Dong et al. J Transl Med (2021) 19:226 https://doi.org/10.1186/s12967-021-02903-5 Journal of Translational Medicine
RESEARCH Open Access Identifcation of circRNA–miRNA–mRNA networks contributes to explore underlying pathogenesis and therapy strategy of gastric cancer Zhijie Dong†, Zhaoyu Liu†, Min Liang†, Jinhui Pan, Mingzhen Lin, Hai Lin, Yuanwei Luo, Xinke Zhou* and Wenxia Yao*
Abstract Background: Circular RNAs (circRNAs) are a new class of noncoding RNAs that have gained increased attention in human tumor research. However, the identifcation and function of circRNAs are largely unknown in the context of gastric cancer (GC). This study aims to identify novel circRNAs and determine their action networks in GC. Methods: A comprehensive strategy of data mining, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and computational biology were conducted to discover novel circRNAs and to explore their potential mechanisms in GC. Promising therapeutic drugs for GC were determined by connectivity map (CMap) analysis. Results: Six overlapped diferentially expressed circRNAs (DECs) were screened from selected microarray and RNA-Seq datasets of GC, and the six DECs were then validated by sanger sequencing and RNase R treatment. Sub- sequent RT-qPCR analysis of GC samples confrmed decreased expressions of the six DECs (hsa_circ_0000390, hsa_circ_0000615, hsa_circ_0001438, hsa_circ_0002190, hsa_circ_0002449 and hsa_circ_0003120), all of which accumulated preferentially in the cytoplasm. MiRNA binding sites and AGO2 occupation of the six circRNAs were predicted using online databases, and circRNA–miRNA interactions including the six circRNAs and 33 miRNAs were determined. Then, 5320 target genes of the above 33 miRNAs and 1492 diferently expressed genes (DEGs) from The Cancer Genome Atlas (TCGA) database were identifed. After intersecting the miRNA target genes and the 889 downregulated DEGs, 320 overlapped target genes were acquired. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that these target genes were related to two critical tumor-associated signaling path- ways. A protein–protein interaction network with the 320 target genes was constructed using STRING, and ffteen hubgenes (ATF3, BTG2, DUSP1, EGR1, FGF2, FOSB, GNAO1, GNAI1, GNAZ, GNG7, ITPR1, ITPKB, JUND, NR4A3, PRKCB) in the network were identifed. Finally, bioactive chemicals (including vorinostat, trichostatin A and astemizole) based on the ffteen hubgenes were identifed as therapeutic agents for GC through the CMap analysis. Conclusions: This study provides a novel insight for further exploration of the pathogenesis and therapy of GC from the circRNA-miRNA-mRNA network perspective.
*Correspondence: [email protected]; [email protected] †Zhijie Dong, Zhaoyu Liu, and Min Liang are co-frst authors Guangzhou Key Laboratory of Enhanced Recovery after Abdominal Surgery, The Fifth Afliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China
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Keywords: Gastric cancer, circRNA, ceRNA, Cmap
Background In the current study, data mining and bioinformatics Circular RNAs (circRNAs) are a newly identifed class of analysis were combined to identify novel circRNAs and noncoding RNAs (ncRNAs) that have recently elicited to investigate the underlying mechanism of circRNA- increased attention [1, 2]. CircRNAs are formed by back- miRNA-mRNA network in GC (Additional fle 2: Figure splicing of a downstream splice donor site to an upstream S1). First, we collected GC-related circRNA microar- splice acceptor site, thus producing a covalently closed ray and RNA-Seq datasets from the Gene Expression RNA molecule. Lacking 5′ caps and 3′ poly (A) tails, cir- Omnibus (GEO) databases and PubMed publications and cRNAs have not received signifcant attention for a long screened overlapped diferentially expressed circRNAs time; recently, with the application of high-throughput (DECs). Ten we validated these overlapped DECs by sequencing, a growing number of circRNAs have been sanger sequencing and RNase R treatment, and verifed unveiled [3]. Studies on circRNAs have revealed that they their expression via reverse transcription-quantitative are structurally stable, presumably because their lack of polymerase chain reaction (RT-qPCR) using GC sam- free ends is resistant to exonuclease activity, which ena- ples. Further subcellular localization analysis and AGO2- bles circRNAs to serve as a new class of diagnostic or binding sites mining indicated that these DECs function prognostic biomarkers of diseases. Furthermore, emerg- as ceRNAs. After predicting the sponge miRNAs of the ing lines of studies have revealed that some circRNAs DECs and miRNA target genes, we constructed circRNA- play important roles in physiological and pathological miRNA-mRNA networks; Kyoto Encyclopedia of Genes conditions including malignant tumors, and they may and Genomes (KEGG) enrichment analysis on the target provide new potential therapeutic targets [4]. genes were conducted to investigate the potential patho- Gastric cancer (GC) is the ffth most common can- genesis of GC. Ten, a protein–protein interaction (PPI) cer and the fourth-leading cause of cancer-related network with target genes was set up and hubgenes were deaths worldwide [5] and is thus a global cancer burden. determined. Moreover, we performed a connectivity Although diagnosis and treatment have improved over map (CMap) analysis based on the hubgenes to identify the last decades, the prognosis remains poor and the potential bioactive compounds, which provides possible 5-year survival rate remains low in patients with GC [6]. alternatives and chemotherapeutics for the treatment of Terefore, the discovery of efective biomarkers and ther- GC patients. apeutic targets is