Science Jiangnan University Wuxi 214122 China

Correspondence should be addressed to Jie Gao gaojiejiangnaneducn

Received 15 October 2019 Accepted 4 January 2020 Published 18 February 2020

Academic Editor Przemko Tylzanowski

Copyright copy 2020 Junhua Xu et al -is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

-e incidence of chronic myeloid leukemia (CML) is increasing year by year which is a serious threat to human health Earlydiagnosis can reduce mortality and improve prognosis LncRNAs have been shown to be effective biomarkers for a variety ofdiseases and can act as competitive endogenous RNA (ceRNA) In this study the dysregulated lncRNA-associated ceRNAnetworks (DLCN) of the chronic phase (CP) accelerated phase (AP) and blastic crisis (BC) for CML are constructed-en basedon dynamic network biomarkers (DNB) some dysregulated lncRNA-associated ceRNA network biomarkers of CP AP and BCfor CML are identified according to DNB criteria -us a lncRNA (SNHG5) is identified from DLCN_CP a lncRNA (DLEU2) isidentified from DLCN_AP and two lncRNAs (SNHG3 SNHG5) are identified from DLCN_BC In addition the critical index(CI) used to detect disease outbreaks shows that CML occurs in CP which is consistent with clinical medicine By analyzing thefunctions of the identified ceRNA network biomarkers it has been found that they are effective lncRNA biomarkers directly orindirectly related to CML -e result of this study will help deepen the understanding of CML pathology from the perspective ofceRNA and help discover the effective biomarkers of CP AP and BC for CML in the future so as to help patients get timelytreatment and reduce the mortality of CML

1 Introduction

Chronic myeloid leukemia (CML) is a clonal malignantproliferative disease of hematopoietic stem cells (HSCs) -emain hallmark is the formation of BCR-ABL fusion gene atthe molecular level [1] BCR-ABL is a chimeric oncogenearising from t (9 22) (q34 q11) chromosomal translocation-e resultant protein-tyrosine kinase (PTK) drives signalingevents and transforms HSCs BCR-ABL activity in HSCcauses CML [2] Generally CML is divided into threephases Initially CML presents in the chronic phase (CP)and it will invariably transform through the accelerationphase (AP) without curative intervention progression thenproceeds to blast crisis (BC) BC is highly resistant totreatment Patients generally die of infection and bleedingcomplications due to a lack of normal granulocytes andplatelets [2] Although the BCR-ABL fusion gene has beendetermined as a pathogenic gene of CML it is only used forthe diagnosis of CML and does not reflect the molecularmechanism and three stages of CML

Noncoding RNAs constitute 80ndash90 of the humantranscriptome -ey are involved in numerous gene regu-lation processes in cells that lead to complex diseases es-pecially cancer [3] Studies have shown that coding andnoncoding RNAs can regulate expression by targeting theircommon miRNAs [4] -is mechanism is called competitiveendogenous RNA (ceRNA) [5] In detail multiple RNAtranscripts containing miRNA binding sites can compete forthe same miRNA to achieve mutual regulation therebyaffecting gene expression in cells

Recent studies have shown that lncRNA is related toCML closely Xiao et al demonstrated that UCA1 functionsas a ceRNA of MDR1 through completely binding to thecommon miR-16 UCA1-MDR1 might be a novel target forenhancing the therapeutic efficacy of CML patients with IMresistance [6] He et al found that SNHG5 promotedimatinib resistance in CML via acting as a ceRNA againstmiR-205-5p [7] Sen et al constructed a ceRNA network forthe CML cell line and the top ranked significant functionalmodules in the ceRNA network displayed cancer associated

HindawiBioMed Research InternationalVolume 2020 Article ID 5189549 8 pageshttpsdoiorg10115520205189549

attributes and revealed putative regulators in CML patho-genesis [8]

In this study the dysregulated lncRNA-associatedceRNA networks (DLCN) of CP AP and BC for CML areconstructed by integrating regulatory interactions amonglncRNAs miRNAs and mRNAs and expression profile dataNext lncRNA-associated ceRNA network biomarkers in thethree DLCNs based on dynamic network biomarkers (DNB)proposed by Chen et al [9] are identified -ree potentiallncRNAs (SNHG5 SNHG3 DLEU2) are uncovered asfunctional ceRNAs with key roles in the pathogenesis ofCML In addition the critical index (CI) constructed todetect disease outbreaks shows that CML occurs in CPwhich is consistent with clinical medicine -en functionalenrichment analysis is performed on the identified ceRNAnetwork biomarkers and the role of the 3 lncRNAs in CMLis validated by KEGG enrichment analysis and literaturemining -ese novel lncRNAs acting as ceRNAs at theposttranscriptional level may become promising diagnosticbiomarkers and therapeutic targets

2 Materials and Methods

21 Microarray Data -e gene expression profile ofGSE47927 based on Affymetrix Human Gene 10 ST Array isdownloaded from the National Center for BiotechnologyInformationrsquos Gene Expression Omnibus (GEO) database(httpwwwncbinlmnihgovgeo) CML is a clonal ma-lignant proliferative disease of HSCs BCR-ABL activity inHSC causes CML 3 normal samples 6 CP patient samples 4AP patient samples and 2 BC patient samples of HSCs arechosen

