Circ MDM2 000139, Circ ATF2 001418, Circ CDC25C 002079...

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Research Article Circ_MDM2_000139, Circ_ATF2_001418, Circ_CDC25C_002079, and Circ_BIRC6_001271 Are Involved in the Functions of XAV939 in Non-Small Cell Lung Cancer Haixiang Yu, Lei Xu, Zhengjia Liu, Bo Guo, Zhifeng Han, and Hua Xin Department of oracic Surgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province 130033, China CorrespondenceshouldbeaddressedtoHuaXin;[email protected] Received 5 May 2019; Revised 29 September 2019; Accepted 5 October 2019; Published 27 November 2019 AcademicEditor:AngeloG.Corsico Copyright©2019HaixiangYuetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited. Background.esmallmoleculeinhibitorXAV939couldinhibittheproliferationandpromotetheapoptosisofnon-smallcell lungcancer(NSCLC)cells.isstudywasconductedtoidentifythekeycircularRNAs(circRNAs)andmicroRNAs(miRNAs)in XAV939-treated NSCLC cells. Methods. After grouping, the NCL-H1299cells in the treatment group were treated by 10 μM XAV939for12h.RNA-sequencingwasperformed,andthenthedifferentiallyexpressedcircRNAs(DE-circRNAs)wereanalyzed by the edgeR package. Using the clusterprofiler package, enrichment analysis for the hosting genes of the DE-circRNAs was performed.UsingCytoscapesoftware,themiRNA-circRNAregulatorynetworkwasbuiltforthedisease-associatedmiRNAsand theDE-circRNAs.eDE-circRNAsthatcouldtranslateintoproteinswerepredictedusingcircBankdatabaseandIRESfinder tool.Finally,thetranscriptionfactor(TF)-circRNAregulatorynetworkwasbuiltbyCytoscapesoftware.Inaddition,A549and HCC-827celltreatmentwithXAV939wereusedtoverifytherelativeexpressionlevelsofkeyDE-circRNAs. Results.erewere 106DE-circRNAs(including61upregulatedcircRNAsand45downregulatedcircRNAs)betweentreatmentandcontrolgroups. EnrichmentanalysisforthehostinggenesoftheDE-circRNAsshowedthat ATF2 wasenrichedintheTNFsignalingpathway. Disease association analysis indicated that 8 circRNAs (including circ_MDM2_000139, circ_ATF2_001418, circ_CDC25C_002079,andcirc_BIRC6_001271)werecorrelatedwithNSCLC.InthemiRNA-circRNAregulatorynetwork,let-7 familymemberscirc_MDM2_000139,miR-16-5p/miR-134-5pcirc_ATF2_001418,miR-133bcirc_BIRC6_001271,and miR-221-3p/miR-222-3pcirc_CDC25C_002079regulatorypairswereinvolved.Atotalof47DE-circRNAscouldtranslate intoproteins.Additionally,circ_MDM2_000139wastargetedbytheTF POLR2A.everificationtestshowedthattherelative expressionlevelsofcirc_MDM2_000139,circ_CDC25C_002079,circ_ATF2_001418,andcirc_DICER1_000834inA549and HCC-827celltreatmentwithXAV939weredownregulatedcomparingwiththecontrol. Conclusions.Let-7familymembers and POLR2A targeting circ_MDM2_000139, miR-16-5p/miR-134-5p targeting circ_ATF2_001418, miR-133b targeting circ_BIRC6_001271,andmiR-221-3p/miR-222-3ptargetingcirc_CDC25C_002079mightberelatedtothemechanisminthe treatmentofNSCLCbyXAV939. 1. Introduction In lung cancers, non-small cell lung cancer (NSCLC) and small-celllungcarcinoma(SCLC)arethetwomaintypes[1]. Lung cancer usually can result in shortness of breath, coughing,chestpains,andweightloss[2,3].In2012,there were 1.8 million new cases of lung cancer and led to 1.6 milliondeathsglobally[4].Especially,NSCLCtakesup85% of all lung cancer cases, which are mainly induced by smoking[5].AsNSCLCprogressesfromstageItostageIV, the five-year survival rate reduces from 47% to 1% [6]. erefore,itisessentialtostudythetreatmentforNSCLC and related mechanism. XAV939isatankyrase(TNKS)inhibitorandanindirect Wnt/β-catenin signaling inhibitor that is often used to in- hibitproliferationofNSCLCcells.Guoetal.reportedthat XAV939couldinhibittheviabilityofSCLCNCI–H446cells bycausingcellapoptosisthroughtheWntsignalingpathway Hindawi Canadian Respiratory Journal Volume 2019, Article ID 9107806, 12 pages https://doi.org/10.1155/2019/9107806

Transcript of Circ MDM2 000139, Circ ATF2 001418, Circ CDC25C 002079...

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Research ArticleCirc_MDM2_000139, Circ_ATF2_001418, Circ_CDC25C_002079,and Circ_BIRC6_001271 Are Involved in the Functions ofXAV939 in Non-Small Cell Lung Cancer

Haixiang Yu, Lei Xu, Zhengjia Liu, Bo Guo, Zhifeng Han, and Hua Xin

Department of �oracic Surgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province 130033, China

Correspondence should be addressed to Hua Xin; [email protected]

Received 5 May 2019; Revised 29 September 2019; Accepted 5 October 2019; Published 27 November 2019