of great importance. Competing endog- enous RNA (ceRNA) refers to RNAs that sequester or Methods sponge miRNAs to regulate mRNA transcripts contain- Screening of DECs in GC from GEO database and PubMed ing common miRNA recognition elements (MREs) [7]. publications Recent studies reported that a large amount of conserved Te microarray and RNA-Seq data was obtained from MREs present in circRNAs [8], making circRNAs a new GEO databases (http://www.ncbi.nlm.nih.gov/gds/) [12] research hotspot in the feld of ceRNA. CircRNAs have and PubMed publications. Tree published microar- been found to participate in various biological processes ray datasets that analyzed circRNA expression profle in GC by working as ceRNAs [9–11]. However, the iden- between GC tissues and matched nontumor tissues were tifcation and function of circRNAs in GC still require retrieved from GEO database until June 2019 (Table 1), further investigation. and the microarray dataset reported by Dang et al. was
Table 1 Basic information of microarray and RNA-Seq datasets Data source Platform Authors Publication Year Sample size circRNA (T/N) Number
Microarray datasets from GEO GSE100170 GPL23259 Dang et al Sci Rep 2017 5/5 62,998 GSE89143 GPL19978 Shao et al Cancer Med 2017 3/3 5,396 GSE78092 GPL21485 Huang et al Mol Med Rep 2017 3/3 13,617 RNA-Seq datasets from PubMed publications PMID: 27,986,464 GPL11154 Chen et al Cancer Lett 2017 3/3 15,623
GEO Gene Expression Omnibus, T tumor, N normal Dong et al. J Transl Med (2021) 19:226 Page 3 of 18
selected for further analysis since it has the largest num- RT‑qPCR ber of identifed circRNAs among the three datasets [13]. RNA was reversely transcribed into cDNA using Pri- And the RNA-Seq dataset reported by Chen et al. [11] meScript™ RT reagent Kit with gDNA Eraser (TaKaRa, was selected for the same reason that it has the largest RR047A) in the presence of random hexamers (TaKaRa). number of identifed circRNAs among RNA-Seq datasets qPCR was conducted using a TB Green® Premix Ex Taq™ until June 2019. Limma [14], a Bioconductor package, II Kit (TaKaRa, RR820A) on the Applied Biosystems 7500 was used to screen DECs, and “|Log2 (fold change) |> 1 Real Time PCR System. β-actin served as an internal and P-value < 0.05” were defned as the criteria for the control. Te specifc primers of circRNAs were listed in DECs screening. Te overlapped DECs were identifed Table 2. by intersecting the DECs from the aforementioned two datasets. Prediction of MREs MiRNA binding sites in DECs were predicted using web Cell culture and tissue specimens tools CircInteractome (https://circi ntera ctome. nia. nih. GES-1-T cells were constructed in the previous study gov/) [16] and Circbank (www.circb ank. cn ) [17] respec- [15], and were cultured in Dulbecco′s modifed Eagle′s tively. Only MREs predicted consistently by both Circbank medium (DMEM) with 10% fetal bovine serum (FBS) and and CircInteractome were used for further analysis. 1% penicillin–streptomycin. Human GC cell lines HGC- 27 and MGC-803 were cultured in RPMI 1640 medium AGO2‑binding sites from cross‑linking with 10% FBS and 1% penicillin–streptomycin. All cells immunoprecipitation (CLIP) data sets were incubated in a humidifed atmosphere contain- Te evidence for AGO2-binding sites was acquired from ing 5% CO2 at 37 °C. Eight pairs of fresh frozen GC tis- published online CLIP data sets on doRiNA database sues and corresponding adjacent normal tissues were (http://dorina. mdc- berlin. de ) [18]. Tese data sets contain obtained from patients who underwent surgery at the AGO2 PAR-CLIP or HITS-CLIP data from several cell Fifth Afliated Hospital of Guangzhou Medical Univer- lines. After downloading the available data sets, AGO2- sity. All samples used in this study were collected with binding sites of circRNA genomic region were acquired. patients’ consent. Te present study was approved by the Ethics Committee of the Fifth Afliated Hospital of Prediction of miRNA target genes Guangzhou Medical University. MiRNAs targeted genes were predicted using miR- Walk 2.0 [19], which involves 12 algorithms (miRWalk, RNA isolation, nuclear‑cytoplasmic fractionation Microt4, miRanda, mirbridge, miRDB, miRMap, miR- and RNase R treatment NAMap, Pictar2, PITA, RNA22, RNAhybrid and Targets- Total RNA was extracted from cells and tissues using can). Target genes identifed by at least eight algorithms TRIzol (Invitrogen) according to the manufacturer’s were chosen. instructions. Nuclear and cytoplasmic RNAs were iso- lated with PARIS Kit (Invitrogen, AM1921) following the Collection of diferently expressed genes (DEGs) of GC manufacturer’s instruction. For RNase R treatment, total from The Cancer Genome Atlas (TCGA) 10 ug RNA was incubated for 20 min at 37 °C with or RNA-Seq data of 415 gastric adenocarcinoma samples without 2 units of RNase R (Epicentre, RNR07250); the and 35 normal tissues was downloaded from the TCGA resulting RNA was then purifed with an RNeasy Min- database. DEGs were determined by DEseq [20], a Bio- Elute Cleanup Kit (Qiagen, 74,204). conductor package; with the cutof of gene reads > 1, total
Table 2 Primer sequences for reverse transcription-quantitative polymerase chain reaction Gene ID Primer sequence Product length
Forward (5′-3′) Reverse (5′-3′) hsa_circ_0000390 GCACAAGAACGCTACCTTTTCTTA TTTTTTAGCATCATTCCAGTCCAG 165 hsa_circ_0000615 GAAAGTCAAGTCTGAAAAGCAATG ACATCTTAGAGTCAACGTCCCAC 150 hsa_circ_0001438 GTAATCAACGTAAGAGAGAATTCGG TGGCTGAGGACGCTCTGAA 157 hsa_circ_0002190 TCAATGGCTCCCTTTATGTCTT CCATGCAGCACAACTCTCTTCT 149 hsa_circ_0002449 CCTTATCCACCACAACCAATG ATCGGGGACCTGTAAAGTGC 152 hsa_circ_0003012 CATCCAGATTATCCACTGCAATC TTCGGGAACCACAGCAGAT 146 Dong et al. J Transl Med (2021) 19:226 Page 4 of 18
A Chen et al.CDang et al. hen et al.Dang et al.
