Research Article Bioinformatic Analysis of Circular RNA-Associated Cerna Network Associated with Hepatocellular Carcinoma

Hindawi BioMed Research International Volume 2019, Article ID 8308694, 14 pages https://doi.org/10.1155/2019/8308694

Research Article Bioinformatic Analysis of Circular RNA-Associated ceRNA Network Associated with Hepatocellular Carcinoma

Jiacheng Wu ,1,2 Shui Liu ,1,2 Yien Xiang ,1,2 Xianzhi Qu ,1,2 Yingjun Xie ,1,2 and Xuewen Zhang 1,2

1Department of Hepatobiliary and Pancreatic Surgery, Second Hospital of Jilin University, Changchun 130041, China 2Jilin Engineering Laboratory for Translational Medicine of Hepatobiliary and Pancreatic Diseases, Changchun 130041, China

Correspondence should be addressed to Yingjun Xie; [email protected] and Xuewen Zhang; [email protected]

Received 20 July 2019; Revised 23 September 2019; Accepted 15 October 2019; Published 3 November 2019

Academic Editor: Hakan Bermek

Copyright © 2019 Jiacheng Wu et al. 3is 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. Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and is associated with a high mortality rate and poor treatment efficacy. In an attempt to investigate the mechanisms involved in the pathogenesis of HCC, bioinformatic analysis and validation by qRT-PCR were performed. 3ree circRNA GEO datasets and one miRNA GEO dataset were selected for this purpose. Upon combined biological prediction, a total of 11 differentially expressed circRNAs, 15 differentially expressed miRNAs, and 560 target genes were screened to construct a circRNA-related ceRNA network. GO analysis and KEGG pathway analysis were performed for the 560 target genes. To further screen key genes, a protein-protein interaction network of the target genes was constructed using STRING, and the genes and modules with higher degree were identified by MCODE and CytoHubba plugins of Cytoscape. Subsequently, a module was screened out and subjected to GO enrichment analysis and KEGG pathway analysis. 3is module included eight genes, which were further screened using TCGA. Finally, UBE2L3 was selected as a key gene and the hsa_circ_0009910–miR-1261–UBE2L3 regulatory axis was established. 3e relative expression of the regulatory axis members was confirmed by qRT-PCR in 30 pairs of samples, including HCC tissues and adjacent nontumor tissues. 3e results suggested that hsa_circ_0009910, which was upregulated in HCC tissues, participates in the pathogenesis of HCC by acting as a sponge of miR-1261 to regulate the expression of UBE2L3. Overall, this study provides support for the possible mechanisms of progression in HCC.

1. Introduction that is widely present in all organs and tissues; it was first identified in viruses in 1970 [4–7]. More than a million Hepatocellular carcinoma (HCC) is one of the most com- different circRNAs have since been identified in human mon malignancies of the digestive system, with a high tissue by high-throughput sequencing [8]. Some studies have mortality rate making it the sixth leading cause of cancer- also confirmed that circRNAs could potentially serve as related mortality worldwide [1, 2]. More than 466,100 new molecular markers or therapeutic targets for certain diseases, cases of HCC and 426,100 deaths occurred in China each particularly in cancer growth, metastasis, and therapy re- year due to HCC [3]. A research focus has been placed on sistance [9–12]. Although a comprehensive understanding how to improve the prognosis of HCC patients. In recent of the functions of circRNAs has not been obtained, recent years, the role of noncoding RNA in the development and studies have shown that they function as microRNA sponges progression of tumors has gradually been recognized by via competitive binding to the microRNA response element researchers in line with the deepening of research on the (MRE) to regulate the expression of target genes of molecular mechanisms associated with tumors. 3e main microRNAs [5, 13]. Many studies have also shown that categories of noncoding RNA are microRNA (miRNA), long circRNAs play important roles in the development of HCC. noncoding RNA (lncRNA), and circular RNA (circRNA). For example, a study by Han et al. found that circMTO1 CircRNA is a special kind of endogenous noncoding RNA could suppress HCC progression by acting as a sponge of 2 BioMed Research International oncogenic miR-9 to promote p21 expression. Another study online: http://www.funrich.org/). 3e thresholds of P < 0.05 by Yu et al. found that Cirs-7 competitively bound to miR-7 and FC > 2 were set to screen the significantly DEMs from to derepress the expression of CCNE1 and PIK3CD genes to the miRNA expression profile. promote the proliferation and invasiveness of liver cancer cells [14, 15]. Nonetheless, more research is still needed to explore the roles of circRNAs in the pathogenesis of HCC. 2.3. ceRNA Analysis of circRNA-Related Genes. 3e Circular Considering that the circRNAs and microRNAs differ- RNA Interactome (https://circinteractome.nia.nih.gov/) was entially expressed between hepatocellular carcinoma tissues used to predict miRNA binding sites (MREs), excluding and adjacent noncarcinoma tissue may play important roles context score percentile lower than 75, which were con- in HCC development and progression, three circRNA ex- sidered as potential target miRNAs of the DECs. 3ese target pression profiles (GSE78520, GSE97332, and GSE94508) miRNAs of DECs were further screened by overlapping with and one miRNA expression profile (GSE64632) were the DEMs identified previously. 3e interactions of miRNAs downloaded from the Gene Expression Omnibus (GEO) and mRNAs were established using TargetScan (http://www. database of the National Center of Biotechnology In- targetscan.org) and miRDB (http://www.mirdb.org). We formation to obtain differentially expressed circRNAs identified mRNAs overlapping between the two algorithms (DECs) and differentially expressed miRNAs (DEMs) using as potential target genes of the miRNAs. 3e interactive R software. 3e interactions of circRNA and miRNA, and networks of DEGs, DEMs, and target genes were thus miRNA and mRNA were predicted using online databases, established and visualized using Cytoscape 3.6.1. and 560 target genes were obtained. Subsequently, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes 2.4. Functional Enrichment Analysis of Target Genes. (KEGG) pathway enrichment, and protein-protein in- Gene Ontology (GO) annotation and Kyoto Encyclopedia of teraction (PPI) network analyses were performed to reveal Genes and Genomes (KEGG) pathway analysis of the target the interactive relationships among the target genes to ex- genes were carried out using Omicsbean online database plore the underlying molecular mechanisms involved in the (http://www.omicsbean.cn/). P < 0.05 was considered sta- carcinogenesis and progression of HCC [16, 17]. A gene tistically significant. module was screened from the PPI network and further validated for the expression levels of the genes and clinical relevance using 3e Cancer Genome Atlas (TCGA). Finally, 2.5. PPI of Target Genes and Identification of Key Module. UBE2L3 was screened out and its ceRNA regulatory network A protein-protein interaction (PPI) network of the target was constructed; the expression levels of molecules in this genes was established using STRING (v10.5) [22]; the regulatory network were also confirmed by qPCR in tumor minimum required interaction score was set to 0.4 and the tissues and adjacent nontumor tissues. 3e purpose of this network was visualized with Cytoscape 3.6.1. CytoHubba, a study was to provide valuable insights for biomarker dis- plugin in Cytoscape, was used to identify hub genes of the covery and the development of a novel treatment strategy for PPI network. “Molecular Complex Detection” (MCODE), HCC (Figure 1). another plugin of Cytoscape, was used to analyze modules of the PPI network, with the degree cut-off set to 5. 3e key module was identified and combined hub genes and 2. Materials and Methods modules GO analysis and KEGG pathway analysis were also 2.1. Microarray Data. Microarray datasets providing performed on this key module. circRNA and miRNA expression profiles of HCC were downloaded from the Gene Expression Omnibus (GEO) 2.6. Further Screening of Module Genes by TCGA. database [18]. 3e three circRNA expression profiles Screening of the module genes in TCGA dataset was per- (GSE78520, GSE97332 [19], and GSE94508 [20]) were from formed by GETIA (http://gepia.cancer-pku.cn/) [23]. Ex- the platform of GLP19978. A total of 15 pairs of samples of pression analysis for different sample types (HCC and HCC tissues and adjacent nontumor tissues were included in normal liver tissues), association analysis of gene expression the circRNA microarray dataset. 3e miRNA expression level and LIHC patients’ tumor stages, and analyses of profile of GSE64632 [21] was from the platform of overall survival and disease-free survival were also GPL18116, which contained three pairs of samples of HCC performed. tissues and adjacent nontumor tissues (Table 1).