Academic Editor: Angelo G. Corsico

Copyright © 2019 Haixiang Yu et al. -is is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background. -e small molecule inhibitor XAV939 could inhibit the proliferation and promote the apoptosis of non-small celllung cancer (NSCLC) cells. -is study was conducted to identify the key circular RNAs (circRNAs) and microRNAs (miRNAs) inXAV939-treated NSCLC cells. Methods. After grouping, the NCL-H1299 cells in the treatment group were treated by 10 μMXAV939 for 12 h. RNA-sequencing was performed, and then the differentially expressed circRNAs (DE-circRNAs) were analyzedby the edgeR package. Using the clusterprofiler package, enrichment analysis for the hosting genes of the DE-circRNAs wasperformed. Using Cytoscape software, the miRNA-circRNA regulatory network was built for the disease-associated miRNAs andthe DE-circRNAs. -e DE-circRNAs that could translate into proteins were predicted using circBank database and IRESfindertool. Finally, the transcription factor (TF)-circRNA regulatory network was built by Cytoscape software. In addition, A549 andHCC-827 cell treatment with XAV939 were used to verify the relative expression levels of key DE-circRNAs. Results. -ere were106 DE-circRNAs (including 61 upregulated circRNAs and 45 downregulated circRNAs) between treatment and control groups.Enrichment analysis for the hosting genes of the DE-circRNAs showed that ATF2 was enriched in the TNF signaling pathway.Disease association analysis indicated that 8 circRNAs (including circ_MDM2_000139, circ_ATF2_001418,circ_CDC25C_002079, and circ_BIRC6_001271) were correlated with NSCLC. In the miRNA-circRNA regulatory network, let-7family members⟶circ_MDM2_000139, miR-16-5p/miR-134-5p⟶circ_ATF2_001418, miR-133b⟶circ_BIRC6_001271, andmiR-221-3p/miR-222-3p⟶circ_CDC25C_002079 regulatory pairs were involved. A total of 47 DE-circRNAs could translateinto proteins. Additionally, circ_MDM2_000139 was targeted by the TF POLR2A. -e verification test showed that the relativeexpression levels of circ_MDM2_000139, circ_CDC25C_002079, circ_ATF2_001418, and circ_DICER1_000834 in A549 andHCC-827 cell treatment with XAV939 were downregulated comparing with the control. Conclusions. Let-7 family membersand POLR2A targeting circ_MDM2_000139, miR-16-5p/miR-134-5p targeting circ_ATF2_001418, miR-133b targetingcirc_BIRC6_001271, and miR-221-3p/miR-222-3p targeting circ_CDC25C_002079 might be related to the mechanism in thetreatment of NSCLC by XAV939.

1. Introduction

In lung cancers, non-small cell lung cancer (NSCLC) andsmall-cell lung carcinoma (SCLC) are the twomain types [1].Lung cancer usually can result in shortness of breath,coughing, chest pains, and weight loss [2, 3]. In 2012, therewere 1.8 million new cases of lung cancer and led to 1.6million deaths globally [4]. Especially, NSCLC takes up 85%of all lung cancer cases, which are mainly induced by

smoking [5]. As NSCLC progresses from stage I to stage IV,the five-year survival rate reduces from 47% to 1% [6].-erefore, it is essential to study the treatment for NSCLCand related mechanism.

XAV939 is a tankyrase (TNKS) inhibitor and an indirectWnt/β-catenin signaling inhibitor that is often used to in-hibit proliferation of NSCLC cells. Guo et al. reported thatXAV939 could inhibit the viability of SCLC NCI–H446 cellsby causing cell apoptosis through theWnt signaling pathway

HindawiCanadian Respiratory JournalVolume 2019, Article ID 9107806, 12 pageshttps://doi.org/10.1155/2019/9107806

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[7]. Besides, XAV939 also repressed the proliferation andmigration of lung adenocarcinoma A549 cells through at-tenuating the Wnt signaling pathway [8]. Moreover, it isknown that circular RNAs (circRNAs) are implicated in thedevelopment and progression of cancers [9].CircRNA_100876 is tightly correlated with the oncogenesisof NSCLC, which may function as a promising prognosticmarker and therapeutic target for the disease [10].Circ_0014130 can serve as a candidate biomarker forNSCLC, and may play critical roles in the formation of thetumor [11]. Besides, plenty of reports have stated that thecircRNAs usually be involved in the NSCLC by the in-teraction with miRNAs. CircRNA forkhead box O3(FOXO3) acts as a tumor suppressor via sponging miR-155in NSCLC, and thus expression restoration of circRNAFOXO3 can be a new therapeutic option for NSCLC [12].CircRNA_100833 (also called circRNA fatty acid desaturases2, circFADS2) promotes the progression of NSCLC throughmediating miR-498 expression; therefore, circFADS2 can beutilized as a novel target for treating NSCLC [13]. However,the key circRNAs associated with the pathogenesis ofNSCLC have not been entirely revealed.

Our preliminary experiments showed that XAV939could significantly inhibit the proliferation and promote theapoptosis of NSCLC NCL-H1299 cells, and 10 μM is theappropriate XAV939 concentration for treating NCL-H1299cells (data not shown). In this study, XAV939 (10 μM) wasused to treat NCL-H1299 cells in the treatment group. Afterhigh throughput sequencing, the sequencing data wereanalyzed using various bioinformatics methods. -is studymight contribute to revealing the key circRNAs mediated byXAV939 in NCL-H1299 cells.

2. Materials and Methods

2.1. Sample Source. -e NSCLC NCL-H1299 cell line wasobtained from the Type Culture Collection of the ChineseAcademy of Sciences (Shanghai, China). Six NCL-H1299 cellsamples were randomly and evenly divided into the treat-ment group and control group. -e cells in the treatmentgroup were treated by XAV939 (10 μM) for 12 h. -e cells inthe control group were treated by equal volume of dimethylsulfoxide (DMSO). Afterwards, the cells were harvested forthe following sequencing.