82 0 191 92 6 516 30% 0% 70% 15% 1% 81%
Upregulated circRNAs Downregulated circRNAs
B 4 1.04 1.92 0.00 0.15 0.21 0.27 hsa_circ_0000390
2 2.93 2.62 3.41 3.12 0.85 0.67 hsa_circ_0000615
0.69 0.52 0.80 0.00 0.00 0.13 hsa_circ_0001438 0 1.21 1.22 0.40 0.00 0.00 0.27 hsa_circ_0002190
0.35 2.97 0.40 0.45 0.00 0.00 hsa_circ_0002449
0.17 1.40 0.40 0.00 0.00 0.00 hsa_circ_0003012 N1 N2 N3 GC1 GC2 GC3 C 5 3.26 3.30 2.88 2.95 3.30 1.50 2.74 2.73 2.16 2.50 hsa_circ_0000390
3 2.98 2.99 2.97 2.40 2.84 1.54 1.99 2.89 1.70 2.62 hsa_circ_0000615
3.13 3.17 3.14 2.55 2.95 1.52 2.81 2.99 1.66 2.41 hsa_circ_0001438 1 3.00 3.02 2.96 2.74 2.81 2.37 2.70 2.76 2.66 2.60 hsa_circ_0002190
3.46 3.56 3.32 3.22 3.34 2.93 3.28 3.09 3.02 2.98 hsa_circ_0002449
3.24 3.24 3.15 2.96 3.18 2.42 2.60 2.72 3.03 2.70 hsa_circ_0003012
N1 N2 N3 N4 N5 GC1 GC2 GC3 GC4 GC5 Fig. 1 Identifcation of diferentially expressed circRNAs (DECs) in gastric cancer (GC) using microarray and RNA-Seq datasets. A Venn diagram showing overlapped circRNAs that were upregulated or downregulated in studies performed by Chen et al. and Dang et al., respectively. B, C Heatmap for the six DECs in individual microarray or RNA-Seq datasets. Red indicates a higher expression and blue indicates a lower expression
12,408 genes were inclusive in all samples, and 1492 genes networks were constructed and visualized using the were diferentially expressed with the criteria of |log2 (fold Cytoscape3.6.1 software [21]. change) |> 1 and P-value < 0.05. Among the 1492 genes, 603 genes were upregulated and 889 genes were downregulated. Establishment of a PPI network and identifcation of hubgenes Construction of circRNA–miRNA–mRNA networks Te PPI network directly indicate the protein interac- Te predicted miRNA target genes and the downregu- tions established using STRING (version 11.0) [22]. lated DEGs from TCGA were intersected to obtain the Seven active interaction sources were selected: textmin- overlapped genes. Ten, circRNA–miRNA–mRNA ing, experiment, database, co-expression, neighborhood, Dong et al. J Transl Med (2021) 19:226 Page 5 of 18
gene fusion, and co-occurrence. Confdent score was set Cell counting Kit‑8 (CCK‑8) assay to medium (score 0.400). Te results were visualized by Cell proliferation was assayed by CCK-8 (Dojindo, the Cytoscape3.6.1 software. Next, the “Molecular Com- Tokyo) according to the manufacturer’s protocols. GC plex Detection” (MCODE), a plugin of Cytoscape, was cells in logarithmic growth were plated in each well of a employed to fnd the highly interacted hubgene clusters. 96-well plate. Ten, on the indicated time, 10 μl of CCK-8 solution was added to each well. Following 1 h of incuba- Bioinformatic analysis tools tion at 37 °C, the absorbance of each well was measured Te KEGG Pathway based data integration and visualiza- at 450 nM by Synergy 2 microplate reader (BioTek, Win- tion was carried out using Pathview Web server (https:// ooski, VT, USA). pathview.uncc.edu/) [23]: expression data of 320 tar- get genes in ENSEMBL ID type of GC tissues (as sam- Results ple) and normal tissues (as control) were loaded into Identifcation of six downregulated DECs in GC the Pathview input tool, and the output sufx was set as First, we collected GC-related circRNA microarray and KEGG pathview. Venn diagrams were generated using RNA-Seq datasets from GEO databases and PubMed an online analysis platform (http://bioinfogp.cnb.csic.es/ publications and screened overlapped DECs. As dis- tools/venny/index.html). played in Table 1, one microarray dataset from GEO databases (GSE100170) [13] and one RNA-Seq dataset CMap analysis from PubMed publications (PMID: 27986464) [11] were CMap (https://portals.broadinstitute.org/CMap/) [24, selected for analysis in this study since these two datasets 25] is a collection of gene expression profles from cul- had the largest numbers of identifed circRNAs respec- tured human cells treated with bioactive small molecules; tively. A total of 180 DECs were identifed in the study CMap helps scientists to discover functional connec- performed by Chen et al. [11], including 82 upregulated tions among diseases, genetic perturbation, and drug circRNAs and 98 downregulated circRNAs (Fig. 1A); a action. Downregulated tags of the ffteen hubgenes were total of 713 DECs were identifed in the study by Dang loaded into the CMap online tool, searching against 6100 et al. [13], of which 191 circRNAs were upregulated and treatment-controls (instances) in which 1309 bioactive 522 circRNAs were downregulated (Fig. 1A). molecules were involved. A connectivity score ranging Further Venn diagram analysis showed that there were from − 1 to 1 was used to estimate the closeness between six overlapped downregulated circRNAs and none over- query signatures and compounds: a positive score indi- lapped upregulated circRNAs in both studies (Fig. 1A). cates a promotive efect of compound on the query signa- Te six overlapped downregulated circRNAs were hsa_ tures, whereas a negative score denotes an inverse efect circ_0000390, hsa_circ_0000615, hsa_circ_0001438, hsa_ of compound on the query signatures. circ_0002190, hsa_circ_0002449 and hsa_circ_0003120. Te expression of the six DECs in individual microarray CircRNA overexpression and cell transfection or RNA-Seq dataset was displayed: in the study by Chen CircRNA overexpression plasmids were cloned with the et al. (Fig. 1B), six DECs was signifcantly lower with good pCD2.1-ciR vector, and all the circRNA sequence was consistency in GC tissues comparing to the matched confrmed by Sanger sequencing. For transient transfec- nontumor tissues; downregulation of the six DECs in tion, GC cells were transfected with the reagents using GC tissues was also observed in the study by Dang et al. Lipofectamine™ 3000 (Invitrogen) according to the man- (Fig. 1C). ufacturer’s instructions.