2.7. Quantitative Real-Time PCR Analysis. A total of 30 HCC 2.2. Differential Expression Analysis. 3e “limma” package patients were recruited for the sampling of tumor and adjacent (3.38.3) in R (5.3.2) was applied to screen differentially nontumor tissues. 3is study was approved by the Ethics expressed circRNAs (DECs) and differentially expressed Committee of the Second Clinical Medical College, Jilin miRNAs (DEMs) between HCC samples and adjacent University. Total RNA extraction was performed using Trizol nontumor samples. 3e significantly DECs (P < 0.01 and reagent (Sangon Biotech, Shanghai, China). Reverse tran- FC > 2) of each of the three circRNA expression profiles were scription of circRNA and mRNA was performed using First identified. 3e overlapping upregulated and downregulated Strand cDNA Synthesis Kit (Sangon Biotech), and reverse DECs were analyzed using FunRich software (available transcription of miRNA was performed using miRNA First BioMed Research International 3

15 15

12 12

9 9

6 6

3 3

0 0 –6 –4.5 –3 –1.5 0 1.5 3 –6–4–20246 Three circRNA expression profiles One miRNA expression profile

DECs DEMs circinteractome

Targetscan, miRDB

Target genes

ceRNA network

PPI analysis (Enrichment analysis)

Key module (Enrichment analysis)

TCGA hsa_circ_0009910

UBE2L3 mRNA Key gene miR-1261

qPCR Figure 1: Process of circRNA-related ceRNA regulatory network construction and identiﬁcation of key genes in HCC.

Table 1: Information on the three circRNA microarrays and one miRNA microarray. Series Type of microarray Tumor Nontumor Platforms Reference GSE78520 circRNA profile1 3 3 GPL19978 — GSE97332 circRNA profile2 7 7 GPL19978 [19] GSE94508 circRNA profile3 5 5 GPL19978 [20] GSE64632 miRNA profile 3 3 GLP18116 [21] 4 BioMed Research International

Strand cDNA Synthesis (tailing reaction) (Sangon Biotech), Table 2: 3e differentially expressed circRNAs and miRNAs from with the following primers: hsa_circ_0009910 (forward: 5′-GC the downloaded expression profiles. AGAACTGGACCCCGTTACC-3′, reverse: 5′-CAGGGACA Dataset Upregulation Downregulation Total TTGCGCGGCCAA-3′), UBE2L3 (forward: 5′-TTAGTGCC- GSE78520 128 17 145 GAAAACTGGAAGC-3′, reverse: 5′-ATTCACCAGTGCTA GSE97332 440 427 867 TGAGGGAC-3′), and GAPDH (forward: 5′-GACAGT- GSE94508 200 337 537 CAGCCGCATCTTCT-3′, reverse: 5′-ACCAAATCCGTT GSE64632 227 88 315 GACTCCGA-3′) synthesized by Sangon Biotech. Primers of miR-1261 and U6 were purchased from GeneCopoeia (Guangzhou, China). 3e primer specificity of hsa_- 3.3. Enrichment Analysis of the Target Genes. GO and KEGG circ_0009910 was verified by Circprimer1.2.0.5 [24] and enrichment analyses were performed for the 560 target genes Sanger sequencing. Real-time PCR was performed using of the DEMs to investigate the biological functions of the 2×SG Fast qPCR Master Mix (Sangon, China), in accor- circRNAs. 3e top 10 GO terms of each group are shown in dance with the manufacturer’s protocol, and the Light- Figure 5(a). In the biological process category, the main Cycler 480II Fast Real-Time PCR System was applied enriched categories were “biological regulation,” “regulation (Roche, Indianapolis, IN, USA). All procedures were of cellular process,” and “regulation of biological process.” In performed in accordance with the manufacturers’ pro- the cellular component category, the main enriched cate- tocols. 3e relative expression was normalized to GAPDH gories were “membrane-bounded organelle,” “organelle,” and/or U6 expression by the comparative CT method. 3e and “intracellular.” In the molecular function category, the relative expression was calculated using the 2− ΔΔCT main enriched categories were “protein binding,” “binding,” method. and “RNA polymerase II transcription factor activity, sequence-specific DNA binding.” Finally, in the KEGG pathway analysis, the most enriched KEGG pathways were 3. Result “ubiquitin-mediated proteolysis,” “JAK-STAT signaling pathway,” and “pathway in cancer.” 3e top 3 enriched 3.1. DECs Based on 7ree Microarray Datasets. 3ree cancer-related pathways are shown in Figure 5(b). microarray databases (GSE78520, GSE97332, and GSE94508) were included in our study, all of which were from the same platform of GPL19978. A summary of the 3.4. Identification of Key Module in the PPI Network. On the three databases is presented in Table 1. Based on the basis of the STRING database, we established a PPI network thresholds of P < 0.01 and |LogFC| > 1, a total of 145 DECs to show the interactions of the 560 target genes, and the top (128 upregulated and 17 downregulated) were identified in 20 hub genes were selected using CytoHubba, which were profile GSE78520, 867 DECs (440 upregulated and 427 the nodes with higher degree in the network. Further downregulated) in profile GSE97332, and 537 DECs (200 MCODE analysis revealed two modules from the network. upregulated and 337 downregulated) in profile GSE94508 Module 1 consisted of 9 genes and 72 edges, while module 2 (Table 2). Eleven co-upregulated DECs (hsa_circ_0072088, consisted of 8 genes and 63 edges. We found that 5 of the 8 hsa_circ_0051732, hsa_circ_0005397, hsa_circ_0000673, genes in module 2 were hub genes of the PPI network, while hsa_circ_0001338, hsa_circ_0003945, hsa_circ_0027478, only 2 of the 9 genes were in module 1. 3erefore, we hsa_circ_0092283, hsa_circ_0003923, hsa_circ_0009910, identified module 2 as the key module for further analysis. and hsa_circ_0001901) were identified by Venn analysis 3e PPI network, hub genes, and modules are shown in among the three databases, while no co-downregulated Figure 6. DECs were identified (Figure 2). 3e basic characteristics of the differentially expressed circRNAs are shown in Table 3. 3.5. Enrichment Analysis of Key Module. To further explore the biological function of the key module, functional enrichment analysis was performed based on the Omicsbean 3.2. Construction of the ceRNA Network. A total of 11 database. Regarding the GO terms, the main enriched ones circRNAs were selected for further study, for which a total of were protein ubiquitination, cytosol, ubiquitin ligase com- 180 target miRNAs were predicted by CircInteractome. plex, and ubiquitin-protein transferase activity. 3e KEGG Based on the thresholds of P < 0.05 and |LogFC| > 1, a total signaling pathway analysis showed marked enrichment of of 315 differentially expressed miRNAs were screened out in ubiquitin-mediated proteolysis. 3e results are shown in GSE64632, including 227 upregulated and 88 downregulated Figure 7. ones (Table 2). Fifteen DEMs were identified by Venn analysis among the 180 target miRNAs and 296 differentially expressed miRNAs, including 8 with upregulated expression 3.6. TCGA Database Analysis. To further identify key genes and 7 with downregulated expression (Figure 3). 3e target with more reliable support for their involvement in the genes for each of these 15 DEMs were predicted using two pathogenesis of HCC from the key module, GEPIA was used online databases, TargetScan and miRDB, and a total of 560 to analyze the transcript expression of the key module genes mRNAs that overlapped between the two databases were and their correlation with tumor stages, overall survival, and selected as target genes. 3e circRNA-miRNA-target gene disease-free survival as derived from TCGA database. 3e network is shown in Figure 4. statistical samples included 50 normal samples and 369 BioMed Research International 5