2.2. Library Construction and RNA-Sequencing. Using theTRIzol reagent (Takara Biotechnology Co., Ltd., Dalian,China), total RNA was extracted from the cells according tothe manufacturer’s instruction. -en, the quality andquantity of total RNA were detected using a -ermo Sci-entific NanoDrop 2000 (-ermo Fisher Scientific, Inc.,Wilmington, DE, USA). Subsequently, cDNA library wasconstructed following the manufacturer’s manual by aTruseqTM RNA Library Prep kit for Illumina® (cat no.E7530L; New England BioLabs, Inc., Ipswich, MA, USA).Based on the 150 paired end method [14], sequencing wasperformed by the Illumina Hiseq 4000 platform (Illumina,Inc., San Diego, CA, USA). -e sequencing data were

deposited in Sequence Read Archives (SRA, https://http://www.ncbi.nlm.nih.gov/sra) database under the accessionnumber SRP136747.

2.3. Quality Control and Preprocessing of Sequencing Data.Quality control of the sequencing data was conducted usingTrimmomatic tool [15]. Barcode sequences were removed,and the bases with continuous quality <10 at two ends of thereads were taken out. Subsequently, the reads that containedless than 80% bases with Q> 20 were filtered out, and thereads with length <50 nt were also wiped off. Based onTopHat2 software (http://ccb.jhu.edu/software/tophat) [16],clean reads were aligned to the human reference genome(GRCh38.p7 and GENCODE) [17]. In order to obtain back-spliced junctions reads, the reads that could not be com-pared to the reference genome in a linear way were com-pared in a nonlinear way using TopHat-fusion algorithm[18].

2.4. Identification and Annotation of CircRNAs.CircRNAs were identified using CIRCexplorer2 software[19], and the circRNAs with junctions read count> 2 wereselected. Based on the locations of circRNAs in the genomeand their relationships with genes, the selected circRNAswere annotated. Firstly, the circRNAs were classifiedaccording to their locations. Secondly, the circRNAs wereconducted with functional annotation based on thecircRNA-hosting genes. -e RefSeq gene annotation filesdownloaded from the University of California Santa Cruz(UCSC, http://genome.ucsc.edu/) database [20] were used asreferences for annotating. Finally, the circRNAs were namedcombined with the names of their hosting genes.

2.5. Identification of the Differentially Expressed CircRNAs(DE-CircRNAs) and Enrichment Analysis. -e expression ofthe circRNAs was estimated based on the number of back-spliced reads. Using the edgeR package (http://bioconductor.org/packages/release/bioc/html/edgeR.html)in R [21], the DE-circRNAs between treatment and controlgroups were screened. -e |log2 fold change (FC)|> 0.585and p value <0.05 were defined as the thresholds. -e ex-pression of circRNA was required to be higher than 0 in atleast 2 samples, and the ineligible circRNAs were filtered out.

Using the clusterprofiler package (http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) in R[22], the hosting genes of the DE-circRNAs were studiedwith Gene Ontology (GO, including biological process (BP),molecular function (MF), and cellular component (CC)categories) [23] and Kyoto encyclopedia of genes and ge-nomes (KEGG) [24] enrichment analyses. -e significantthreshold was set at p value <0.05.

2.6.MiRNASpongeAnalysis andDiseaseAssociationAnalysis.Previous studies have found that there are multiple targetsites of miRNAs in some circRNAs sequences, and thuscircRNAs can bind with miRNAs to play certain regulatory

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roles in vivo [25, 26]. Using miRanda tool [27], miRNA-circRNA pairs were predicted for the DE-circRNAs.

Based on DisGenet (http://www.disgenet.org) [28] andmiRWalk (http://mirwalk.uni-hd.de/) [29] databases, thegenes or miRNAs correlated with NSCLC were searched. Ifthe hosting genes of DE-circRNAswere related to NSCLC, theDE-circRNAs were deemed to be associated with the disease.For the disease-associatedmiRNAs and the DE-circRNAs, themiRNA-circRNA regulatory network was built using Cyto-scape software (http://www.cytoscape.org) [30].

2.7. Prediction of the CircRNAs with the Ability to Translateinto Proteins. -e corresponding data of the DE-circRNAswere obtained from circBank (http://www.circbank.cn/) andcircBase [31] databases. -e DE-circRNAs with protein-encoding ability (coding_prob> 0.364) were selected fromcircBank database. Besides, the IRESfinder tool [32] wasused to predict whether there were internal ribosome entrysites (IRESs) in the DE-circRNAs. -e circRNAs with bothprotein-encoding ability and IRESs were considered to bewith the ability to translate into proteins.

2.8. Construction of Transcription Factor (TF)-CircRNARegulatoryNetwork. -eTRCirc database [33] (http://www.licpathway.net/TRCirc/view/index) integrates the chip-se-quencing data, RNA-sequencing data, and 450k array data inENCODE database and combines with human circRNA

information in circBase database for the analysis of circRNAtranscriptional regulation. TFs were predicted for the DE-circRNAs using TRCirc database [33], and then TF-circRNAregulatory network was constructed using Cytoscape soft-ware [30].

2.9.ValidationofKeyDE-circRNAs inA549andHCC-827CellTreatment with XAV939. In order to observe the effect ofXAV939 on the expressions of key DE-circRNAs, A549 andHCC-827 cells were used. A549 and HCC-827 cells werepurchased from the Cell Bank of Chinese Academy ofScience (Shanghai, China). Cells in the logarithmic growthphase of the experimental group were treated with 10 μMXAV939, and the control group was supplemented with anequal volume of DMSO. -en, the total RNA was extractedby the TRIzol reagent (Takara Biotechnology Co., Ltd.,Dalian, China). Finally, the relative expression levels of keyDE-circRNAs were detected by the real-time reverse tran-scription polymerase chain reaction (RT-PCR). -e primerinformation is shown in the Supplementary Table 1.