Table 3 Essential characteristics of the six diferently expressed circRNAs CircRNA Type Chr Start End Strand Spliced length Gene symbol hsa_circ_0000390 Exonic chr12 32760890 32,764,217 345 FGD4 + hsa_circ_0000615 Exonic chr15 64,791491 64,792,365 874 ZNF609 + hsa_circ_0001438 Exonic chr4 128,995614 128999117 294 LARP1B + hsa_circ_0002190 Exonic chr7 129760588 129762042 304 KLHDC10 + hsa_circ_0002449 Exonic chr5 139574030 139574237 207 C5orf32 + hsa_circ_0003012 Exonic chr12 105303432 105322472 830 SLC41A2 − Dong et al. J Transl Med (2021) 19:226 Page 6 of 18
A
hsa_circ_0000390 hsa_circ_0000615 hsa_circ_0001438
hsa_circ_0002190 hsa_circ_0002449 hsa_circ_0003012
B 22 RNaseR (-) RNaseR (+)
20
2-2
2-4
2-6 8 9 H s D P GA CDR1a
hsa_circ_000039hsa_circ_00006150 hsa_circ_000143hsa_circ_0002190hsa_circ_000244hsa_circ_0003012 Fig. 2 Validation of the six DECs by Sanger sequencing and RNase R treatment. A Head-to-tail splicing in the RT-qPCR product of the six DECs by Sanger sequencing. Blue arrow indicates the back-splicing of DECs. B qRT-PCR analysis of the abundance of the six DECs in MGC-803 cells after treatment with RNase R. The amounts were normalized to the value measured in the untreated group Dong et al. J Transl Med (2021) 19:226 Page 7 of 18
ABhsa_circ_0000390 hsa_circ_0000615 1.6 ANT 1.6 ANT ve l ) )
evel 1.4 1.4 GC le GC NT NT nl 1.2 1.2 on o i oA oA
1.0 ss 1.0 essi dt dt 0.8 re 0.8 p ze ze i i l l ex expr 0.6 a 0.6 e m ma ve 0.4 iv 0.4 or or t ti a N l la ( (N 0.2 0.2 Re Re 0.0 0.0 # # # # # 1# 2# 3# 4 5# 6# 7# 8 1 2# 3 4# 5# 6 7# 8#
CDhsa_circ_0001438 hsa_circ_0002190 1.6 ANT 1.6 ANT vel )
1.4 eve l 1.4 T) le GC l GC NT
AN 1.2 1.2 on on oA to
ssi 1.0 1.0 e essi d dt
e 0.8 0.8 iz li ze expr expr al 0.6 0.6 e ma rm ve 0.4 v 0.4 or ti ti a No la l ( 0.2 (N 0.2 Re 0.0 Re 0.0 # # # # # # 1# 2# 3 4 5# 6# 7# 8# 1# 2 3# 4# 5 6# 7 8
EFhsa_circ_0002449 hsa_circ_0003012 1.6 ANT 1.6 ANT ve l )
1.4 evel 1.4 T) le GC GC NT nl
AN 1.2 1.2 o on oA to
1.0 ss i 1.0 e essi dt
ed 0.8 0.8 pr ze i iz l ex expr al 0.6 0.6 ma m ve ve 0.4 0.4 or or ti ti N la la ( (N 0.2 0.2 Re Re 0.0 0.0 # # # 1# 2# 3 4 5# 6# 7# 8# 1# 2 3# 4# 5# 6# 7# 8# Fig. 3 Expression of the six DECs in GC by RT-qPCR analysis: A hsa_circ_0000390, B hsa_circ_0000615, C hsa_circ_0001438, D hsa_circ_0002190, E hsa_circ_0002449, and F hsa_circ_0003012. The amounts of circRNAs were normalized to the value measured in adjacent normal tissues. ANT, adjacent normal tissues; GC, gastric cancer
Validation of the six DECs and verifcation of their was apparently decreased (Fig. 2B). Tese results con- downregulation with GC samples frmed the circular characteristics of the six DECs. We then analyzed the six DECs. Te principal charac- Ten, RT-qPCR analysis was performed with 8 pairs teristics of the six DECs were listed in Table 3. Divergent of GC samples and adjacent nontumor tissues to verify primers (Table 2) were designed against the six DECs. By the induced expression of the six DECs. As shown in sanger sequencing of the RT-PCR product, head-to-tail Fig. 3A–F, expression of the six DECs were dramati- splicing in the six DECs was confrmed (Fig. 2A). More- cally downregulated in GC tissues than adjacent normal over, after the treatment of RNase R, a processive 3′ to tissues. 5′ exonuclease, the six DECs was apparently enriched, which was consistent with the positive control CDR1as [26]; however, the abundance of linear GAPDH mRNA Dong et al. J Transl Med (2021) 19:226 Page 8 of 18
A 24
23
on 22 essi io 1 pr
at 2 Ex
NR 20 ve C/ 0 5 2 ti
9 1
NA DH
6 190 01 la -1 P 0 2 3
2 nR
Re 001438 00 GA
00003 000 0 0002449 1 s
_000 _0 -2 U c_ c_ c c_
2 r r r rc irc_ i
c ci
_cir _ -3 a a 2 sa_c hsa_ci hsa_ hsa_ci hs hs h
B
FGD4 ZNF609 LARP1B
hsa_circ_0000390 hsa_circ_0000615 hsa_circ_0001438
KLHDC10 C5orf32 SLC41A2
hsa_circ_0002190 hsa_circ_0002449 hsa_circ_0003012
CircInteractome circBank (miRanda) circBank (targetscan) Ago2-binding sites Fig. 4 Subcellular distribution of the six DECs and identifcation of circRNA-miRNA interactions and AGO2 occupation. A qRT-PCR analysis of subcellular distribution of the six DECs. The amounts of circRNAs were normalized to the value measured in the nucleus. C/N ratio, the ratio of cytoplasm to nucleus. B Structural patterns of the six DECs with predicted MREs and AGO2-binding sites. Red dots represent MREs predicted by CircInteractome while blue dots and green dots represent MREs predicted by Circbank using miRanda and Targetscan, respectively. Black triangles represent AGO2-binding sites Dong et al. J Transl Med (2021) 19:226 Page 9 of 18
AB
Downregulated DEGs miRNA Targets 889 603 889 5320
569 320 5000 9.7% 5.4% 84.9% -Log10 (Adjust P-value) 0 5 10 15 20 25
-6 -4 -2 0 2 4 6 Log2 (Fold Change) Fig. 5 Identifcation of 320 overlapped genes that probably play critical roles in GC. A Volcano plot of the diferentially expressed genes (DEGs) in GC based on the data from The Cancer Genome Atlas (TCGA). Signifcantly upregulated circRNAs are indicated in red and downregulated circRNAs are indicated in blue (FC 2.0 or FC 0.5 and p < 0.05). B Venn diagram for the intersection between downregulated DEGs and miRNA target genes ≥ ≤