GSE94508 GSE94508

136 328 53 9 11 316 0 GSE97332 0 406 GSE97332 60 0 12 57 5 GSE78520 GSE78520 (a) (b)

–6.76 hsa_circ_0051732 hsa_circ_0001338 –9.00 –5.76 hsa_circ_0027478 –4.84 hsa_circ_0092283 –6.25 –4.00 hsa_circ_0005397 hsa_circ_0003923 –3.24 hsa_circ_0009910 –4.00 –2.56 hsa_circ_0005397 hsa_circ_0072088 –1.96 hsa_circ_0000673 –2.25 –1.44 hsa_circ_0001338 1.44 hsa_circ_0003945 2.25 hsa_circ_0092283 1.96 hsa_circ_0009910 2.56 hsa_circ_0000673 hsa_circ_0072088 3.24 4.00 hsa_circ_0003923 4.00 hsa_circ_0051732 4.84 6.25 hsa_circ_0003945 5.76 hsa_circ_0027478 hsa_circ_0001901 6.76 hsa_circ_0001901 9.00 GSM2072074 GSM2072073 GSM2072075 GSM2072077 GSM2072076 GSM2072078 GSM2477191 GSM2477195 GSM2477193 GSM2477197 GSM2477199 GSM2477196 GSM2477198 GSM2477192 GSM2477194 GSM2477200 (c) (d)

hsa_circ_0051732 –12.25

hsa_circ_0009910 –9.00

hsa_circ_0000673 –6.25 hsa_circ_0092283 –4.00 hsa_circ_0003945 –2.25 hsa_circ_0027478 2.25 hsa_circ_0001901 4.00 hsa_circ_0072088

hsa_circ_0005397 6.25

hsa_circ_0001338 9.00

hsa_circ_0003923 12.25 GSM2561837 GSM2561836 GSM2561838 GSM2561840 GSM2561839 GSM2561841 GSM2561842 GSM2561832 GSM2561834 GSM2561830 GSM2561829 GSM2561835 GSM2561831 GSM2561833 (e)

Figure 2: Differentially expressed circRNA screening. (a) Venn analysis of commonly upregulated circRNAs. (b) Venn analysis of commonly downregulated circRNAs. Heatmap of 11 commonly upregulated circRNAs in GSE78520 (c), GSE97332 (d), and GSE94508 (e). hepatocellular carcinoma samples. As shown in Table 4, and that of miR-1261 was 0.634 ± 0.1109-fold downregulated UBE2L3 was upregulated in HCC and showed a significant in HCC versus adjacent nontumor tissues (Figure 9). positive correlation with tumor stage and negative corre- lations with OS and DFS (Figure 8). It was identified as a key 4. Discussion gene in the pathogenesis of HCC. As a result of the development of high-throughput RNA sequencing and novel biochemical/computational biology 3.7. Construction of circRNA-miRNA-Key Gene Network and methods, an increasing number of studies have shown the qRT-PCR. UBE2L3 was selected as a key gene from the key importance of circRNAs in the initiation and development module; then, the hsa_circ_0009910–miR-1261–UBE2L3 of various diseases, including malignant cancers [25]. axis was constructed based on the former ceRNA network. circRNAs can often serve as biomarkers for diagnosis and 3e expression levels of the axis members were examined by prognosis because of their diversity and tissue-specific ex- qRT-PCR in 30 pairs of samples including HCC tissues and pression as well as their stability based on the covalently adjacent nontumor tissues. 3e results showed that the closed loop structures [26]. In HCC in particular, substantial relative expression levels of hsa_circ_0009910 and UBE2L3 evidence has been accumulated to prove the critical roles of were 1.812 ± 0.291-fold and 2.41 ± 0.4792-fold upregulated circRNAs. With the intensification of research on the 6 BioMed Research International

Table 3: Basic characteristics of diﬀerential expressed circRNAs. Spliced Gene Circbase ID Spot_ID Position Strand Best transcript Regulation length symbol hsa_circ_0072088 hsa_circRNA_103809 chr5 : 32379220-32388780 693 − NM_016107 ZFR Up chr19 : 48660270- hsa_circ_0051732 hsa_circRNA_102587 127 − NM_000234 LIG1 Up 48660397 chr17 : 30500849- hsa_circ_0005397 hsa_circRNA_102034 233 + NM_001033568 RHOT1 Up 30503232 chr16 :11940357- hsa_circ_0000673 hsa_circRNA_101707 251 NM_015659 RSL1D1 Up 11940700 chr3 :128824688- hsa_circ_0001338 hsa_circRNA_001416 434 − NM_001204883 RAB43 Up 128825122 hsa_circ_0003945 hsa_circRNA_104760 chr9 : 33953282-33956144 258 − NM_018449 UBAP2 Up chr12 : 69109406- hsa_circ_0027478 hsa_circRNA_101094 1029 + NM_020401 NUP107 Up 69125499 chr22 : 36681395- hsa_circ_0092283 hsa_circRNA_400071 300 − NM_002473 MYH9 Up 36681695 chr2 : 238933982- hsa_circ_0003923 hsa_circRNA_102954 162 + NM_080678 UBE2F Up 238940895 hsa_circ_0009910 hsa_circRNA_100053 chr1 :12049221-12052747 315 + NM_014874 MFN2 Up chr9 :138773785- hsa_circ_0001901 hsa_circRNA_000996 220 − NM_015447 CAMSAP1 Up 138774005

hsa-miR-188-3p –9.00 hsa-miR-421 –6.25 hsa-miR-331-3p 106 15 281 –4.00 hsa-miR-532-3p hsa-miR-384 –2.25

hsa-miR-409-3p 2.25 hsa-miR-140-3p Target miRNAs of 4.00 DE circRNAs hsa-miR-767-5p 6.25 hsa-miR-520f-5p DE miRNAs in GSE64632 hsa-miR-330-3p 9.00 hsa-miR-1261 hsa-miR-1299 hsa-miR-1827 hsa-miR-1270 hsa-miR-892a GSM1575889 GSM1575891 GSM1575893 GSM1575890 GSM1575888 GSM1575892 (a) (b)

Figure 3: Identification of differentially expressed miRNAs. (a) Venn analysis of target miRNAs of DECs and differentially expressed miRNAs of GSE64632 dataset. (b) Heat map of 15 DEMs in GSE64632 dataset. mechanisms of circRNA activity, their function of acting as more potentially significant circRNAs have yet to be iden- miRNA sponges in the process of tumor development has tified, which requires further exploration and research. been proven [5, 13]. Although the roles of some circRNAs in In the present study, using multiple cohort profile HCC have been identified [27, 28], it is suggested that many datasets and integrated bioinformatic analysis, a total of 11 BioMed Research International 7