-e probe set in the HuGene-1_0-st array is reannotatedto obtain the corresponding lncRNA expression profile dataFirst the probe set of the Affymetrix HuGene-1_0-st array isremapped to the human genome (GRCh38) using SeqMapIf the probe set maps to the human genome uniquelywithout a mismatch the probe set is retained Second thechromosomal location of the above probe set matchesthe chromosomal location of the lncRNA from theGENCODE project [5] Finally 848 lncRNA expressionprofiles are obtained For the mRNA expression profile ifmultiple probes are mapped to the same gene the averageis taken and finally 18288 mRNA expression profiles areobtained

22 Gene Expression Profile Analysis -e significantlydifferentially expressed (SDE) genes are detected by t-testand multiple test corrections Set the p value of 005 andthe fold change of 15 to obtain SDE mRNAs and SDElncRNAs between CP AP and BC in CML and normalsamples

By integrating the experimentally validated miRNA-mRNA interactions downloaded from TarBase v80 data-base and starBase v20 databases 769832 nonredundantmiRNA-mRNA interactions are obtained -e experimen-tally validated miRNA-lncRNA interactions are downloaded

from starBase v20 database and 10213 miRNA-lncRNArelationships are obtained

In order to identify dysregulated lncRNA-mRNAcompetitive interaction in CP AP and BC for CML Basedon the ldquoceRNA hypothesisrdquo a hypergeometric test is used toscreen lncRNA-mRNA competition pairs with p valuelt005 which are called candidate lncRNA-mRNA com-petitive interactions and 12071 candidate lncRNA-mRNAcompetitive interactions are obtained as follows

p 1113944

min NmNln c( )

iNc

Nm

i1113888 1113889

NTotal minus Nm

Nln c minus i1113888 1113889

NTotal

Nln c

1113888 1113889

(1)

where NTotal is the number of common miRNAs betweenall human miRNAs targeted human mRNAs and all humanmiRNAs regulated all human lncRNAs Nm is the numberof miRNAs targeted a given mRNA Nln c is the number ofmiRNAs which regulated a given lncRNA Nc is thenumber of common miRNAs shared by mRNA andlncRNA

Next the Pearson correlation coefficient (PCC) isused to identify mRNA-lncRNA competitive interac-tions -e PCC between mRNA and lncRNA is correlatedpositively defining the mRNA-lncRNA competitive in-teraction In order to improve the reliability of the resultslncRNA-mRNA pairs with PCC gt05 are selected asfollows

PCC(x y) 1113936

ni1 xi minus x( 1113857 yi minus y( 1113857

1113936ni1 xi minus x( 1113857

21113936

ni1 yi minus y( 1113857

21113969 (2)

where xi and yi represent the expressions of gene x and geney of the i-th sample in the reference sample respectively x

and y represent the average gene expression of gene x andgene y in the reference sample respectively

23 Identifying lncRNA Biomarkers Based on DNBGenerally each person or sample has multiple modules Tofurther identify lncRNA biomarkers DNB is used todetect potential modules Dysregulated mRNA-lncRNAcompetitive interactions obtained previously are clus-tered by hierarchical cluster analysis According to thecriteria for identification of DNB proposed by Chen et al[9] the optimal group of genes or proteins is selected asDNB -us a CI can be constructed to evaluate the DNBmodule and detect the critical state in multiple samples asfollows

CIt SDt times PCCt

OPCCt

(3)

where PCCt is the average PCC of the DNB group at time t inabsolute value OPCCt is the average PCC between the DNBgroup and the outside of the DNB group at time t in absolutevalue SDt is the standard deviation of the DNB group attime t

2 BioMed Research International

3 Results and Discussion

31 Differentially Expressed mRNAs and lncRNAs In thisstudy the dataset is divided into normal samples and CMLpatient samples in CP AP and BC and the SDE genes areselected by t-test 1920 SDE mRNAs in CP 1786 SDEmRNAs in AP and 2682 SDE mRNAs in BC are identifiedrespectively 41 SDE lncRNAs in CP 31 SDE lncRNAs in AP

and 80 SDE lncRNAs in BC are identified respectivelyFinally in order to consider fully 4399 SDEmRNAs and 107SDE lncRNAs are obtained from the union set

By integrating the regulatory relationship betweenmRNA and lncRNA in SDE genes DLCN_CP DLCN_APand DLCN_BC are constructed based on ceRNA theory-edysregulated mRNA-lncRNA pairs of CP AP and BC forCML are identified as described in Materials and Methods

Table 1 -e dysregulated mRNA-lncRNA pairs of CP AP and BC for CML

DLCN_CP DLCN_AP DLCN_BCmRNA-lncRNAinteraction 2436 3411 5891

Node_lncRNA 14 14 14Node_mRNA 1510 1892 2598

ITM2B

SGMS1

SNHG5

ACVR1C

ECHDC2

KIAA1598

IncRNAmRNA

(a)

(c)