Identifcation of circRNA‑miRNA interactions Taken together, the six DECs possibly function by and AGO2‑binding sites sponging miRNAs. We subsequently determined the potential function of the six DECs in GC. Since the function of circRNAs was associated with their subcellular localization, RT- Construction of circRNA‑miRNA‑mRNA networks and KEGG qPCR analysis of nuclear or cytoplasmic fractions of GC pathway analysis of the target genes cell lines was performed to evaluate the subcellular dis- To further illustrate the potential ceRNA mechanisms of tribution of the six DECs. As expected, U1 snRNA was the six DECs in GC, prediction of miRNA target genes enriched in the nuclear while the coding GAPDH mRNA was performed by miRWalk with the criteria of that tar- was primarily localized in cytoplasm; as demonstrated, get genes identifed by at least 8 out of 12 algorithms all the six DECs were found predominantly in the cyto- were chosen for further analysis, and a total of 5320 plasm (Fig. 4A). target genes of the above-mentioned 33 miRNAs were Given that increasing circRNAs have been reported to obtained. In addition, 1492 DEGs in GC were obtained play important roles in tumor through absorbing miRNA from TCGA (Additional fle 1), of which 603 DEGs and that the six DECs accumulated preferentially in the were upregulated and 889 DEGs were downregulated cytoplasm, three online databases were used to deter- (Fig. 5A). After intersecting the 5320 miRNA target genes mine whether the six DECs function as ceRNAs in GC. and the 889 downregulated DEGs from TCGA, 320 over- Herein CircInteractome and Circbank were utilized to lapped target genes were acquired (Fig. 5B), which prob- predict potential target miRNAs of the six DECs, and ably serve crucial roles in GC. only circRNA–miRNA interactions identifed by both Ten, circRNA-miRNA-mRNA networks were gener- Circbank and CircInteractome were used (Fig. 4B), which ated using a combination of data from circRNA-miRNA are also shown in Additional fle 2: Table S1. A total of 36 interactions and the overlapped 320 target genes, which circRNA-miRNA interactions consisting of 6 circRNAs indicated an overall perspective of regulation networks and 33 miRNAs (3 overlapped miRNAs) were identifed. consisting of six circRNAs, 33 miRNAs and 320 tar- Moreover, online AGO2 PAR-CLIP or HITS-CLIP data get mRNAs. Te ceRNA networks with six DECs were was downloaded from doRiNA database [18] to ascertain shown in Additional fle 2: Figure S2–3. whether AGO2 occupies in the region of the six DECs. Next, KEGG pathway enrichment analysis were per- Te result demonstrated that all the six DECs contain formed using pathview [23] to illustrate the functional AGO2 binding sites and 4 out of 6 circRNAs contain at annotations of the 320 target genes. As displayed in least two AGO2 binding sites (Fig. 4B). Fig. 6, the top two enriched pathways were “MAPK sign- aling pathway” and “PI3K-AKT signaling pathway”, both of which were tumor-related pathways [27–30]. Dong et al. J Transl Med (2021) 19:226 Page 10 of 18
Fig. 6 Pathway analysis and visualization of 320 target genes. The top two enriched pathways: A MAPK signaling pathway; B PI3K-AKT signaling pathway Dong et al. J Transl Med (2021) 19:226 Page 11 of 18
A TEF PER1 CSRNP1 HLF RORA LIFR MAF PDCD4 BTG2 PRDM1 CRY2 NRP2 ELL2 JUND SIK1 ATF3 JDP2 F13A1 BMP6 IRF4 KAT2B KLF10 TMEM47 FOSB EPHA4 DUSP1 TSC22D3 USP2 LNX1 KLF2 SOCS3 ZEB1 FOS EGR1 NTN1 BTBD11 NR3C2 CDKN2B FBXO32 CLU JUN ABCG2 SFRP1 HPGD EGR3 GABARAPL1 KLF4 FBXL7 TIMP3 ME1 ADAMTS1 FYCO1 FGF2 CAMK2G MALL PDK4 TIAM1 ITPKB DPP4 MAP1B CAV1 AQP3 P2RY1 SLC2A4 RHOJ PGM5 KIT DLC1 STK32B RHOB FAM13A HK1 PRKCB LRRK2 MYLK MEIS1 SYNM DMD INPP5A SLC2A12 CXCL12 SVIL P2RY2 KCNMA1 BCAS1 RASL12 GNAZ MYH11 HEBP1 PPP1R12B CALD1 LPP TMOD2 PRKG1 ITPR1 PALLD PDE5A PRKAR2B IL6R GNG7 TMOD1 MSRB3 GNAO1 CFL2 VAMP2 SNTA1 GNAI1 LMOD1 FLNC SYNPO2 PDE2AATP2B4 SPEG PTGFR PDE1B CACNB2 PPP2R3A LRRC4 CLTB HSPB8 PDLIM4 BVES AKAP6 KCNE4 FOXF2
B GNAZ GNAO1 EGR1 BTG2 FGF2 GNG7 PRKCB NR4A3 FOSB ITPKB DUSP1 GNAI1 ITPR1 ATF3 JUND Fig. 7 Construction of a protein–protein interaction (PPI) network with 320 target mRNAs and identifcation of hubgenes from the PPI network. A The PPI network with 320 target genes. B The subnetwork with 15 hubgenes that extracted from the PPI network with the “Molecular Complex Detection (MCODE)” algorithm. A, B The node color changes gradually from light blue to dark blue in ascending order according to the fold change of target genes; the thickness of line between nodes indicate the interacting proteins varies based on the combined score for the protein association Dong et al. J Transl Med (2021) 19:226 Page 12 of 18
Identifcation of ffteen hubgenes from the PPI network could disturb the gene expression pattern of the ffteen with MCODE and validation of their downregulation in GC hubgenes. Downregulated tags of the ffteen hubgenes and their regulation by circRNAs were submitted to the CMap online tool for analysis. Ten, a PPI network was built to analyze protein interac- After the signature query, three chemicals, vorinostat, tions with the 320 target genes. After removing uncon- astemizole, and trichostatin A (TSA), with the most sig- nected nodes and nodes with connecting frequency nifcant negative enrichment score were identifed as the smaller than 5, 125 nodes and 305 edges were mapped potential therapeutic compounds for GC (Table 4). Te in the PPI network (Fig. 7A). Considering the key role chemical structures of the three drugs were shown in of hubgenes in biological process, MCODE, a plugin of Fig. 9A–C. Te instances for each compound according Cytoscape, was utilized to screen hubgenes from the PPI to the permuted results of CMap analysis were listed in network. Using the criteria of that the k-core is 2, nine Additional fle 2: Table S2; as shown, all the three com- subnetworks were identifed, and one subnetwork with pounds with diferent doses or in diferent cell lines highest MCODE score 5.571 that contains 15 nodes and consistently exhibited negative correlations to the query 39 edges was selected (Fig. 7B). Tere were ffteen genes