GOLGA6L1 SETD1A SCN4A LENEP MOB3C INPP5B OSBPL7 PKDCCTP53I11

NUDT19 S100A7A CIB2 ELMOD1 ZNRF3 ZNF587 THEM6 TREML2 ZNF488FABP2 E2F2 CREB3L2 ALG10B ADAM30 SUDS3 FKBP1B LMBR1L TOM1L2 UBE2W CYP2B6 MPL TMEM14E ERMAP LYPLA2 LHPP PRELID2 TIE1 ARNTL2 FRG2C URM1 ADCK4 RAB41

EPHA8 PTPN18 PVRL1 KRT73 BDKRB2 ZNF169 COPS7B SOCS3 GAPT LYNX1CCL22 ZNF772 SLC9A1 RPS6KA4 IL23R CDKAL1 RAB36 RAB14 KRT32 RPUSD1 FAM53B

MYC HIF3A WASF2 BLOC1S3 WDFY2 RAE1 MRGPRX3 WNT3A ERF ZNF750 PRSS33 CPA4 HLF BEAN1 PLCXD1 SPRYD4 TMEM249 PRR24 VPS52 BCL7B TPBG DDA1 CBX5 SFXN2 CCDC97 THEM5 YPEL2 NRIP2 SYT15TUFT1 BSN PRKRIR EGLN3 AP3M1 SMARCD1 DESI1 FLCN PIPOX CYP8B1 USP5 CAMK1G EYA3 NHLH1 ZNF253 GRB2 FAM53C CPLX2 SFR1 ZNF513 JOSD2 ZNF460 UGT3A1 ASIC1 FNTB TEX28 POLE3 F2RL2 CHPF2 AWAT2 ZSCAN22 CYP4V2 SPRR4 OPTC NRIP3 ZNF490 ZNF528 B4GALT2 ARHGEF37 EIF5A2 ST8SIA3 CARNS1 SLC17A7 ACAD8 SDHAF2 DNAJC14 VAT1 WDTC1 TSPAN11 UBE2G1 GAL3ST4 NAA40 ZNF844 FGF18 ZDHHC19 LIX1L PLA2G1B FAM83E TSPAN18 GOLGA6L6 HOXC4 NFATC4 PLEKHH1 NMUR2 MIF4GD C2orf43 TCTN2 MAPK1IP1L TMPRSS11B PBOV1 MSMO1 CAPRIN2 USP12 IRG1 DUSP5 EIF2B1 PLSCR4 FKBP5 HECTD2 INPP5J EFEMP2 ZC3H12B STK40 PRUNE MUL1 ARF3 GPR3 MMD PRPF40B RTN4IP1 TMEM196 GIPC3 DDX58 TMEM144 MOB3A APOBEC3F KLHL12 FAM3C MTX3 RCOR3 PCDHA1 ARHGAP1 TTYH2 SYT9 STAMBPL1 ADAM21 YWHAZ HIGD1A WNT5B LBX2 HRASLS5 RBM27 PRKAR2B VPS4A SPOP MXD4 TMEM70 RYR3 SCAMP1 PCDHA2 CAP1 IL1RN MPZ SERPINH1 AP1S1 DOLPP1 IGFBP1 SRGN VHL KDSR GRIK1 RAB6C ZNF18 CD53 ETS1 KLF5 GBE1 FAM154A ZNF582 C15orf62 RYK THAP6 KCNIP1 HOXC10 PAM LECT1 GLT6D1 IFNGR2 CCER1 HMMR NPTX1 IL1RAPL2 KCNIP4 UBTD2 SNTN RALGAPB TVP23C CD22 POU5F1 ZXDA NDRG3 TMEM257 HN1L UNG BTF3L4 PTGS1 LPPR1 ABCA12 EVC RAB6A C18orf63 TSNAX GPR110 PIN1 ACO1 PTPLA NCALD SLC25A37 PPM1D hsa-miR-1827 RHOBTB3 PAQR3 C9orf3 MLIP TDGF1 PTBP1 CITED2 GJA1 PIGH hsa-miR-331-3p PABPC1L2A HSPB3 TIMM21 ZNF654 ATP6V0C TESPA1 LHCGR TNFRSF19 CHST11 KANK1 hsa-miR-140-3p MANSC1 DNAJC30 MXD3 HTR2C ILDR1 TRIM49 SETDB2 IPPK SLC27A2 SLC2A5 NCAPG2 CHRDL1 NXPH2 ZNF565 LYPD6 SBDS hsa-miR-1299 IL31 ZNF705A ZNF594 SMAP2 ZNF154 ZNF624 POLDIP3 BRK1 KRTAP4-5 ZNF780A SSBP2 TNFAIP1 TACR2 MATN2 ZNF776 ZNF559 ZFP2 hsa_circ_ CISD2 C10orf53 ZFP30 ZNF180 hsa-miR-892a hsa_circ_ ZNF302 CHMP5 IL12RB1 SUCLG2 PRR16 FANCL SOHLH2 ZNF181 ZNF439 0009910 NRXN2 GLT1D1 0003945 CISD1 GIMAP6 MS4A8 RAD21 ARRDC3 PTN GLDC FAM135B LGI1 TRH RNF145 METTL15 VPS26B COQ2 SENP2 C10orf82 hsa-miR-384 HDGF PDX1 LPAR1 CCDC172 C5orf30 ZNF268 SMPDL3A hsa_circ_0003923 hsa_circ_0001338 APLF SAMM50 MDM4 GTPBP4 PRNP GTF2B OXGR1 KLHDC8B HERPUD1 PYHIN1 GCC1 TRIM49C ZNF583 SNX24 ZNF175 CSNK2A1 ZHX2 TARSL2 EVI2B SPG20 ESM1 hsa-miR-188-3p CENPQ ARL4A C1orf74 DNAL4 GPX4 CCDC169-SOHLH2 ZNF440 WBP11 ALKBH8 DIRC1 SURF4 IKZF2 ENSA HMGCLL1 ANKRD40 SYTL2 NIF3L1 ZNF705D ANKRD10 SLC50A1 hsa_circ_0000673 hsa_circ_0001901 hsa-miR-767-5p PTS LYSMD1 ITM2B CBLL1 PEG10 TUBB6 LIMA1 ZNF781

GRIA3 KDELC1 CPOX CSDE1 COL11A1 CCDC43

PMM1 hsa-miR-520f-5p ZNF577 XKR4 BASP1 MYCN LYRM7 KRT79 MRPL9 hsa_circ_0092283 hsa_circ_0072088 RNF175 C8orf37 GINS1 LETMD1 PSTPIP2 SUMO1 KLF2 CACNA2D3 C8orf34 APITD1 hsa_circ_0027478 PGM5 SRP9 hsa-miR-330-3p DDX5 C10orf118 CDC26 NFIA TMEM169 RDH12 GNG10 SCAI EIF4E2 BTLA BRE LTA4H hsa-miR-421 SLC4A8 PI15 C12orf65 BET1L ADAMTS2 TIMELESS HAPLN3 NAP1L5 AHR hsa-miR-409-3p hsa-miR-1261 RPS6KB1 PMP22 HMGCS1 FAM214A hsa-miR-532-3p AKR1D1 PACRGL CASP3 hsa-miR-1270