000

001

002

003

004

005

hsa00600 sphingolipid metabolismhsa00601 glycosphingolipid biosynthesis-lacto and neolacto series

hsa01524 platinum drug resistancehsa04114 oocyte meiosis

hsa04115 p53 signaling pathwayhsa04150 mTOR signaling pathway

hsa04310 wnt signaling pathwayhsa04360 axon guidance

hsa04390 hippo signaling pathwayhsa04392 hippo signaling pathway-multiple species

hsa04550 signaling pathways regulating pluripotency of stem cellshsa04720 long-term potentiation

hsa04722 neurotrophin signaling pathwayhsa04916 melanogenesis

hsa04971 gastric acid secretionhsa04978 mineral absorption

hsa05031 amphetamine addictionhsa05166 HTLV-I infection

hsa05202 transcriptional misregulation in cancerhsa05205 proteoglycans in cancer

hsa05206 microRNAs in cancerhsa05214 glioma

hsa05217 basal cell carcinoma

minuslg (P value)

Pathway enrichment

Gene number100125150175200

minuslg (P value)30252015

(d)

0 200 400 6000

200

400

600

800

1000

Node degree

Num

ber o

f nod

es

R2 = 06738

(b)

Figure 1 Network distribution and functional analysis of the DLCN_CP (a) Global view of the DLCN_CP for CML -e DLCN_CPconsists of 2436 edges between 1510 mRNAs and 14 lncRNAs LncRNAs are circled in circles (b) Degree distribution of nodes in theDLCN_CP (c) DNB of the DLCN_CP (d) Pathway enrichment analysis of DNB in the DLCN_CP

BioMed Research International 3

-e result is shown in Table 1 (see Supplementary Table 1)To further investigate the dysregulated mRNA-lncRNApairs the same mRNA and lncRNA are taken from threestages and 14 lncRNAs and 858mRNAs are obtained finally-at is 1548 1864 and 2386 mRNA-lncRNA pairs in CPAP and BC compete for interaction

In order to study the basic topological characteristics ofbiological networks degree and degree distribution of thethree networks are calculated as shown in Figures 1(a) 2(a)3(a) and Supplementary Table 2 In DLCN_CP the powerlaw distribution of R2 06738 is observed (Figure 1(b)) InDLCN_AP the power law distribution of R2 07381 isobserved (Figure 2(b)) In DLCN_BC the power law dis-tribution of R2 08878 is observed (Figure 3(b)) indicatingthat only a few nodes of the three networks are connected toother nodes highly and the correlation between nodes isrelatively high -ese nodes show typical scale-free featuresof biological networks called hub genes

32 Identifying Dysregulated lncRNA-Associated ceRNANetworkBiomarkers Based onDNB inDLCN_CPDLCN_APand DLCN_BC To further investigate the competition be-tween mRNA and lncRNA in CP AP and BC of CML thedysregulated lncRNA-associated ceRNA network bio-markers are identified (see Table 2) based on DNB Firsthierarchical clustering is performed on DLCN_CPDLCN_AP and DLCN_BC respectively To make theclassification of each period more obvious the relativedistance of each category at each period is set to 07 -ethree periods are divided into 9 categories 8 categories and8 categories respectively After analyzing all categories byDNB a group of 28 genes is identified as DNB in CP whichinclude 27 mRNAs and 1 lncRNA A group of 62 genes isidentified as DNB in AP which include 61 mRNAs and 1lncRNA A group of 71 genes is identified as DNB in BCwhich include 69 mRNAs and 2 lncRNAs -e dysregulatedlncRNA-associated ceRNA network biomarkers in

DLEU2

BCOR

MMD

SLC35F1

IncRNAmRNA

(a)

(c)

hsa00051 fructose and mannose metabolism

hsa03013 RNA transport

hsa03015 mRNA surveillance pathway

hsa03060 protein export

hsa04024 cAMP signaling pathway

hsa04115 p53 signaling pathway

hsa04150 mTOR signaling pathway

hsa04211 longevity regulating pathway

hsa05164 influenza A

hsa05200 pathways in cancer


Pathway enrichment

000

001

002

003

004

005

minuslg (P value)

Gene number

4321

35

30

25

20

15

minuslg (P value)

(d)

0 200 400 6000

200

400

600

800

1000

1200

Node degree

Num

ber o

f nod

es

R2 = 07381

(b)

Figure 2 Network distribution and functional analysis of the DLCN_AP (a) Global view of the DLCN_AP for CML -e DLCN_APconsists of 2436 edges between 1510 mRNAs and 14 lncRNAs LncRNAs are circled in circles (b) Degree distribution of nodes in theDLCN_AP (c) DNB of the DLCN_AP (d) Pathway enrichment analysis of DNB in the DLCN_AP


DLCN_CP are shown in Figure 1(c) and SupplementaryTable 3 and lncRNA SNHG5 competes with five mRNAs-e dysregulated lncRNA-associated ceRNA network bio-markers in DLCN_AP are shown in Figure 2(c) and Sup-plementary Table 3 and lncRNA DLEU2 competes withthree mRNAs -e dysregulated lncRNA-associated ceRNAnetwork biomarkers in DLCN_BC are shown in Figure 3(c)and Supplementary Table 3 and two lncRNAs (SNHG3 andSNHG5) compete with 15 mRNAs Figure 4 shows thechanges of CI in detail In the process of CML the CI value

of CP is the largest indicating that CML occurs in CP Andin clinical medicine CML is found in the CP and begins toreceive treatment It has been reported that overexpressionof SNHG5 inhibits the expression of mir-205-5p in CMLpatients and is related to the expression of mir-205-5p in-versely which has been shown to be a ceRNA against miR-205-5p to promote imatinib resistance in CML [7] B-cell-specific activator protein (BSAP) interacts with the DLEU2promoter directly Derepression of the DLEU2 promoter viainhibition of histone deacetylation combined with BSAP