signature. (ATF3, BTG2, DUSP1, EGR1, FGF2, FOSB, GNAI1, In addition, we validated whether the three bioactive GNAO1, GNAZ, GNG7, ITPKB, ITPR1, JUND, NR4A3, drugs can alleviate GC progression and can modulate and PRKCB) in this subnetwork, and these ffteen genes expression of hubgenes. Our results showed that treat- were identifed as hubgenes. ment of GC cells with increasing concentrations of all Te expression levels of the ffteen genes in GC from the three drugs markedly reduced GC cell proliferation TCGA were demonstrated in Fig. 8A–O, and results by CCK-8 assays in a dose-dependent manner (Fig. 9D, F showed that all these ffteen hubgenes was downregu- and H), and these drugs treatments further upregulated lated in GC tissues compared with normal tissues. Ten, the mRNA levels of almost all 15 hubgenes by qRT-PCR RT-qPCR analysis was performed with the above-men- analysis (Fig. 9E, G and I). Tese results indicate that the tioned 8 pairs of GC samples and adjacent nontumor tis- three drugs predicted by CMap analysis could be used as sues to verify the induced expression of these hubgenes; therapeutic agents for GC. the results showed that expression of the ffteen hub- genes were dramatically decreased in GC tissues than Discussion adjacent normal tissues (Additional fle 2: Figure S4A–D). CircRNAs that were frst discovered in eukaryotic cells In addition, to determine the efect of circRNAs on the in 1979 [31] has been recently re-recognized and elic- expression of hubgenes with wet lab experiments, overex- ited attention. Unlike the formation of traditional linear pressing plasmids of the six circRNAs were constructed RNA, circRNAs display a unique covalently closed circu- and GC cells were transfected with pooled circRNA vec- lar form, which is responsible for their high stability and tors. Te respective increase of circRNA expressions resistance to exonucleases. In addition, previous studies induced by overexpressing plasmids was confrmed have proved their sequence conservation, abundant pres- by qRT-PCR analysis (Fig. 8P); meanwhile, signifcant ence in exosomes and plasma, and spatial and cell-type increases of almost all 15 hubgenes (except four of them) specifcity [32]. Tereby, circRNAs have great potential in mRNA level was observed (Fig. 8Q). Collectively, these to serve as a biomarker for early diagnosis and progno- results indicate that the hubgenes are modulated by sis of many human diseases including cancers. With the circRNAs. development of high-throughput sequencing and bioin- formatics analysis, mounting circRNAs were found and Identifcation of three therapeutic drugs for GC based confrmed to be involved in the regulation of diversifed on CMap analysis and validation of their efect on GC cell biological processes in diferent cancer types [33, 34]. In proliferation and hubgenes expressions GC, some circRNAs, such as circHECTD1 [35] and circ- CMap can be used to predict compounds that may FAT1 [36], have been reported to be involved in GC cell induce or reverse a given gene expression signature, and proliferation, migration and invasion, and thereby exert we employed CMap to fnd potential compounds that their tumor-promoting or tumor-suppressing efects.
(See fgure on next page.) Fig. 8 Box-scatter plots for the 15 hubgenes expression in GC from TCGA and the efect of circRNAs overexpressions on hubgenes expressions. A–O Box scatter plots for the expression of 15 hubgenes in GC from TCGA: A ATF3, B BTG2, C DUSP1, D EGR1, E FGF2, F FOSB, G GNAI1, H GNAO1, I GNAZ, J GNG7, K ITPKB, L ITPR1, M JUND, N NR4A3, and OPRKCB. P–Q Overexpressing plasmids of the six circRNAs were constructed and GC cells were transfected with pooled circRNA vectors. P The overexpression efciency of circRNA vectors on RNA level of circRNAs were measured by qRT-PCR analysis. Q The mRNA levels of 15 hubgenes in GC cells transfected with circRNA vectors by qRT-PCR analysis Dong et al. J Transl Med (2021) 19:226 Page 13 of 18
ABATF3 (P = 3.07E-07) BTG2 (P = 1.93E-15) C DUSP1 (P = 7.61E-11) D EGR1 (P = 1.52E-07) 1.25E5
2.0E4 1.0E5 7.5E4 6.0E4 1.5E4 7.5E4 5.0E4 4.0E4 1.0E4 5.0E4
2.5E4 2.0E4 Relative expression 5.0E3 Relative expression Relative expression Relative expression 2.5E4
0 0 0 0 Normal Tumor Normal Tumor Normal Tumor Normal Tumor
E FGF2 (P = 4.28E-07) F FOSB (P = 2.70E-06) G GNAI1 (P = 8.50E-10) H GNAO1 (P = 3.19E-07)
3.0E3 2.0E4
2.0E3 1.5E4 6.0E3 2.0E3 1.5E3
1.0E4 4.0E3 1.0E3 1.0E3 2.0E3 Relative expression
Relative expression 5.0E3 Relative expression Relative expression 5.0E2
0 0 0 0 Normal Tumor Normal Tumor Normal Tumor Normal Tumor
I GNAZ (P = 6.35E-07) J GNG7 (P = 2.37E-27) K ITPKB (P = 2.21E-06) L ITPR1 (P = 1.19E-07)
2.0E3 1.5E4
1.5E3 1.0E3 4.0E3
1.0E4 1.0E3 5.0E2 2.0E3 5.0E3 5.0E2 Relative expression Relative expression Relative expression Relative expression
0 0 0 0 Normal Tumor Normal Tumor Normal Tumor Normal Tumor
M JUND (P = 2.37E-12) N NR4A3 (P = 1.53E-06) O PRKCB (P = 2.24E-05)
1.5E4 2.0E4 6.0E3
1.5E4 1.0E4 4.0E3
1.0E4
5.0E3 2.0E3 5.0E3 Relative expression Relative expression Relative expression
0 0 0 Normal Tumor Normal Tumor Normal Tumor
P Q Vector-Ctrl GES-1-T GES-1-T circRNA-Over l Vector-Ctrl *** l ve ) 64 circRNA-Over *** *** le rl 64 ve ) *** l *** le
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Re *** *** 5 0 Re 90 1 38 49 12 6 4 4 0 03 2 3 3 2 1 2 1 7 3 00 P1 F B I1 Z B D B 00 TF G R S O A G K N A C 000 00 0 T G G O NA A N N P c_ c_ _ A B US E F F N G G T ITPR1 JU R4 RK r r rc_0001rc_000219rc rc_000 D G I N P ci i i ci i i G _ _c _c _ _c _c as as as as as as h h h h h h Dong et al. J Transl Med (2021) 19:226 Page 14 of 18