NDFIP1 PRCC RCAN1

RASA1 TMEFF1 PPP1CC TLDC2 MTL5 PTHLH CLDN11 HMGA2 LBH BET1 ANAPC11 C3orf62 PRR23C C1orf101 CASP14 SS18L1 SPRY1 PAPD5 EIF2A HDAC3 INSIG1 EDEM3 TESC ANGPTL7 CBX1 CSF3 DDOST NRAS SNX16 RND3 NAIP CNDP1 EDIL3 BZRAP1 PTGFR DCPS ONECUT2 C15orf40 ELTD1 NARS POU2F3 SOSTDC1 TOMM70A GTDC1 CLMP ZCWPW2 STOML3 ENTPD3 43355 KIAA2026 CYTH1 FMO4 CSF3R ZNF382 TMEM183A SLC16A14 MIPOL1 WBP2NL CCDC121 XKR9 PTP4A1 SLC39A8 FAM198B ACTBL2 TMEM199 CREG2 SHISA3 NEDD8 PBX3 RAD51 COPS4 AFP CCDC170 SET PPIL3 SERPINA7 ILF2 ETNK2 RASAL2 NPTN UBL4A C1orf174 C2orf68 UBE2L3 DNAAF3 LAMB4 GIF GSG1L CARHSP1 RFC5 RAB9A PCYOX1L SENP5 CBFB Figure 4: 3e network of circRNA-miRNA-target genes. Red represents circRNA, green represents miRNA, and yellow represents target gene. differentially expressed circRNAs and 315 differentially module. Its genes were found to be particularly associated expressed miRNAs were screened from four GEO databases. with protein ubiquitination (in Biological Process terms) Based on the mechanism of conserved endogenous circR- and ubiquitin-mediated proteolysis (in the KEGG pathway NAs harboring abundant miRNA binding sites to act as analysis). 3ey were also significantly related to the cancer miRNA sponges and to function as ceRNAs to regulate gene process. 3e module genes were further analyzed based on expression [29–31], a total of 15 DEMs were screened out to the TCGA database and UBE2L3 was identified as a key gene interact with the candidate circRNAs and 560 mRNAs were associated with the pathogenesis of HCC. 3en, the selected as potential target genes of them. To further hsa_circ_0009910–hsa-miR-1261–UBE2L3 regulatory axis speculate on the function of the ceRNA network, functional was constructed and its expression was verified by qPCR. In annotation and pathway analysis of the target genes were the study, the expression of hsa_circ_0009910 and UBE2L3 performed. 3e results of the GO and KEGG pathway en- was upregulated and that of hsa-miR-1261 was down- richment analyses suggested that the target genes were regulated in HCC, which is consistent with the theory of significantly enriched in different cancer-related functions ceRNA [29]. and pathways. To identify key module in the target genes, a It is well known that circRNA-mediated ceRNA path- PPI network was constructed and combined with hub gene ways are essential for multiple functions, with the target and module analyses, and module 2 was identified as a key mRNA determining their function based on ceRNA theory 8 BioMed Research International

500

80 400

60 300

40 200 Number of genes Percent of genes (%)

20 100

0 0

Vesicle

Cytosol

Binding

Organelle

Cytoplasm

Intracellular

Enzyme binding

Protein bindings

Intracellular part

Receptor binding

Biological regulation

Intracellular organelle

Developmental process

Endomembrane system

Identical protein binding

Regulation of cellular process

Membrane-bounded organelle

Regulation of biological process

Protein domain specific binding

sequence-specific DNA binding

Multicellular organismal process

Anatomical structure development

Positive regulation of cellular process

Single-multicellular organism process

Multicellular of organism development

Single-organism developmental process

Intracellular membrane-bounded organelle

Regulatory region sequence-specific binding

RNA polymerase II transcription factor activity,

Nucleic acid binding transcription factor activity

Transcription factor activity, sequence-specific DNA binding

Transcriptional activator activity, RNA polymerase II transcription

Biological process Cell component Molecular function (a) Figure 5: Continued. BioMed Research International 9

Jak-STAT signaling pathway LPAR1 MPL Pathways in cancer

NRAS Ubiquitin mediated proteolysis IFNGR2

PPP1CC HTR7

GRB2 PTGS1

RAD51 GNG10

FGF18 SLC18A1

FANCL SOCS3

ETS1 SPRY1

EGLN3 UBE2G1

UBE2L3 E2F2

IL12RB1 DDX58

VHL CSF3R WNT3A CSF3 WNT5B BDKRB2 CASP3 (b)

Figure 5: Enrichment analysis of target genes. (a) GO enrichment analysis of target genes. (b) KEGG pathway analysis of target genes.

PTP4A1CD22HSPB3STAMBPL1NFIA NRIP3NRIP2PRNP BCL7BCACNA2D3CREB3L2 GINS1PBX3 GJA1USP5 SETD1ANPTX1 RAB41SCN4A NPTN GBE1 SENP5 SUCLG2 RYK TIMM21 WASF2 COQ2 PTN NCALD HIGD1A SURF4 AP3M1 SERPINH1 WNT5B SET MTX3 CSDE1 CYP4V2 ASIC1 E2F2 GPX4 PEG10 MDM4 EIF2A IFNGR2 CARNS1 TESPA1 EIF4E2 HLF RAB14 PABPC1L2A ARNTL2 CENPQ BET1 RAE1 AKR1D1 ALG10B GCC1 HMGCLL1 RASAL2 MXD4 RCAN1 OPTC EIF2B1 POLDIP3 ARRDC3 RYR3 COL11A1 RTN4IP1 TOM1L2 IL23R FKBP5 KLF5 AWAT2 LTA4H SPRY1 HIF3A BET1L FLCN RNF145 PMM1 SYT9 AHR PTPN18 CBX5 MRPL9 SERPINA7

LYPLA2 KLF2

ADAMTS2 INSIG1

LHPP SPOP

ATP6V0C IGFBP1

HRASLS5 URM1

MPZ RAB6C PMP22 WDTC1 RAB36 NARS HDAC3 CAP1 ADCK4 UBL4A SAMM50 LYNX1 CYTH1 PGM5 SOCS3 PRKAR2B POU2F3 MYCN HMMR ARHGAP1 AP1S1