PARP9

SGMS1

TMEM187

ARHGAP32

ITM2B

TTC28

CDH1

SNHG5

SH3BP4

RAB3D

RPS14

RPSA

SYDE2

NUP62CL

SNHG3

RPL13

EPSTI1

IncRNAmRNA

(a)

(c)

hsa00120 primary bile acid biosynthesishsa00220 arginine biosynthesis

hsa00562 inositol phosphate metabolismhsa01521 EGFR tyrosine kinase inhibitor resistance

hsa01524 platinum drug resistancehsa03010 ribosome

hsa03015 mRNA surveillance pathwayhsa04015 rap1 signaling pathway

hsa04070 phosphatidylinositol signaling systemhsa04071 sphingolipid signaling pathway

hsa04150 mTOR signaling pathwayhsa04151 PI3K-Akt signaling pathway

hsa04152 AMPK signaling pathwayhsa04261 adrenergic signaling in cardiomyocytes

hsa04360 axon guidancehsa04390 hippo signaling pathway

hsa04550 signaling pathways regulating pluripotency of stem cellshsa04650 natural killer cell mediated cytotoxicity

hsa04810 regulation of actin cytoskeletonhsa04910 insulin signaling pathway

hsa05034 alcoholismhsa05100 bacterial invasion of epithelial cells

hsa05146 amoebiasishsa05160 hepatitis C

hsa05162 measleshsa05164 influenza A

hsa05168 herpes simplex infectionhsa05200 pathways in cancer

hsa05203 viral carcinogenesishsa05213 endometrial cancer

hsa05215 prostate cancerhsa05218 melanoma

hsa05230 central carbon metabolism in cancerhsa05322 systemic lupus erythematosus

000 001 002 003 004

ndashlg (P value)

152025303540

Gene number

Pathway enrichment

1234

ndashlg (P value)(d)

0 200 400 600 800 10000

200

400

600

800

1000

Node degree

Num

ber o

f nod

es

R2 = 08876

(b)

Figure 3 Network distribution and functional analysis of the DLCN_BC (a) Global view of the DLCN_BC for CML -e DLCN_BCconsists of 2436 edges between 1510 mRNAs and 14 lncRNAs LncRNAs are circled in circles (b) Degree distribution of nodes in theDLCN_BC (c) DNB of the DLCN_BC (d) Pathway enrichment analysis of DNB in the DLCN_BC

Table 2 -e detailed information of the identified lncRNA biomarkers for CML CP AP and BC

Ensemble ID Gene name Genomic location Known research

ENSG00000203875 SNHG5 Chr6 85677007ndash85678733 (minus ) (1) Lymphoma [20](2) Gastric cancer [21]

ENSG00000231607 DLEU2 Chr13 50082169ndash50528643 (+) (1) Chronic lymphocytic leukemia [22]ENSG00000242125 SNHG3 Chr1 28505943ndash285108929 (+) (1) Hepatocellular carcinoma [23]


knockdown increases miR-15a16ndash1 expression and alsoincreases malignant B-cell death In summary therapytargeting enhanced host gene DLEU2 transcription mayaugment CLL therapy [10]

To further investigate the biological function of eachDNB a significantly enriched KEGG pathway using themRNA in each DNB is identified (Supplementary Table 4)In DLCN_CP several pathways well known in CML areenriched significantly such as Wnt signaling pathway andp53 signaling pathway In the canonical Wnt pathway theprimary role of Wnt ligands in binding to their receptors isto stabilize the cytoplasmic β-catenin by inhibiting theβ-catenin degradation complex -en β-catenin frees toenter the nucleus and activatesWnt-regulated genes throughits interaction with TCF (T-cell factor) family transcriptionfactors and concomitant recruitment of coactivator [11] Ithas been reported that Wnt1 and β-catenin depletion andoverexpression of nuclear β-catenin together with TCFbinding sites activation demonstrates that ABCB1 in CML ispositively regulated by the canonical pathway of Wnt sig-naling [12] p53 is considered to be a universal sensor ofgenotoxic stress and is involved in different DNA repairmechanisms and in cell cycle checkpoint regulation throughvarious signaling pathways -e resistance of CML to thetyrosine kinase inhibitor (imatinib) may be associated withpersistent STAT5-mediated ROS production and the ab-normality of the p53 pathway [13] In addition some otherpathways are also related to CML closely including themTOR signaling pathway and the Hippo signaling pathwayIn CML cells autophagy and cytotoxicity induced by dio-sgenin are accompanied by the production of reactive ox-ygen species (ROS) and inhibition of the mTOR signalingpathway [14] Developing drugs andor identifying tumormarkers of the Hippo signaling pathway and the Aurorakinase family alone or in combination can optimize CMLtreatment by enhancing the susceptibility of leukemia cellsto apoptosis and leading to better disease prognosis [15] InDLCN_AP the p53 signaling pathway is well known inCML and pathways in cancer and mTOR signaling pathwayare related to CML In DLCN_BC the PI3K-Akt signalingpathway is related to CML -e phosphatidylinositol-3prime-