3. Results

3.1. Data Preprocessing, Identification, and Annotation ofCircRNAs. -e results of quality control and sequencealignment separately were listed in Tables 1 and 2. A total of8914 circRNAs corresponding to 3542 hosting genes wereidentified. After annotation of the circRNAs, the GO

Table 1: -e results of quality control for the sequencing data.

Sample Raw reads Clean reads Effective rate (%) Error rate (%) Q20 (%) Q30 (%) GC content (%)k3 55388409 54435729 98.28 0.01 98.17 95.44 46.97k2 52553686 51681295 98.34 0.01 97.93 94.92 47.2k1 52146382 51484123 98.73 0.01 98.18 95.44 47.73s3 54313514 53504243 98.51 0.01 96.92 92.81 47.05s2 49367345 48641646 98.53 0.01 98.19 95.47 47.52s1 53737400 52909845 98.46 0.01 97.76 94.55 47.31Note. s1, s2, and s3 represent the samples in the treatment group. k1, k2, and k3 represent the samples in the control group. Sample, the name of samples; rawreads, the number of raw reads; clean reads, the number of clean reads; error rate, the average base sequencing error rate; Q20, the percentage of the bases withPhred value> 20; Q30, the percentage of the bases with Phred value> 30; GC content, the percentage of G/C bases.

Table 2: -e results of sequence alignment.

Reads Mapping k3 k2 k1 s3 s2 s1

Left reads

Input 54435729 51681295 51484123 53504243 48641646 52909845Mapped reads 43706641 41399669 40805316 42960786 38202327 41965536

Mapped rate (%) 80.29 80.11 79.26 80.29 78.54 79.32Uniquely mapped 40483797 38479400 37712691 40029424 35183009 38751263

Uniquely mapped rate (%) 74.37 74.46 73.25 74.82 72.33 73.24

Right reads

Input 54435729 51681295 51484123 53504243 48641646 52909845Mapped reads 41882371 39205669 39063079 40162292 36529153 39526360

Mapped rate (%) 76.94 75.86 75.87 75.06 75.10 74.71Uniquely mapped 38760420 36422877 36072263 37394997 33623734 36501173

Uniquely mapped rate (%) 71.20 70.48 70.06 69.89 69.13 68.99Overall read Mapped rate (%) 78.60 72.60 77.60 77.70 76.80 77.00Note. s1, s2, and s3 represent the samples in the treatment group. k1, k2, and k3 represent the samples in the control group. Left/right reads, sequences at thetwo ends; input, the total number of sequences; mapped reads, the number of the reads mapped to the genome; mapping rate, the ratio of the reads mapped tothe genome; unique mapped, the number of the reads mapped to a unique position in the genome; unique rate, the ratio of the reads mapped to a uniqueposition in the genome.

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functional terms enriched for the hosting genes of thecircRNAs are shown in Figure 1, such as biological regu-lation, cellular progress, chemoattractant activity, and so on.

3.2.Differential ExpressionAnalysis andEnrichmentAnalysis.-ere were 106 DE-circRNAs between treatment andcontrol groups, including 61 upregulated circRNAs and 45

downregulated circRNAs (Table 3). -e clustering heatmapof the DE-circRNAs is presented in Figure 2. -e samples ofcontrol and treatment groups were significantly separated bythe DE-circRNAs. For the hosting genes of the DE-circR-NAs, 204 BP terms (such as microtubule cytoskeleton or-ganization), 27 CC terms (such as centrosome), 40 MF terms(such as N-acetyltransferase activity), and 9 KEGG pathways

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(such as the TNF signaling pathway, which involved acti-vating transcription factor 2 (ATF2)) were enriched(Figure 3).

3.3.miRNASpongeAnalysis andDiseaseAssociationAnalysis.After miRNA-circRNA pairs were predicted for the DE-circRNAs, the top 10 miRNAs targeting more circRNAs wereselected and listed in Table 4. -rough disease associationanalysis, 8 circRNAs (including circ_MDM2_000139, corre-sponding to hosting gene MDM2 (murine double minute 2);circ_ATF2_001418, corresponding to hosting gene ATF2;circ_CDC25C_002079, corresponding to hosting gene CDC25C(cell division cycle 25C); and circ_BIRC6_001271, corre-sponding to hosting gene BIRC6 (baculoviral inhibitor of ap-optosis repeat-containing 6)) were found to be related to thedisease (Table 5). Finally, the miRNA-circRNA regulatorynetwork was constructed, which had 106 nodes (including 38miRNAs and 64 circRNAs) and 194 regulatory pairs (includinglet-7 family members⟶circ_MDM2_000139, miR-16-5p/miR-134-5p⟶circ_ATF2_001418, miR-133b⟶circ_BIRC6_001271, hsa-miR-197-3p circ_RIPK1_001778, hsa-miR-128-2-5p circ_PRKAA1_001969, and miR-221-3p/miR-222-3p⟶circ_CDC25C_002079) (Figure 4).

3.4. Prediction of the CircRNAs with the Ability to Translateinto Proteins. -e DE-circRNAs were mapped to circBankand circBase databases, and three novel circRNAs (includingcirc_ATF2_001418, circ_FLYWCH1_007212, and circ_GTF2IP1_006504) were not found in the two databases.Among the 65 DE-circRNAs with protein-encoding ability,there were 47 DE-circRNAs which had IRESs. -erefore, the47 DE-circRNAs were taken as circRNAs that couldtranslate into proteins.