SFXN2 TESC

SLC25A37 LHCGR

ZHX2 VAT1 CLDN11 UBE2L3 NEDD8 BRE CPLX2 MPL

PAPD5 ZNRF3

CBFB POLE3

SYTL2 SUDS3 PTPLA GRB2 FANCL HOXC4 ARF3 MSMO1

PPIL3 CYP8B1

DDX58 DDOST

NAP1L5 PDX1

SLC4A8 WBP11 NAIP PPP1CC SUMO1 PTGS1 ERF TPBG

SENP2 FKBP1B

FAM53C RHOBTB3

DDA1 IL12RB1

UBTD2 EGLN3 FABP2 GNG10 CASP3 CSF3R PIPOX LECT1

SFR1 ARL4A

STK40 PTS

TDGF1 AFP TIE1 UBE2G1 NRAS TUBB6 PKDCC SRP9

EPHA8 GTF2B

ZSCAN22 SMARCD1

CNDP1 RDH12 IRG1 RAD21 VHL SLC9A1 TSNAX PTHLH

EDIL3 ILF2 APLF LPAR1 RAD51 VPS26B TNFRSF19 POU5F1 ALKBH8 MYC BRK1 STOML3 VPS4A

CARHSP1 COPS4

CITED2 ETS1

NFATC4 TOMM70A MIF4GD OXGR1 SDHAF2 NCAPG2 TMEM199 PRCC FGF18 TIMELESS EDEM3 APITD1 MXD3 UNG ELMOD1 COPS7B GRIK1 FNTB APOBEC3F UBE2W ZNF513 ACO1 DUSP5 GTPBP4 NRXN2 RND3 DOLPP1 PTBP1 MUL1 HMGCS1 BEAN1 CDC26 BLOC1S3 RPS6KB1 TCTN2 RAB6A NDRG3 VPS52 ENTPD3 WNT3A FMO4 CHMP5 GAPT F2RL2 PAM TACR2 SETDB2 HTR2C SS18L1 CYP2B6 HMGA2 HECTD2 TARSL2 PIN1 SBDS PLA2G1B SLC2A5 RASA1 PI15 PTGFR DIRC1 TRH ACAD8 YWHAZ MATN2 DDX5 CAMK1G GLDC GRIA3 RAB9A ENSARPS6KA4 WDFY2PPM1D LYPD6RBM27 BDKRB2CSNK2A1 PCDHA2SLC27A2NIF3L1PRR16NMUR2ACTBL2CSF3ANAPC11RFC5 (a) Figure 6: Continued. 10 BioMed Research International

LPAR1 TRH UBE2G1

NEDD8 HECTD2 TACR2 HTR2C

F2RL2 SOCS3 VHL

GNG10 NMUR2

UBE2L3 CDC26 BDKRB2 PTGFR ANAPC11

(b) (c)

Figure 6: PPI analysis. (a) PPI analysis and hub gene screening of target genes. (b) Module 1 of the PPI network. (c) Module 2 of the PPI network. Red represents hub genes.

ANAPC11

Biological process

CDC26

UBE2G1

SOCS3

UBE2L3 Cellular component

NEDD8 Molecular function

HECTD2 KEGG term

GO term Protein ubiquitination Cytosol Protein modifcation by small protein conjugation Ubiquitin ligase complex Protein modifcation by small protein conjugation or removal Ubiquitin-protein transferase activity Protein polyubiquitination Ubiquitin-like protein transferase activity Ubiquitin-dependent protein catabolic process Ubiquitin conjugating enzyme activity KEGG term Ubiquitin mediated proteolysis Cell cycle Figure 7: Tumor-related GO enrichment analysis and KEGG pathway analysis of key module genes.

[32]. In the current study, the functions of the target genes pathway analysis. Protein ubiquitination is an important and the key module were all particularly associated with posttranslational mechanism for regulating the activity and ubiquitin-mediated proteolysis, as revealed by the KEGG levels of proteins in various conditions, including cancer BioMed Research International 11

Table 4: TCGA analysis of key module genes. Disease-free Stage Overall survival Module gene Hub gene (top 20) Upregulate survival F value P value HR P value HR P value VHL Yes – 2.4 0.0677 1.4 0.083 1.5 0.015∗ CDC26 No – 2.4 0.04∗ 1.5 0.025∗ 1.6 0.0031∗∗ ANAPC11 No Tumor∗ 2.61 0.513 1.3 0.13 1.3 0.084 UBE2L3 Yes Tumor∗ 4.5 0.00409∗ 1.8 0.0011∗∗ 1.5 0.0077∗∗ SOCS3 Yes Normal∗ 0.651 0.583 1 0.92 0.95 0.72 NEDD8 Yes Tumor∗ 1.13 0.336 1.3 0.11 1.6 0.0041∗∗ UBE2G1 Yes – 1.01 0.39 1.1 0.67 0.9 0.49 HECTD2 No – 1.76 0.155 1.8 0.0011∗∗ 1.5 0.0077∗∗

7 ∗ F 7.0 value = 4.5 Pr(>F) = 0.00409 6.5 6 6.0

5 5.5

UBE2L3 5.0 UBE2L3

4 Relative expression of 4.5 Relative expression of

4.0 3 3.5 Stage I Stage II Stage III Stage IV

LIHC (num (T) = 369; num (N) = 160) (a) (b) Overall survival Disease-free survival 1.0 1.0 Logrank p = 0.00092 p HR(high) = 1.8 Logrank = 0.0076 0.8 p(HR) = 0.0011 0.8 HR(high) = 1.5 n(high) = 181 p(HR) = 0.0077 n(low) = 181 n(high) = 181 0.6 0.6 n(low) = 181

0.4 0.4 Percent survival Percent survival

0.2 0.2

0.0 0.0

0 20 40 60 80 100 120 0 20 40 60 80 100 120 Months Months Low UBE2L3 TPM Low UBE2L3 TPM High UBE2L3 TPM High UBE2L3 TPM (c) (d)

Figure 8: TCGA analysis of UBE2L3 by GEPIA. (a) Boxplots depicting expression levels in HCC versus nontumor liver tissues. (b) Violin plots depicting expression levels associated with tumor grades in HCC. (c) Kaplan–Meier plots comparing the overall survival rates with high expression and low expression in HCC. (d) Kaplan–Meier plots comparing the disease-free survival rates with high expression and low expression in HCC. 12 BioMed Research International

Splice site hsa_circ_0009910

AGGTGCTGGCTCGGAGGCACATGAAAGTGGCTTTTTTTGGCCGCGCAATGTCCTG C 60 70 80 90 100 110

UBE2L3mRNA miR-1261 Splice site sequence in circbase: TTTTTTTGGCCG-CGCAATGTCCCT (a) (b) ∗ ∗∗ 8 4 6 3 4 2 2 1 hsa-miR-1261 hsa_circ_0009910

Relative expression of 0 0 Tumor Adjacent Relative expression of Tumor Adjacent nontumor nontumor (c) (d) 15 ∗ 10

UBE2L3 0

Relative expression of –5 Tumor Adjacent nontumor (e)

Figure 9: Regulatory axis construction and qPCR validation. (a) hsa_circ_0009910–miR-1261–UBE2L3 regulatory axis. (b) Sanger sequencing for circRNA primer veriﬁcation. (c–e) Validation of expression of hsa_circ_0009910–miR-1261–UBE2L3 axis members in 30 pairs of HCC and adjacent nontumor tissues by qRT-PCR. ∗P < 0.05, ∗∗P < 0.01.

[33]. In recent years, many studies have indicated the roles of and migration were enhanced, while they were inhibited protein ubiquitination and ubiquitin-mediated proteolysis upon its knockdown in QGY-7703. Further study found that in tumorigenesis, involved in regulating cell cycle pro- UBE2L3 may degrade CDKN2B and CLDN1 [36]. 3ere- gression, apoptotic factors, cancer metastasis, and the tu- fore, UBE2L3 may be an important oncogene in the de- mor-associated microenvironment. Disrupted regulation of velopment of HCC, but the upstream mechanism associated protein ubiquitination may be one of the triggers for tumor with it has not previously been reported. development [34]. 3erefore, the circRNAs in the ceRNA It is widely recognized that miRNA-mediated pathways network may also play a tumor regulatory role through play roles in tumorigenesis, including cell proliferation, ubiquitin-mediated proteolysis. migration, and apoptosis. In the ceRNA network established Based on the integrated bioinformatic analysis, UBE2L3 in this study, 15 differentially expressed miRNAs were in- was finally identified as a key gene among the target genes. It cluded, including 8 upregulated and 7 downregulated ones is one of the 38 ubiquitin-conjugating enzymes (E2) and in tumor samples. 3ey mediated the link between circRNA participates in the ubiquitin transfer pathway and protein and target genes. Hsa-miR-1261, upstream of UBE2L3, was degradation [35]. It was previously observed that UBE2L3 downregulated in HCC; its role in tumorigenesis has been expression may play an important role in the pathobiology reported in several different tumor types. For example, of HCC and be expressed more highly in HCC samples than Zhang et al. reported that miR-1261 could regulate the in normal tissues; in addition, increased expression of expression of circ-PTPRZ1/PAK1 and inhibit the pro- UBE2L3 is associated with the development of HCC, which liferation and invasion and promote the apoptosis of glioma matches our results. We also found that its expression was cells [37]. Moreover, Hong et al. reported that miR-1261 was positively correlated with tumor stage through TCGA da- downregulated in thyroid cancer cells and plays an in- tabase. Liu et al. found that UBE2L3 was ubiquitously hibitory role against proliferation and invasion [38]. 3e role expressed in all cell lines, but it was expressed more highly in of miR-1261 as identified in the present study appears to be the strongly metastatic types. Upon UBE2L3’s over- similar to the results of previous research, but further expression in the SNU-423 cell line, cellular proliferation verification of this is required. BioMed Research International 13