kinase (PI3K)-Akt signaling pathway is activated by manytypes of cellular stimuli or toxic insults and regulates fun-damental cellular functions such as transcription transla-tion proliferation growth and survival [16] Further studieshave shown that phosphatidylinositol-3 kinase (PI3K)Aktnuclear factor (NF)-lB signaling pathway is involved inCML pathogenesis [17] Also pathways in cancer Phos-phatidylinositol signaling system Natural killer cell medi-ated cytotoxicity Hippo signaling pathway and mTORsignaling pathway are related to CML closely Natural killer(NK) cells are lymphocytes of the innate immune systemthat are involved in the early defense of allogeneic (nonself )cells and autologous cells that undergo various forms ofstress such as infection with viruses bacteria or parasites ormalignant transformation [18] -e presence of suppressorcells and perhaps an intrinsic inability of HNK-1+ cells maytogether contribute to the impaired NK cytotoxicity of lowNK responder CML patients [19]

4 Conclusions

Studies have shown that lncRNA can be used as an effectivebiomarker for various diseases In ceRNA theory mRNAand lncRNA can regulate each otherrsquos expression by tar-geting their common miRNAs which is important forunderstanding the progression of the disease But little isknown about CML In this study DLCN_CP DLCN_APand DLCN_BC are constructed by integrating whole-ge-nome lncRNA and mRNA expression profile data To fur-ther investigate the competition between mRNA andlncRNA in CP AP and BC of CML DNB is used to screen amodule in the dysregulated lncRNA-associated ceRNAnetworks and thus identify the lncRNA-associated ceRNAnetwork biomarkers for CML A lncRNA SNHG5 competeswith five mRNAs in the DNB of DLCN_CP A lncRNADLEU2 competes with three mRNAs in the DNB ofDLCN_AP Two lncRNAs (SNHG3 and SNHG5) competewith 15 mRNAs in the DNB of DLCN_BC Furthermore theCI constructed to detect disease outbreaks shows that CMLoccurs in CP which is consistent with clinical medicine-isalso indicates that lncRNA biomarkers are effectiveAccording to reports SNHG5 is associated with CMLAlthough DLEU2 and SNHG3 are not related to CML di-rectly related materials show that DLEU2 is closely relatedto CLL which provides us with new insights into ceRNA-mediated CML therapeutic regulatory mechanisms andfacilitates the diagnosis of biomarkers and CML therapeutictargets

Functional enrichment analysis of mRNAs coexpressedwith lncRNAs reveals biological pathways of CP AP and BCin CML Wnt signaling pathway is enriched significantly inDLCN_CP It has been reported Wnt1 and β-catenin de-pletion and overexpression of nuclear β-catenin togetherwith TCF binding sites activation demonstrates that ABCB1in CML is positively regulated by the canonical pathway ofWnt signaling p53 signaling pathway is significantlyenriched in DLCN_CP and DLCN_AP -e resistance ofCML to the tyrosine kinase inhibitor (imatinib) may beassociated with persistent STAT5-mediated ROS

030

035

040

045CI

CI sc

ore

TimeNormal CP AP BC

Figure 4 -e CI of CML Trends in CI used to detect diseaseoutbreaks indicate that CML occurs in CP which is consistent withclinical medicine


production and the abnormality of the p53 pathway PI3K-Akt signaling pathway is significantly enriched inDLCN_BC Further studies have shown that phosphatidy-linositol-3 kinase (PI3K)Aktnuclear factor (NF)-lB sig-naling pathway is involved in CML pathogenesis Besidespathways in cancer are significantly enriched in DLCN_CPDLCN_AP and DLCN_BC

In summary this study identifies three candidate sets ofbiomarkers in the ceRNA network based on DNB-e resultwill enhance our understanding of CML pathology from theceRNA perspective and help us discover the effective bio-markers of CP AP and BC in CML which help patients gettimely treatment and thereby reduce the mortality rate ofCML -is study only focuses on three periods of CML Inthe future it is hoped that the network can be built for thesame patient at different time points which will be moreaccurate in identifying biomarkers

Data Availability

-e dataset used to analyze in the study can be downloadedfrom httpswwwncbinlmnihgovgeoqueryacccgiaccGSE47927

Conflicts of Interest

-e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

-is work is supported by Key Project of the NationalNatural Science Foundation of China (Grant no11831015)the Major Research Plan of National Natural ScienceFoundation of China (Grant no91730301) the PostgraduateResearch amp Practice Innovation Program of Jiangsu Prov-ince (Grant no KYCX18_1864) and the Postgraduate Re-search amp Practice Innovation Program of the JiangnanUniversity (Grant no JNKY19_051)

Supplementary Materials

Supplementary Table 1 dysregulated lncRNA-mRNAcompeting interactions of CP AP BC for CML Supple-mentary Table 2 dysregulated lncRNA-associated ceRNAnetworks of CP AP and BC for CML (DLCN_CPDLCN_AP and DLCN_BC) Supplementary Table 3CeRNA network biomarkers in DLCN_CP DLCN_AP andDLCN_BC Supplementary Table 4 significantly enrichedpathways in DLCN_CP DLCN_AP and DLCN_BC (Supplementary Materials)

References

[1] A M Akram Z Iqbal T Akhtar et al ldquoPresence of novelcompound BCR-ABL mutations in late chronic and advancedphase imatinib sensitive CML patients indicates their possiblerole in CML progressionrdquo Cancer Biology amp 3erapy vol 18no 4 pp 214ndash221 2017