3.5. Construction of TF-CircRNA Regulatory Network.After TFs were predicted for the DE-circRNAs, the TF-circRNA regulatory network was built (Figure 5). In the TF-circRNA regulatory network, there were 72 nodes (including22 circRNAs and 50 TFs) and 115 edges. Especially,circ_MDM2_000139, which was correlated with the disease,was targeted by RNA polymerase II (POLR2A) in the TF-circRNA regulatory network.

-e expressed levels of key DE-circRNAs in A549 andHCC-827 cells are treated by XAV939

In order to validate the effect of XAV939 on other NSCLCcells, the relative levels of key DE-cirRNAs such as circ_MD-M2_000139, circ_ATF2_001418, circ_DICER1_000834, circ_PRKAA1_001969, circ_RIPK1_001778, and circ_CDC25C_002079 were further studied in A549 and HCC-827 cells. Asshown in Figure 6, comparing with the NC group, the relativeexpression levels of circ_MDM2_000139, circ_CDC25C_002079, circ_ATF2_001418, and circ_DICER1_000834 inA549and HCC-827 cell treatment with XAV939 were down-regulated (P< 0.05 or P< 0.01). -ere were no differences inthe circ_PRKAA1_001969 levels between the control andXAV939 group. In addition, after treatment with XAV939, thecirc_RIPK1_001778 levels were upregulated in the A549 cells,and no obvious change was found in HCC-827 cells.

4. Discussion

In this study, 106 DE-circRNAs (including 61 upregulatedcircRNAs and 45 downregulated circRNAs) were screenedbetween the treatment and control groups. Disease associ-ation analysis showed that 8 circRNAs (includingcirc_MDM2_000139, circ_ATF2_001418, circ_CDC25C_002079, and circ_BIRC6_001271) were correlated withNSCLC. Besides, let-7 family members⟶circ_MDM2_000139, miR-16-5p/miR-134-5p⟶circ_ATF2_001418, miR-133b⟶circ_BIRC6_001271, and miR-221-3p/miR-222-3p⟶circ_CDC25C_002079 regulatory pairs wereinvolved in the miRNA-circRNA regulatory network. Fur-thermore, 47 DE-circRNAs were taken as circRNAs thatcould translate into proteins. In addition, circ_MDM2_000139 was found to be targeted by POLR2A in theTF-circRNA regulatory network. -e results of validationexperiments showed that circ_MDM2_000139, circ_CDC25C_002079, circ_ATF2_001418, and circ_DICER1_000834 were also downregulated in the A549 and HCC-827cells after treatment with the XAV939, which were con-sistent with the sequencing results.

Tumor necrosis factor-α (TNF-α) acts as a critical in-flammatory factor that links inflammation and tumor, which isalso correlated with angiogenesis, proliferation, invasion, andmigration in human cancers [34]. Via TNF-α/nuclear factorkappa B (NF-κB) and phosphatidylinositol 3-kinase (PI3K)/

Table 3: -e most significant differentially expressed circular RNAs (circRNAs) (top 10 listed).

CircRNA_name Chrome Exon count Gene name logFC logCPM LR P value FDRcirc_POC1B_000154 chr12 6 POC1B –5.905057 8.894108 13.44331 0.000246 0.277026circ_RUFY2_003156 chr10 4 RUFY2 –5.727558 8.778988 11.1986 0.000819 0.691708circ_HMGCR_002010 chr5 3 HMGCR –5.434035 8.602139 8.488467 0.003574 0.711366circ_NPLOC4_000576 chr17 3 NPLOC4 –5.243566 8.494641 7.161364 0.007449 0.711366circ_CAPRIN2_000093 chr12 5 CAPRIN2 –5.228417 8.487116 7.073269 0.007824 0.711366circ_ZMYND8_006470 chr20 4 ZMYND8 5.2603368 8.50343 7.25106 0.007086 0.711366circ_SENP5_006766 chr3 1 SENP5 5.3402468 8.54897 7.790759 0.005251 0.711366circ_ARFGEF1_008029 chr8 3 ARFGEF1 5.6240365 8.715481 10.05927 0.001516 0.711366circ_RC3H2_007996 chr9 7 RC3H2 5.9666606 8.935713 14.32273 0.000154 0.260251circ_DCBLD2_006709 chr3 5 DCBLD2 7.1049669 9.787842 39.81423 2.79E-10 9.44E-07Note. FC, fold change; CPM, counts per million; LR, likelihood ratio; FDR, false discovery rate.

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AKT pathways, sotetsuflavone suppresses the migration andinvasion of NSCLC cells and may be effective in treating thetumor [35]. -rough targeting ATF2, tumor suppressor miR-

204 can inhibit cell proliferation and migration, and promoteG1 arrest and cell apoptosis in NSCLC [36]. Elevated miR-16 isan independent factor that predicts unfavorable overall survival