Hsa_circ_0009910 was one of the 11 commonly upre- Health Program of Jilin Province Finance Department, gulated circRNAs in the three datasets. Its role in tumors has Special Medical and Health Personnel Program of Jilin been tentatively explored in recent years. For example, Ping Province Finance Department (2019SCZT015), Health et al. reported that circ_0009910 was significantly upregu- Technology Innovative Program of Jilin Province lated in acute myeloid leukemia patients compared with its (2018J053), and the Project of Hepatobiliary and Pancreatic level in iron deficiency anemia patients, and its high ex- Disease Translational Medicine Platform Construction pression was predictive of poor prognosis; moreover, its (2017F009). silencing could inhibit cell proliferation and induce apoptosis through increasing miR-20a-5p [39]. Its procancer effects have also been verified in gastric cancer and osteo- References sarcoma [40, 41], but its expression and its correlation with [1] L. A. Torre, F. Bray, R. L. Siegel, J. Ferlay, J. Lortet-Tieulent, prognosis in HCC have never been reported. and A. Jemal, “Global cancer statistics, 2012,” CA: A Cancer In our study, hsa_circ_0009910 and UBE2L3 were Journal for Clinicians, vol. 65, no. 2, pp. 87–108, 2015. confirmed to be highly expressed and miR-1261 was [2] S. P. Choo, W. L. Tan, B. K. P. Goh, W. M. Tai, and A. X. Zhu, expressed at a low level by qPCR in 30 pairs of samples from “Comparison of hepatocellular carcinoma in Eastern versus HCC patients, including tumor tissues and adjacent non- Western populations,” Cancer, vol. 122, no. 22, pp. 3430– tumor tissues. Combined with bioinformatic prediction, it is 3446, 2016. preliminarily suggested that hsa_circ_0009910–miR-1261– [3] W. Chen, R. Zheng, P. D. Baade et al., “Cancer statistics in China, 2015,” CA: A Cancer Journal for Clinicians, vol. 66, UBE2L3 axis may exhibit a regulatory relationship in the no. 2, pp. 115–132, 2016. pathogenesis of HCC. Hsa_circ_0009910 may be a novel [4] W. R. Jeck, J. A. Sorrentino, K. Wang et al., “Circular RNAs molecule involved in the carcinogenesis of HCC. However, are abundant, conserved, and associated with ALU repeats,” our study is based on bioinformatic analysis, so further RNA, vol. 19, no. 2, pp. 141–157, 2013. experiments are needed to confirm the conclusions made [5] S. Memczak, M. Jens, A. Elefsinioti et al., “Circular RNAs are a here. large class of animal RNAs with regulatory potency,” Nature, vol. 495, no. 7441, pp. 333–338, 2013. Abbreviations [6] C. Lin, L. Wang, J. Sheng et al., “Transdifferentiation of pancreatic stromal tumor into leiomyosarcoma with metas- HCC: Hepatocellular carcinoma tases to liver and peritoneum: a case report,” BMC Cancer, miRNA: MicroRNA vol. 16, no. 1, p. 947, 2016. circRNA: Circular RNA [7] H. L. Sanger, G. Klotz, D. Riesner, H. J. Gross, and MRE: MicroRNA response element A. K. Kleinschmidt, “Viroids are single-stranded covalently GEO: Gene expression omnibus closed circular RNA molecules existing as highly base-paired rod-like structures,” Proceedings of the National Academy of DECs: Differentially expressed circRNAs Sciences, vol. 73, no. 11, pp. 3852–3856, 1976. DEMs: Differentially expressed miRNAs [8] S. Xia, J. Feng, L. Lei et al., “Comprehensive characterization GO: Gene ontology of tissue-specific circular RNAs in the human and mouse KEGG: Kyoto encyclopedia of genes and genomes genomes,” Briefings in Bioinformatics, vol. 18, no. 6, PPI: Protein-protein interaction pp. 984–992, 2017. TCGA: 3e cancer genome atlas. [9] J. Guarnerio, M. Bezzi, J. C. Jeong et al., “Oncogenic role of fusion-circRNAs derived from cancer-associated chromo- Data Availability somal translocations,” Cell, vol. 166, no. 4, pp. 1055-1056, 2016. Expression profile of GSE78520, GSE97332, GSE94508, and [10] S. Memczak, P. Papavasileiou, O. Peters, and N. Rajewsky, GSE64632 in the manuscript was downloaded from NCBI- “Identification and characterization of circular RNAs as a new GEO (https://www.ncbi.nlm.nih.gov/gds). class of putative biomarkers in human blood,” PLoS One, vol. 10, no. 10, Article ID e0141214, 2015. [11] J. H. Bahn, Q. Zhang, F. Li et al., “3e landscape of microRNA, Ethical Approval Piwi-interacting RNA, and circular RNA in human saliva,” 3is study was approved by the Research Ethics Committee Clinical Chemistry, vol. 61, no. 1, pp. 221–230, 2015. of Second Hospital of Jilin University. [12] L. Szabo, R. Morey, N. J. Palpant et al., “Statistically based splicing detection reveals neural enrichment and tissue-specific induction of circular RNA during human fetal devel- Conflicts of Interest opment,” Genome Biology, vol. 16, no. 1, p. 126, 2015. [13] T. B. Hansen, T. I. Jensen, B. H. Clausen et al., “Natural RNA 3e authors declare no competing financial and/or circles function as efficient microRNA sponges,” Nature, nonfinancial interests in relation to the work described. vol. 495, no. 7441, pp. 384–388, 2013. [14] L. Yu, X. Gong, L. Sun, Q. Zhou, B. Lu, and L. Zhu, “3e Acknowledgments circular RNA Cdr1as act as an oncogene in hepatocellular carcinoma through targeting miR-7 expression,” PLoS One, 3e authors thank Liwen Bianji, Edanz Group China (http:// vol. 11, no. 7, Article ID e0158347, 2016. www.liwenbianji.cn/ac), for editing the English text of a [15] L. Xu, M. Zhang, X. Zheng, P. Yi, C. Lan, and M. Xu, “3e draft of this manuscript. 3e work was funded by Special circular RNA ciRS-7 (Cdr1as) acts as a risk factor of hepatic 14 BioMed Research International