[2] S A Abraham L E M Hopcroft E Carrick et al ldquoDualtargeting of p53 and c-MYC selectively eliminates leukaemicstem cellsrdquo Nature vol 534 no 7607 pp 341ndash346 2016

[3] E Birney J A Stamatoyannopoulos and A Dutta ldquoIden-tification and analysis of functional elements in 1 of thehuman genome by the ENCODE pilot projectrdquo Naturevol 447 no 7146 pp 799ndash816 2007

[4] M Zhou Z Diao X Yue et al ldquoConstruction and analysis ofdysregulated lncRNA-associated ceRNA network identifiednovel lncRNA biomarkers for early diagnosis of humanpancreatic cancerrdquo Oncotarget vol 7 no 35 pp 56383ndash56394 2016

[5] G Zhang H Sun Y Zhang et al ldquoCharacterization ofdysregulated lncRNA-mRNA network based on ceRNA hy-pothesis to reveal the occurrence and recurrence of myo-cardial infarctionrdquo Cell Death Discovery vol 4 no 1 p 352018

[6] Y Xiao C Jiao Y Lin et al ldquolncRNA UCA1 contributes toimatinib resistance by acting as a ceRNA against miR-16 inchronic myeloid leukemia cellsrdquo DNA and Cell Biologyvol 36 no 1 pp 18ndash25 2017

[7] B He Y Bai W Kang and X Jiang ldquoLncRNA SNHG5regulates imatinib resistance in chronic myeloid leukemia viaacting as a CeRNA against MiR-205-5prdquo American Journal ofCancer Research vol 7 no 8 pp 1704ndash1713 2017

[8] K Sen A Sarkar R K Maji Z Ghosh S Gupta andT C Ghosh ldquoDeciphering the cross-talking of humancompetitive endogenous RNAs in K562 chronic myelogenousleukemia cell linerdquo Molecular BioSystems vol 12 no 12pp 3633ndash3642 2016

[9] L N Chen R Liu Z P Liu L Meiyi and A KazuyukildquoDetecting early-warning signals for sudden deterioration ofcomplex diseases by dynamical network biomarkersrdquo Sci-entific Reports vol 2 no 7391 pp 342ndash349 2012

[10] U Klein M Lia M Crespo et al ldquo-e DLEU2miR-15a16-1cluster controls B cell proliferation and its deletion leads tochronic lymphocytic leukemiardquo Cancer Cell vol 17 no 1pp 28ndash40 2010

[11] S Hatsell T Rowlands M Hiremath and P Cowin ldquoBeta-catenin and Tcfs in mammary development and cancerrdquoJournal of Mammary Gland Biology and Neoplasia vol 8no 2 pp 145ndash158 2003

[12] S Correa R Binato B Du Rocher et al ldquoWntβ-cateninpathway regulates ABCB1 transcription in chronic myeloidleukemiardquo BMC Cancer vol 12 no 1 p 303 2012

[13] Y Cheng Y Hao A Zhang et al ldquoPersistent STAT5-me-diated ROS production and involvement of aberrant p53apoptotic signaling in the resistance of chronic myeloidleukemia to imatinibrdquo International Journal of MolecularMedicine vol 41 no 1 pp 455ndash463 2018

[14] S Jiang J Fan Q Wang et al ldquoDiosgenin induces ROS-dependent autophagy and cytotoxicity via mTOR signalingpathway in chronic myeloid leukemia cellsrdquo Phytomedicinevol 23 no 3 pp 243ndash252 2016

[15] A P Z C Marsola B P Simotildees L C Palma G B-C MariaM B Sandra and A d C Fabıola ldquoExpression of Hipposignaling pathway and Aurora kinase genes in chronic my-eloid leukemiardquo Medical Oncology vol 35 no 3 p 26 2018

[16] K A West S S Castillo and P A Dennis ldquoActivation of thePI3KAKT pathway and chemotherapeutic resistancerdquo DrugResistance Updates vol 5 no 6 pp 234ndash248 2003

[17] Q Li Y Wu S Fang et al ldquoBCRABL oncogene-inducedPI3K signaling pathway leads to chronic myeloid leukemiapathogenesis by impairing immuno-modulatory function of


hemangioblastsrdquo Cancer Gene 3erapy vol 22 no 5pp 227ndash237 2015

[18] A A Maghazachi ldquoInsights into seven and single trans-membrane-spanning domain receptors and their signalingpathways in human natural killer cellsrdquo PharmacologicalReviews vol 57 no 3 pp 339ndash357 2005

[19] M Dabholkar R J Tatake S Advani et al ldquoStudies on naturalkiller cells in chronic myeloid leukemia patients in remissionrdquoNeoplasma vol 37 no 1 pp 47ndash53 1990

[20] R Tanaka H Satoh M Moriyama et al ldquoIntronic U50 small-nucleolar-RNA (snoRNA) host gene of no protein-codingpotential is mapped at the chromosome breakpoint t(36)(q27q15) of human B-cell lymphomardquo Genes to Cells vol 5 no 4pp 277ndash287 2000

[21] L Zhao H Guo B Zhou et al ldquoLong non-coding RNASNHG5 suppresses gastric cancer progression by trappingMTA2 in the cytosolrdquo Oncogene vol 35 no 44 pp 5770ndash5780 2016