s3s2s1k3k1k2

circ_CEP350_002555circ_DICER1_000834circ_BUB1_001363circ_ANKRD17_001165circ_ACBD6_002557circ_CAPRIN2_000093circ_PCGF6_000451circ_RHOBTB3_002031circ_SS18_001017circ_PRKAA1_001969circ_EEF1AKMT2_000464circ_SLC37A3_001755circ_NPLOC4_000576circ_ELF2_001212circ_GLG1_000638circ_CLNS1A_000296circ_RIPK1_001778circ_ATF2_001418circ_HMGCR_002010circ_KLC1_000857circ_IL27RA_000886circ_FNDC3B_002290circ_POC1B_000154circ_ZNF420_000930circ_MDM2_000139circ_LCOR_000432circ_DDX21_000403circ_RUFY2_003156circ_TAF1_001504circ_DCAF1_002197circ_PHKB_000610circ_GABPB1_000686circ_BRWD1_002625circ_NAA16_000026circ_ANO6_000108circ_BIRC6_001271circ_TBC1D31_002933circ_ATXN2_000185circ_RBBP8_003487circ_SPAST_003597circ_GPBP1_003993circ_CDC25C_002079circ_UTRN_001904circ_EIF3M_000244circ_LARP4B_000337circ_GNB1_004160circ_PBX3_002787circ_SMARCC1_006692circ_ANKHD1_006659circ_CCDC14_006718circ_ETFA_006137circ_PHKB_006097circ_ATXN3_006176circ_SENP5_006766circ_CLIP3_003443circ_ZNF569_003444circ_CCDC91_005854circ_PIK3C3_006244circ_ACTN1_006163circ_TRA2B_006761circ_NOM1_006537circ_CNTLN_006876circ_SUGP1_006198circ_TBC1D31_006972circ_MARCH3_006641circ_UBE2J2_006772circ_STAM2_006399circ_CLASP2_006685circ_MARS_005871circ_VIPAS39_006172circ_MAP3K5_006595circ_IGF2BP3_006488circ_DCBLD2_006709circ_HUWE1_001501circ_ZGRF1_006311circ_REV1_006383circ_POLQ_006715circ_GTF2IP1_006504circ_AGTPBP1_006893circ_CFAP70_003168circ_TMEM245_004374circ_ARFGEF1_008029circ_MKLN1_007656circ_CNTROB_007161circ_GNPDA2_007422circ_SLF1_002024circ_APLF_007499circ_TMEM181_001931circ_DOCK1_004726circ_MTO1_001829circ_PCM1_006941circ_PPP6R3_005930circ_ZMYND8_006470circ_PHC3_007842circ_SRCAP_007221circ_CD2AP_007692circ_RC3H2_007996circ_FLYWCH1_007212circ_STK39_007537circ_FGGY_006800circ_HECTD2_006002circ_UXS1_006384circ_KMT2E_006521circ_HTT_006280circ_ACTR6_005885circ_COQ2_006299

Sample

Condition

Condition

Control

Treat

Sample

k1

k2

k3

s1

s2

s3

–1

0

1

Figure 2: -e clustering heatmap of the differentially expressed circular RNAs (circRNAs). s1, s2, and s3 represent the samples in thetreatment group. k1, k2, and k3 represent the samples in the control group.

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(OS) and disease-free survival (DFS), and thus miR-16 ex-pressionmay be taken as a prognostic indicator inNSCLC [37].Via mediating oncogenic cyclin D1 (CCND1), miR-134 re-presses proliferation, invasion, and migration and acceleratesapoptosis of NSCLC cells [38, 39]. ATF2 was enriched in theTNF signaling pathway, suggesting that miR-16-5p/miR-134-5p targeting circ_ATF2_001418might act in themechanisms ofNSCLC through the TNF signaling pathway. We concludedthat the TNF signaling pathway might be another potentialtarget of XAV939.

MDM2 and matrix metalloproteinase 9 (MMP9) ex-pressions are related to the formation and migration of

lung cancer; therefore, they can serve as the markers for thetreatment and prognosis of the disease [40]. MDM2 geneamplification is closely associated with DFS and OS, in-dicating that MDM2 amplification can be used for pre-dicting the survival of NSCLC patients who experiencedsurgical treatment [41]. -e let-7 family members functionas tumor suppressors in lung cancer, which can be re-pressed by Lin-28 and inhibit cell proliferation [42].-rough suppressing the transcription of POLR2A, the typeII glycoprotein CD26 plays an inhibitory role in tumorgrowth [43]. -erefore, let-7 family members and POLR2Atargeting circ_MDM2_000139 might be also correlatedwith the progression of NSCLC. However, after consultingthe literature, there were few reports providing direct re-lationship between let-7 family members, POLR2A, andXAV939.

Increased BIRC6 protein level may be a predictive factorfor chemoresistance and an adverse prognostic marker forNSCLC, and inhibiting BIRC6 may be a useful method fortreating the tumor [44]. MiR-133b is found to be able todecrease cisplatin resistance, and its overexpression sup-presses cell growth and invasion in cisplatin-resistantNSCLC via regulating glutathione-S-transferase P1 (GSTP1)[45]. -rough reducing CDC25C and CDC2 protein levels,the heat shock protein 90 (HSP90) inhibitor is implicated inantiproliferative activity and tumor progression in lung

Microtubule anchoringMicrotubule cytoskeleton organization

Somatic diversification of immune receptorsCardiac muscle cell apoptotic processStriated muscle cell apoptotic process

Microtubule organizing center partCentriole

CentrosomeTrans−Golgi network

Site of double−strand breakPeptide N−acetyltransferase activity

Ubiquitin conjugating enzyme activityUbiquitin−like protein conjugating enzyme activity

Vinculin bindingN−acetyltransferase activity

Ubiquitin mediated proteolysisProtein processing in endoplasmic reticulum

Glucagon signaling pathwayTNF signaling pathway

Amino sugar and nucleotide sugar metabolismD

escr

iptio

n

CategoryBP

CC

KEGG

MF

2.5 5.0 7.5 10.00.0Count

Figure 3:-e functional terms and pathways enriched for the hosting genes of the differentially expressed circular RNAs (circRNAs) (top 5listed). BP, biological process; CC, cellular component; MF, molecular function; and KEGG, Kyoto encyclopedia of genes and genomes.-eblack trend line represents -log10 (p value).

Table 4: -e top 10 miRNAs targeting more circular RNAs(circRNAs).