Table 4 Three compounds identifed as treatment options for GC by CMap analysis CMap name Mean n Enrichment p Specifcity Percent non- null
Vorinostat 0.634 12 0.727 0 0.0708 100 − − Astemizole 0.621 5 0.766 0.00134 0.0495 100 − − Trichostatin A 0.443 182 0.404 0 0.3564 71 − −
However, the identifcation and function of circRNAs in DECs, and collectively, 36 circRNA-miRNA interactions GC still require further exploration. composed of 6 circRNAs and 33 miRNAs were identifed. In this study, we collected microarray and RNA- After intersecting the target genes of the aforemen- Seq datasets of GC from GEO databases and Pub- tioned 33 miRNAs and DEGs in GC from TCGA, 320 Med publications and screened six downregulated overlapped target genes were acquired to establish cir- circRNAs (hsa_circ_0000390, hsa_circ_0000615, hsa_ cRNA-miRNA-mRNA regulatory networks, providing circ_0001438, hsa_circ_0002190, hsa_circ_0002449 and an evidence of the ceRNA functional mechanism of the hsa_circ_0003120). Following sanger sequencing, RNase six DECs in GC. Te KEGG pathway analysis indicated R treatment and RT-qPCR analysis, the six DECs were that these target genes were related to two critical tumor- validated and their decreased expression in GC tissues associated signaling pathways, “MAPK signaling path- was confrmed. Previous studies have revealed that hsa_ way” [27, 28] and “PI3K-AKT signaling pathway” [29, circ_0000615 acts as miRNA sponge to exert diferent 30]. To comprehensively reveal potential relationships of roles in diferent solid tumors [37–40]; for example, hsa_ target genes, we developed a PPI network using the 320 circ_0000615 upregulates Sp1 expression by adsorbing target genes and extracted 15 hubgenes (ATF3, BTG2, miR-150-5p, and thereby promoting the proliferation and DUSP1, EGR1, FGF2, FOSB, GNAI1, GNAO1, GNAZ, metastasis of nasopharyngeal cancer cells [38]. However, GNG7, ITPKB, ITPR1, JUNID, NR4A3, PRKCB); and the until now studies about the efect of hsa_circ_0000615 diferential expressions of the 15 hubgenes were subse- and the other fve DECs on GC have been rarely reported. quently validated with GC tissues from TCGA and with Further studies are imperative to determine their poten- our GC samples. As previously demonstrated, some of tial roles in GC. the ffteen genes serve crucial roles in GC [45–47]. For As ceRNA, circRNA harboring MREs can sponge miR- example, Tang et al. have demonstrated that FOSB was NAs, which suppress the miRNA activity and result in signifcantly decreased in GC tissues, consistent with alteration of expression level of miRNA target genes [41, the results of this study, and moreover, downregulated 42]. Subcellular localization analysis and AGO2-bind- expression of FOSB was correlated with poor prognosis ing sites mining in this study indicate that the six DECs for GC patients [46]. in the cytoplasm probably sponge miRNAs. To further With the development of novel agents in recent determine whether the above six DEC act as ceRNAs in years, survival outcomes of GC patients have improved GC, their corresponding MREs were predicted with two [48]. However, the overall prognosis of patients with online tools, CircInteractome and Circbank. Te former advanced gastric cancer remains poor, and more efective web tool forecasts MREs via Targetscan algorithm, which drugs against GC are needed. Terefore, CMap analysis predicts MREs by surveying for 7-mer or 8-mer comple- of the ffteen hubgenes was performed to explore avail- mentarity to seed region and the 3′ end of each miRNA able compounds for the treatment of GC. Based on the [16, 43]. Te latter web tool predicts MREs based on two genome-wide expression profling of gene transcripts diferent algorithms including miRanda [44] and Targets- technology, CMap presents a data-driven and reli- can [43]. We selected miRNAs predicted by both CircIn- able approach for identifying new drugs or reposition- teractome and Circbank as target miRNAs of the six ing existing drugs [49]. Tree chemicals (vorinostat,
(See fgure on next page.) Fig. 9 Chemical structures of the three compounds identifed by the connectivity map (CMap) analysis and the efect of these three compounds on GC cell proliferation and hubgenes expressions. A–C Chemical structures of the three compounds: A Vorinostat, B Astemizole, and C Trichostatin A. D, F, H GC cells were treated with increasing concentrations of D Vorinostat, F Astemizole, and H Trichostatin A, and GC cell proliferations were measured by CCK-8 assays. E, G, I GC cells were treated with E Vorinostat, G Astemizole, and I Trichostatin A, and the mRNA levels of 15 hubgenes were measured by qRT-PCR analysis Dong et al. J Transl Med (2021) 19:226 Page 15 of 18
A B
Vorinostat
C Astemizole
Trichostatin A
Ctrl D E GES-1-T Vorinostat GES-1-T 150 l ve ) Vorinostat l 16 *** le r *** *** M) *** Ct on 0μ 100 to *** essi to 4 *** ed ** ** *** pr ** ** ** liz
ex * ma 50 ve 1 ti or ormalized la
(N *** (N Re
Cell proliferation (OD450) *** *** 0 3 2 1 1 2 B I1 Z B 1 D F G P R F S O1 A G7 R N CB μM T T A N N P 0 0 A FG O N U R4A3 K .5 . B US EG F G NA G G ITPK IT J 2 5 D G N PR
Ctrl F G GES-1-T Astemizole GES-1-T 150 l ***
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Cell proliferation (OD450) *** 0 3 2 1 2 B 1 1 Z B 1 3 B F G P S O A K R A μM S GF AI P ND 4 C 0 .0 .0 AT O N A K 5 0 BT U EGR1 F F N GN GNG7 ITP IT JU R 1 D G G N PR
H I Ctrl HGC-27 HGC-27 Trichostatin A 150 16
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ze ** dt i xp l ze li ee 50 ma v 1 i ma or *** t or *** la (N (N Cell proliferation (OD450) Re ** 0 ng/ml 3 2 1 1 2 1 1 7 B 1 B 0 0 F G P F SB AZ G A3 00 0 T G PR ND 4 1 4 A GR O AO N PK BT US E F F N G GN IT IT JU D GNAI G NR PRKC Dong et al. J Transl Med (2021) 19:226 Page 16 of 18