microvascular invasion in hepatocellular carcinoma,” Journal [33] D. Popovic, D. Vucic, and I. Dikic, “Ubiquitination in disease of Cancer Research and Clinical Oncology, vol. 143, no. 1, pathogenesis and treatment,” Nature Medicine, vol. 20, no. 11, pp. 17–27, 2017. pp. 1242–1253, 2014. [16] 3e Gene Ontology Consortium, “Expansion of the gene [34] Y.-J. Chen, H. Wu, and X.-Z. Shen, “3e ubiquitin- Ontology knowledgebase and resources,” Nucleic Acids Re- proteasome system and its potential application in hepato- search, vol. 45, no. D1, pp. D331–d338, 2017. cellular carcinoma therapy,” Cancer Letters, vol. 379, no. 2, [17] M. Kanehisa, M. Furumichi, M. Tanabe, Y. Sato, and pp. 245–252, 2016. K. Morishima, “KEGG: new perspectives on genomes, [35] R. J. Deshaies and C. A. P. Joazeiro, “RING domain E3 pathways, diseases and drugs,” Nucleic Acids Research, vol. 45, ubiquitin ligases,” Annual Review of Biochemistry, vol. 78, no. D1, pp. D353–D361, 2017. no. 1, pp. 399–434, 2009. [18] T. Barrett, S. E. Wilhite, P. Ledoux et al., “NCBI GEO: archive [36] Y. Liu, C. Song, H. Ni et al., “UBE2L3, a susceptibility gene for functional genomics data sets—update,” Nucleic Acids that plays oncogenic role in hepatitis B-related hepatocellular Research, vol. 41, no. D1, pp. D991–D995, 2013. carcinoma,” Journal of Viral Hepatitis, vol. 25, no. 11, [19] D. Han, J. Li, H. Wang et al., “Circular RNA circMTO1 acts as pp. 1363–1371, 2018. the sponge of microRNA-9 to suppress hepatocellular car- [37] F. Zhang, S.-R. Mai, F.-P. Cao, C. X. Cao, and L. Zhang, “MiR- cinoma progression,” Hepatology, vol. 66, no. 4, 1261/circ-PTPRZ1/PAK1 pathway regulates glioma cell pp. 1151–1164, 2017. growth and invasion,” Human Cell, vol. 32, no. 4, pp. 540–547, [20] L. Fu, T. Yao, Q. Chen, X. Mo, Y. Hu, and J. Guo, “Screening 2019. differential circular RNA expression profiles reveals hsa_circ_ [38] H. Wei, L. Pan, D. Tao, and R. Li, “Circular RNA circZFR 0004018 is associated with hepatocellular carcinoma,” contributes to papillary thyroid cancer cell proliferation and Oncotarget, vol. 8, no. 35, pp. 58405–58416, 2017. invasion by sponging miR-1261 and facilitating C8orf4 ex- [21] H. Peng, M. Ishida, L. Li et al., “Pseudogene INTS6P1 reg- pression,” Biochemical and Biophysical Research Communi- ulates its cognate gene INTS6 through competitive binding of cations, vol. 503, no. 1, pp. 56–61, 2018. miR-17-5p in hepatocellular carcinoma,” Oncotarget, vol. 6, [39] L. Ping, C. Jian-Jun, L. Chu-Shu, L. Guang-Hua, and Z. Ming, no. 8, pp. 5666–5677, 2015. “Silencing of circ_0009910 inhibits acute myeloid leukemia [22] D. Szklarczyk, J. H. Morris, H. Cook et al., “3e STRING cell growth through increasing miR-20a-5p,” Blood Cells, database in 2017: quality-controlled protein-protein associ- Molecules, and Diseases, vol. 75, pp. 41–47, 2019. ation networks, made broadly accessible,” Nucleic Acids Re- [40] M. Liu, K. D. Liu, L. Zhang et al., “Circ_0009910 regulates search, vol. 45, no. D1, pp. D362–D368, 2017. growth and metastasis and is associated with poor prognosis [23] Z. Tang, C. Li, B. Kang, G. Gao, C. Li, and Z. Zhang, “GEPIA: a in gastric cancer,” European Review for Medical and Phar- web server for cancer and normal gene expression profiling macological Sciences, vol. 22, no. 23, pp. 8248–8256, 2018. and interactive analyses,” Nucleic Acids Research, vol. 45, [41] N. Deng, L. Li, J. Gao et al., “Hsa_circ_0009910 promotes no. W1, pp. W98–w102, 2017. carcinogenesis by promoting the expression of miR-449a [24] S. Zhong, J. Wang, Q. Zhang, H. Xu, and J. Feng, “CircPrimer: target IL6R in osteosarcoma,” Biochemical and Biophysical a software for annotating circRNAs and determining the Research Communications, vol. 495, no. 1, pp. 189–196, 2018. specificity of circRNA primers,” BMC Bioinformatics, vol. 19, no. 1, p. 292, 2018. [25] S. P. Barrett and J. Salzman, “Circular RNAs: analysis, expression and potential functions,” Development, vol. 143, no. 11, pp. 1838–1847, 2016. [26] X. M. Jiang, Z. L. Li, J. L. Li et al., “A novel prognostic biomarker for cholangiocarcinoma: circRNA Cdr1as,” Eu- ropean Review for Medical and Pharmacological Sciences, vol. 22, no. 2, pp. 365–371, 2018. [27] G. S. Zhao, Y. Liu, Q. Zhang et al., “Transarterial chemo- embolization combined with huaier granule for the treatment of primary hepatic carcinoma: safety and efficacy,” Medicine (Baltimore), vol. 96, no. 29, Article ID e7589, 2017. [28] X. Yang, Q. Xiong, Y. Wu, S. Li, and F. Ge, “Quantitative proteomics reveals the regulatory networks of circular RNA CDR1as in hepatocellular carcinoma cells,” Journal of Pro- teome Research, vol. 16, no. 10, pp. 3891–3902, 2017. [29] Y. Zhong, Y. Du, X. Yang et al., “Circular RNAs function as ceRNAs to regulate and control human cancer progression,” Molecular Cancer, vol. 17, no. 1, p. 79, 2018. [30] Y. Zhang, W. Liang, P. Zhang et al., “Circular RNAs: emerging cancer biomarkers and targets,” Journal of Experimental & Clinical Cancer Research, vol. 36, no. 1, p. 152, 2017. [31] M. Cortes-Lopez and P. Miura, “Emerging functions of circular RNAs,” Yale Journal of Biology and Medicine, vol. 89, no. 4, pp. 527–537, 2016. [32] L. S. Kristensen, T. B. Hansen, M. T. Venø, and J. Kjems, “Circular RNAs in cancer: opportunities and challenges in the field,” Oncogene, vol. 37, no. 5, pp. 555–565, 2018. International Journal of Journal of Peptides Nucleic Acids

The Scientifc International Journal of International Journal of World Journal Cell Biology Microbiology Hindawi Publishing Corporation Hindawi Hindawi Hindawi Hindawi http://www.hindawi.comwww.hindawi.com Volume 20182013 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018

Anatomy Biochemistry Research International Research International

Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018

Submit your manuscripts at www.hindawi.com

Advances in Genetics Bioinformatics Research International Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018

Advances in International Journal of International Journal of Stem Cells BioMed Genomics Zoology International Research International Hindawi Hindawi Hindawi Hindawi Hindawi www.hindawi.com Volume 2018 Virology www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018

Neuroscience Journal

Enzyme Journal of Journal of Research Parasitology Research Marine Biology Archaea Hindawi Hindawi Hindawi Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018