MiRNA Frequencyhsa-miR-4659b-3p 32hsa-miR-4778-3p 32hsa-miR-4659a-3p 30hsa-miR-4691-5p 27hsa-miR-6792-3p 27hsa-miR-3059-5p 24hsa-miR-6875-3p 24hsa-miR-103a-1-5p 23hsa-miR-103a-2-5p 22hsa-miR-6881-3p 21

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cancer cells and thus can be applied for the treatment of lungcancer [46]. MiR-221 and miR-222 are involved in multiplehuman cancers, which play tumor-suppressive roles in lungcancer and may be promising targets for the therapy of thedisease [47]. -ese indicated that miR-133b targetingcirc_BIRC6_001271 and miR-221-3p/miR-222-3p targetingcirc_CDC25C_002079 might be implicated in the patho-genesis of NSCLC. Dicer is important for microRNA-me-diated silencing and other RNA interference, which wereprofoundly involved in cancer related networks [48]. Dıaz-

Garcıa et al. found that the DICER1 expression level variedamong cancer specimens and 66% cancer samples haddecreased DICER1 mRNA. Besides, the median overallsurvival (OS) of those with low level of DICER1 mRNA wassubstantially reduced [49]. In addition, the copy numbervariation of DICER1 correlates well with the expression andsurvival of NSCLC, and the increased expression DICER1increases the survival [50]. In our study, we found thatcirc_DICER1_000834 was downregulated in the A549 andHCC-827 cells after treatment with the XAV939.-e reports

Table 5: -e circular RNAs (circRNAs) correlated with non-small cell lung cancer.

CircRNA_name Gene symbol Disease name Score No. of Pmids No. of Snps Sourcecirc_MDM2_000139 MDM2 Non-small cell lung carcinoma 0.2085165 32 2 BEFREE; CTD_humancirc_ATF2_001418 ATF2 Non-small cell lung carcinoma 0.0005495 2 0 BEFREEcirc_DICER1_000834 DICER1 Non-small cell lung carcinoma 0.0008242 3 0 BEFREEcirc_PRKAA1_001969 PRKAA1 Non-small cell lung carcinoma 0.0002747 1 0 BEFREEcirc_BIRC6_001271 BIRC6 Non-small cell lung carcinoma 0.0002747 1 0 BEFREEcirc_BUB1_001363 BUB1 Non-small cell lung carcinoma 0.0002747 1 0 BEFREEcirc_RIPK1_001778 RIPK1 Non-small cell lung carcinoma 0.0005495 2 0 BEFREEirc_CDC25C_002079 CDC25C Non-small cell lung carcinoma 0.0002747 1 0 BEFREE

circ_SUGP1_006198

hsa-miR-107

circ_EIF3M_000244

circ_CAPRIN2_000093

hsa-miR-31-5p

circ_MARCH3_006641

circ_GNPDA2_007422

hsa-miR-22-3p

circ_ARFGEF1_008029

hsa-miR-15a-5p

circ_SMARCC1_006692

circ_GPBP1_003993

circ_LARP4B_000337

circ_NPLOC4_000576

circ_PRKAA1_001969hsa-miR-128-2-5p

circ_MKLN1_007656

hsa-miR-16-5p

circ_ANKRD17_001165

circ_HTT_006280

circ_PHKB_000610

circ_PHKB_006097

hsa-miR-185-5p

hsa-miR-19a-3p

circ_LCOR_000432circ_ACTN1_006163

circ_ATF2_001418

circ_MAP3K5_006595

hsa-let-7e-5p

circ_RBBP8_003487

hsa-let-7b-5p circ_ZGRF1_006311

hsa-let-7f-5p

circ_MDM2_000139

hsa-let-7g-5phsa-let-7a-5p

circ_RHOBTB3_002031

hsa-miR-98-5p

hsa-miR-183-5p

circ_ZMYND8_006470

circ_SS18_001017

hsa-miR-188-5p

circ_GTF2IP1_006504

circ_UTRN_001904

circ_FGGY_006800

hsa-miR-451a

hsa-miR-197-3p

circ_TBC1D31_006972

circ_KMT2E_006521

circ_COQ2_006299

hsa-miR-134-5pcirc_RC3H2_007996

hsa-miR-21-5p

circ_GLG1_000638

circ_HMGCR_002010

circ_BIRC6_001271

circ_ZNF420_000930

hsa-miR-31-3p

hsa-miR-29a-3pcirc_CFAP70_003168

circ_POC1B_000154

circ_AGTPBP1_006893

hsa-miR-133bhsa-let-7b-3p

circ_ETFA_006137

hsa-miR-200c-3p

circ_GABPB1_000686

circ_REV1_006383

circ_TBC1D31_002933

hsa-miR-429

circ_SLF1_002024

hsa-miR-200b-3p

circ_ACTR6_005885

circ_TRA2B_006761

hsa-miR-622

hsa-miR-222-3p

hsa-miR-221-3p

circ_SPAST_003597hsa-miR-449c-5p

circ_CLASP2_006685

circ_STAM2_006399

circ_HECTD2_006002

circ_RIPK1_001778

circ_IL27RA_000886

circ_ATXN2_000185

circ_CD2AP_007692

circ_DCBLD2_006709

circ_CDC25C_002079

circ_PBX3_002787

hsa-miR-145-5p circ_EEF1AKMT2_000464

circ_APLF_007499

circ_SENP5_006766

hsa-miR-29c-3p

circ_BRWD1_002625

hsa-miR-29b-3p

circ_CCDC14_006718

circ_HUWE1_001501

circ_PCM1_006941

circ_NAA16_000026

hsa-miR-20a-5p

hsa-miR-30e-5p

circ_ANO6_000108

hsa-miR-30c-5p

circ_FLYWCH1_007212hsa-miR-93-5p

Figure 4: -e miRNA-circular RNA (circRNA) regulatory network. Blue triangles, red circles, and green circles represent miRNAs,upregulated circRNAs, and downregulated circRNAs, respectively. -e circles with yellow ring represent disease-associated circRNAs.