TSA, and astemizole) were determined and validated as further exploration of the pathogenesis and therapy strat- the therapeutic options for GC. As histone deacetylase egies of GC from the circRNA-miRNA-mRNA network (HDAC) inhibitor, vorinostat alters the level of histone perspective. and nonhistone protein acetylation and thereby regulates gene expression, cell proliferation, angiogenesis and cell Abbreviations survival [50]. Vorinostat is FDA-approved for the treat- CircRNA: Circular RNA; GC: Gastric cancer; GEO: Gene Expression Omnibus; ment of cutaneous T cell lymphoma [51] and has been RNA-seq: RNA sequencing; DECs: Diferentially expressed circRNAs; RT-qPCR: Reverse transcription-quantitative polymerase chain reaction; DEGs: Difer- investigated in diverse clinical trials as a potential mono- ently expressed genes; TCGA: The Cancer Genome Atlas; KEGG: Kyoto Ency- or combination-drug therapy for solid tumors includ- clopedia of Genes and Genomes; ceRNA: Competing endogenous RNA; MRE: ing GC [52, 53]. TSA is another kind of typical HDAC MiRNA response element; PPI: Protein–protein interaction; CMap: Connectivity map; CLIP: Cross-linking immunoprecipitation; MCODE: Molecular Complex inhibitor used mainly in laboratory experiments. TSA is Detection; HDAC: Histone deacetylase; FDA: Food and Drug Administration. known to induce cell cycle arrest and apoptosis in dif- ferent cancer cell lines, and its antitumor efect in solid Supplementary Information tumors including GC has been illuminated previously The online version contains supplementary material available at https://doi. [54]. Astemizole is an old anti-histamine that can tar- org/10.1186/s12967-021-02903-5. get important proteins involved in the cancer progres- sion, namely, ether à-go-go (Eag1) and Eag-related gene Additional fle 1: 1492 DEGs in GC obtained from TCGA. potassium channels [55], thus inhibiting tumor cell pro- Additional fle 2: Table S1. CircRNA–miRNA interactions identifed liferation [56]. Moreover, previous evidence has revealed by both CircInteractome and Circbankdatabases. Table S2. Permuted that Eag1 is expressed in several human tumor cell lines, results of the three compounds by CMap analysis. Figure S1. Flow chart of thepresent study. Figure S2. The circRNA–miRNA–mRNA regulatory including those from GC [57]. However, its anti-GC networks of hsa_circ_0000615,hsa_circ_0001438, hsa_circ_0002190 and efect have not been elucidated presently. In this study, hsa_circ_0002449 in GC. Figure S3. The circRNA–miRNA–mRNAregulatory we found that it potentially serves as therapeutic agent networks of hsa_circ_0000390 and hsa_circ_0003012 in GC. Figure S4. Expression of ffteenhubgenes in GC by RT-qPCR. for GC. Tis study provides a basis for exploring the patho- Acknowledgements genesis and treatment strategy of GC from the circRNA- Not applicable. miRNA-mRNA network perspective and provides more evidence for three bioactive compounds (vorinostat, Authors’ Contributions WY, ZL, ML, ZD, and XZ contributed conception and design of the study; TSA, astemizole) as anti-GC agents. A recent study has WY, ZD, and ML organized the data; ZD, ZL, and WY performed the statistical presented perspectives on circRNA‐miRNA‐mRNA net- analysis; ZD and JP performed the experiments; ZD, JP, HL, and WL collected works to explore the pathogenesis and therapy for pan- the clinical specimens. All authors contributed to manuscript revision, read creatic ductal adenocarcinoma (PDAC) [58], and another and approved the submitted version. study reported similar insights into the pathogenesis and Funding therapy of HCC from the circRNA–miRNA–mRNA net- This study was supported by the National Natural Science Foundation of China (81673206 and 31500142), the Science and Technology Program of work view [59]. However, these perspectives or insights Guangzhou (201902020001, 201905010004 and 20191A011090), the Project needs further wet lab investigations. Nevertheless, of Educational Commission of Guangdong Province (2019KTSCX142), combined with our fndings, these studies further our and Guangdong Medical Science and Technology Research Fund Project understanding of tumors from the perspective of cir- (A2019466). cRNA‐related ceRNA networks. Availability of data and materials All data generated or analyzed during this study are included in this article. Conclusions The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualifed In conclusion, through an integrated analysis of microar- researcher. ray and RNA-Seq data, RT-qPCR and computational biol- ogy, six circRNA-miRNA-mRNA regulatory networks Declarations were established and these ceRNA networks uncovered Ethics approval and consent to participate six circRNAs (hsa_circ_0000390, hsa_circ_0000615, The Ethical Committee of the Fifth Afliated Hospital of Guangzhou Medical hsa_circ_0001438, hsa_circ_0002190, hsa_circ_0002449 University approved the present study. Primary GC samples were from the and hsa_circ_0003120) that might function as ceRNA Fifth Afliated Hospital of Guangzhou Medical University with informed to play important roles in GC. Additionally, three bioac- consent. tive compounds (vorinostat, TSA, astemizole) obtained Consent for publication from the CMap analysis were identifed as therapeu- The authors agree for publication. tic agents for GC. Our study provides a new insight for Competing interests The authors declare that they have no competing interests. Dong et al. J Transl Med (2021) 19:226 Page 17 of 18
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