[22] Y Liu M Corcoran O Rasool et al ldquoCloning of two can-didate tumor suppressor genes within a 10 kb region onchromosome 13q14 frequently deleted in chronic lympho-cytic leukemiardquo Oncogene vol 15 no 20 pp 2463ndash24731997

[23] T Zhang C Cao D Wu and L Liu ldquoSNHG3 correlates withmalignant status and poor prognosis in hepatocellular car-cinomardquo Tumor Biology vol 37 no 2 pp 2379ndash2385 2016


attributes and revealed putative regulators in CML patho-genesis [8]

In this study the dysregulated lncRNA-associatedceRNA networks (DLCN) of CP AP and BC for CML areconstructed by integrating regulatory interactions amonglncRNAs miRNAs and mRNAs and expression profile dataNext lncRNA-associated ceRNA network biomarkers in thethree DLCNs based on dynamic network biomarkers (DNB)proposed by Chen et al [9] are identified -ree potentiallncRNAs (SNHG5 SNHG3 DLEU2) are uncovered asfunctional ceRNAs with key roles in the pathogenesis ofCML In addition the critical index (CI) constructed todetect disease outbreaks shows that CML occurs in CPwhich is consistent with clinical medicine -en functionalenrichment analysis is performed on the identified ceRNAnetwork biomarkers and the role of the 3 lncRNAs in CMLis validated by KEGG enrichment analysis and literaturemining -ese novel lncRNAs acting as ceRNAs at theposttranscriptional level may become promising diagnosticbiomarkers and therapeutic targets

2 Materials and Methods

21 Microarray Data -e gene expression profile ofGSE47927 based on Affymetrix Human Gene 10 ST Array isdownloaded from the National Center for BiotechnologyInformationrsquos Gene Expression Omnibus (GEO) database(httpwwwncbinlmnihgovgeo) CML is a clonal ma-lignant proliferative disease of HSCs BCR-ABL activity inHSC causes CML 3 normal samples 6 CP patient samples 4AP patient samples and 2 BC patient samples of HSCs arechosen

-e probe set in the HuGene-1_0-st array is reannotatedto obtain the corresponding lncRNA expression profile dataFirst the probe set of the Affymetrix HuGene-1_0-st array isremapped to the human genome (GRCh38) using SeqMapIf the probe set maps to the human genome uniquelywithout a mismatch the probe set is retained Second thechromosomal location of the above probe set matchesthe chromosomal location of the lncRNA from theGENCODE project [5] Finally 848 lncRNA expressionprofiles are obtained For the mRNA expression profile ifmultiple probes are mapped to the same gene the averageis taken and finally 18288 mRNA expression profiles areobtained

22 Gene Expression Profile Analysis -e significantlydifferentially expressed (SDE) genes are detected by t-testand multiple test corrections Set the p value of 005 andthe fold change of 15 to obtain SDE mRNAs and SDElncRNAs between CP AP and BC in CML and normalsamples

By integrating the experimentally validated miRNA-mRNA interactions downloaded from TarBase v80 data-base and starBase v20 databases 769832 nonredundantmiRNA-mRNA interactions are obtained -e experimen-tally validated miRNA-lncRNA interactions are downloaded

from starBase v20 database and 10213 miRNA-lncRNArelationships are obtained

In order to identify dysregulated lncRNA-mRNAcompetitive interaction in CP AP and BC for CML Basedon the ldquoceRNA hypothesisrdquo a hypergeometric test is used toscreen lncRNA-mRNA competition pairs with p valuelt005 which are called candidate lncRNA-mRNA com-petitive interactions and 12071 candidate lncRNA-mRNAcompetitive interactions are obtained as follows

p 1113944

min NmNln c( )

iNc

Nm

i1113888 1113889

NTotal minus Nm

Nln c minus i1113888 1113889

NTotal

Nln c

1113888 1113889

(1)

where NTotal is the number of common miRNAs betweenall human miRNAs targeted human mRNAs and all humanmiRNAs regulated all human lncRNAs Nm is the numberof miRNAs targeted a given mRNA Nln c is the number ofmiRNAs which regulated a given lncRNA Nc is thenumber of common miRNAs shared by mRNA andlncRNA

Next the Pearson correlation coefficient (PCC) isused to identify mRNA-lncRNA competitive interac-tions -e PCC between mRNA and lncRNA is correlatedpositively defining the mRNA-lncRNA competitive in-teraction In order to improve the reliability of the resultslncRNA-mRNA pairs with PCC gt05 are selected asfollows

PCC(x y) 1113936

ni1 xi minus x( 1113857 yi minus y( 1113857

1113936ni1 xi minus x( 1113857

21113936

ni1 yi minus y( 1113857

21113969 (2)

where xi and yi represent the expressions of gene x and geney of the i-th sample in the reference sample respectively x

and y represent the average gene expression of gene x andgene y in the reference sample respectively

23 Identifying lncRNA Biomarkers Based on DNBGenerally each person or sample has multiple modules Tofurther identify lncRNA biomarkers DNB is used todetect potential modules Dysregulated mRNA-lncRNAcompetitive interactions obtained previously are clus-tered by hierarchical cluster analysis According to thecriteria for identification of DNB proposed by Chen et al[9] the optimal group of genes or proteins is selected asDNB -us a CI can be constructed to evaluate the DNBmodule and detect the critical state in multiple samples asfollows