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CTCF MAX

RXRA

TAF1MXI1

circ_DCBLD2_006709

RAD21

circ_ACTN1_006163

MAZ

GABPA

SMC3

NFATC1

MEF2CPBX3

STAT3

BHLHE40

BCL3

TCF3

YY1

PML

circ_PIK3C3_006244

IRF4BCL11ASP1

PAX5

circ_PHC3_007842

BCLAF1

circ_EEF1AKMT2_000464

MTA3

TBL1XR1

USF2 USF1

circ_IGF2BP3_006488

circ_RHOBTB3_002031

EBF1

circ_ANKRD17_001165

circ_GLG1_000638

circ_SENP5_006766

circ_SLC37A3_001755

circ_EIF3M_000244

circ_GABPB1_000686

SIN3A

POU2F2

circ_RIPK1_001778

circ_CD2AP_007692

circ_CLNS1A_000296BATF

SPI1NFIC

circ_MARS_005871

RUNX3

CEBPB

ZNF143

ELK1

TBP

REST

ELF1TCF12

CHD2 EGR1

FOXM1

NFKB

ATF2

circ_CEP350_002555 EP300

circ_UTRN_001904

MEF2A

IKZF1

circ_TRA2B_006761

circ_MDM2_000139

circ_NOM1_006537

POLR2A

Figure 5: -e transcription factor (TF)-circRNA regulatory network. Orange inverted triangles represent TFs. Red circles and green circlesrepresent upregulated circRNAs and downregulated circRNAs, respectively. -e circles with yellow ring represent the disease-associatedcircRNAs.

circ_MDM2_000139-A549

A549

Rela

tive e

xpre

ssio

n of

ci

rc_M

DM

2_00

0139

NC XAV939

∗∗

0.0

0.5

1.0

1.5

(a)

circ_MDM2_000139-HCC-827

HCC-827

Rela

tive e

xpre

ssio

n of

ci

rc_M

DM

2_00

0139

NC XAV9390.0

0.5

1.0

1.5

(b)

circ_ATF2_001418-A549

A549

Rela

tive e

xpre

ssio

n of

ci

rc_A

TF2_

0014

18

NC XAV939

∗∗

0.0

0.5

1.0

1.5

(c)circ_ATF2_001418-HCC-827

HCC-827

Rela

tive e

xpre

ssio

n of

ci

rc_A

TF2_

0014

18

NC XAV9390.0

0.5

1.0

1.5

∗∗

(d)

circ_DICER1_000834-A549

A549

Rela

tive e

xpre

ssio

n of

ci

rc_D

ICER

1_00

0834

NC XAV939

∗∗∗

0.0

0.5

1.0

1.5

(e)

circ_DICER1_000834-HCC-827

HCC-827

Rela

tive e

xpre

ssio

n of

ci

rc_D

ICER

1_00

0834

NC XAV939

0.0

0.5

1.0

1.5

(f )

Figure 6: Continued.

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indicated that the DICER1 in the pathogenesis of NSCLCand circ_DICER1_000834 might play an important duringthe XAV939 treatment for NSCLC.

In conclusion, 106 DE-circRNAs between the treatmentand control groups were identified. Besides, let-7 familymembers and POLR2A targeting circ_MDM2_000139, miR-16-5p/miR-134-5p targeting circ_ATF2_001418, miR-133btargeting circ_BIRC6_001271, and miR-221-3p/miR-222-3ptargeting circ_CDC25C_002079 might be involved in thefunction during the treatment of NSCLC by XAV939.However, the roles of these RNAs and regulatory re-lationships in treatment of NSCLC by XAV939 needed to befurther confirmed by experimental research studies.

Data Availability

-e data used to support the findings of this study areavailable from the corresponding author upon request.

Additional Points

Highlights. (1) -ere were 106 differentially expressedcircRNAs between the XAV939-treated NSCLC cells andcontrol. (2) ATF2 was enriched in the TNF signalingpathway. (3) Circ_MDM2_000139, circ_ATF2_001418,circ_CDC25C_002079, and circ_BIRC6_001271 were keycircRNAs in XAV939-treated NSCLC cells.

Conflicts of Interest

-e authors declare that they have no conflicts of interest.

Acknowledgments

-is research was supported by-e Study of the CorrelationBetween ROCK1 and theMetastasis of Lung Cancer (Projectnumber: SCZSY201728).

Supplementary Materials

-e relative expression levels of key DE-circRNAs weredetected by the real-time reverse transcription polymerasechain reaction (RT-PCR). -e primer information is shownin the Supplementary Table 1. (Supplementary Materials)

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ci

rc_C

DC2

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∗∗

0.0

0.5

1.0

1.5

XAV939NC

A549

(k)

circ_CDC25C_002079-HCC-827

Rela

tive e

xpre

ssio

n of

ci

rc_C

DC2

5C_0

0207

9

0.0

0.5

1.0

1.5

XAV939NC

HCC-827

(l)

Figure 6: -e relative expressions of key DE-circRNAs in the A549 and HCC-827 cells. NC represents the control cells. XAV939 representsthe A549 and HCC-827 cells treated by 10 μM XAV939. ∗P< 0.05 indicates a significant difference compared with that of the NC group;∗∗P< 0.01 indicates a very significant difference compared with that of the NC group; ∗∗∗P< 0.001.

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