CIt SDt times PCCt

OPCCt

(3)

where PCCt is the average PCC of the DNB group at time t inabsolute value OPCCt is the average PCC between the DNBgroup and the outside of the DNB group at time t in absolutevalue SDt is the standard deviation of the DNB group attime t









ITM2B

SGMS1

SNHG5

ACVR1C

ECHDC2

KIAA1598

IncRNAmRNA

(a)

(c)

000

001

002

003

004

005













minuslg (P value)

Pathway enrichment

Gene number100125150175200


(d)

0 200 400 6000

200

400

600

800

1000

Node degree

Num

ber o

f nod

es

R2 = 06738

(b)






DLEU2

BCOR

MMD

SLC35F1

IncRNAmRNA

(a)

(c)












Pathway enrichment

000

001

002

003

004

005

minuslg (P value)

Gene number

4321

35

30

25

20

15

minuslg (P value)

(d)

0 200 400 6000

200

400

600

800

1000

1200

Node degree

Num

ber o

f nod

es

R2 = 07381

(b)





PARP9

SGMS1

TMEM187

ARHGAP32

ITM2B

TTC28

CDH1

SNHG5

SH3BP4

RAB3D

RPS14

RPSA

SYDE2

NUP62CL

SNHG3

RPL13

EPSTI1

IncRNAmRNA

(a)

(c)


















000 001 002 003 004

ndashlg (P value)

152025303540

Gene number

Pathway enrichment

1234


0 200 400 600 800 10000

200

400

600

800

1000

Node degree

Num

ber o

f nod

es

R2 = 08876

(b)










4 Conclusions



030

035

040

045CI

CI sc

ore

TimeNormal CP AP BC





Data Availability




Acknowledgments




References


































ITM2B

SGMS1

SNHG5

ACVR1C

ECHDC2

KIAA1598

IncRNAmRNA

(a)

(c)

000

001

002

003

004

005













minuslg (P value)

Pathway enrichment

Gene number100125150175200


(d)

0 200 400 6000

200

400

600

800

1000

Node degree

Num

ber o

f nod

es

R2 = 06738

(b)






DLEU2

BCOR

MMD

SLC35F1

IncRNAmRNA

(a)

(c)












Pathway enrichment

000

001

002

003

004

005

minuslg (P value)

Gene number

4321

35

30

25

20

15

minuslg (P value)

(d)

0 200 400 6000

200

400

600

800

1000

1200

Node degree

Num

ber o

f nod

es

R2 = 07381

(b)





PARP9

SGMS1

TMEM187

ARHGAP32

ITM2B

TTC28

CDH1

SNHG5

SH3BP4

RAB3D

RPS14

RPSA

SYDE2

NUP62CL

SNHG3

RPL13

EPSTI1

IncRNAmRNA

(a)

(c)


















000 001 002 003 004

ndashlg (P value)

152025303540

Gene number

Pathway enrichment

1234


0 200 400 600 800 10000

200

400

600

800

1000

Node degree

Num

ber o

f nod

es

R2 = 08876

(b)










4 Conclusions



030

035

040

045CI

CI sc

ore

TimeNormal CP AP BC





Data Availability




Acknowledgments




References






























DLEU2

BCOR

MMD

SLC35F1

IncRNAmRNA

(a)

(c)












Pathway enrichment

000

001

002

003

004

005

minuslg (P value)

Gene number

4321

35

30

25

20

15

minuslg (P value)

(d)

0 200 400 6000

200

400

600

800

1000

1200

Node degree

Num

ber o

f nod

es

R2 = 07381

(b)





PARP9

SGMS1

TMEM187

ARHGAP32

ITM2B

TTC28

CDH1

SNHG5

SH3BP4

RAB3D

RPS14

RPSA

SYDE2

NUP62CL

SNHG3

RPL13

EPSTI1

IncRNAmRNA

(a)

(c)


















000 001 002 003 004

ndashlg (P value)

152025303540

Gene number

Pathway enrichment

1234


0 200 400 600 800 10000

200

400

600

800

1000

Node degree

Num

ber o

f nod

es

R2 = 08876

(b)










4 Conclusions



030

035

040

045CI

CI sc

ore

TimeNormal CP AP BC





Data Availability




Acknowledgments




References





























PARP9

SGMS1

TMEM187

ARHGAP32

ITM2B

TTC28

CDH1

SNHG5

SH3BP4

RAB3D

RPS14

RPSA

SYDE2

NUP62CL

SNHG3

RPL13

EPSTI1

IncRNAmRNA

(a)

(c)


















000 001 002 003 004

ndashlg (P value)

152025303540

Gene number

Pathway enrichment

1234


0 200 400 600 800 10000

200

400

600

800

1000

Node degree

Num

ber o

f nod

es

R2 = 08876

(b)










4 Conclusions



030

035

040

045CI

CI sc

ore

TimeNormal CP AP BC





Data Availability




Acknowledgments




References






























4 Conclusions



030

035

040

045CI

CI sc

ore

TimeNormal CP AP BC





Data Availability




Acknowledgments




References





























Data Availability




Acknowledgments




References



























identifying dysregulated lncrna-associated cerna network

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