Related Circular RNA Signature

294

Original Article Page 1 of 12

Identification and integrated analysis of hepatocellular carcinoma- related circular RNA signature

Feiran Wang1#, Xiaodong Xu2#, Nannan Zhang1, Zhong Chen1

1Department of General Surgery, Affiliated Hospital of Nantong University, Nantong 226000, China; 2Department of General Surgery, the 4th Affiliated Hospital of Nantong University, Yancheng 224000, China Contributions: (I) Conception and design: Z Chen, F Wang; (II) Administrative support: Z Chen; (III) Provision of study materials or patients: F Wang, X Xu; (IV) Collection and assembly of data: X Xu, N Zhang; (V) Data analysis and interpretation: N Zhang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #These authors contributed equally to this work. Correspondence to: Professor Zhong Chen. Department of Hepatobiliary Surgery, Affiliated Hospital of Nantong University, and Research Institute of Hepatobiliary Surgery of Nantong University, 20 Xisi Road, Nantong 226001, China. Email: [email protected].

Background: Circular RNAs (circRNAs), a novel type of non-coding RNA, play a vital role in the pathogenesis and development of cancer. CircRNAs signatures may be useful as prognostic and predictive factors as well as clinical tools for evaluating disease status and prognosis. This study was carried out to explore novel circRNA signatures in hepatocellular carcinoma (HCC). Methods: The expression profiles of circRNAs were retrieved from the Gene Expression Omnibus (GEO). The expression profiles of miRNAs and mRNAs were obtained from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. The results of the microarray were validated by quantitative real-time RCR (qPCR). Based on circRNA-miRNA pairs and miRNA-mRNA pairs, a competitive endogenous RNA (ceRNA) network was constructed. Functional analysis was performed via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA). Furthermore, survival analysis was carried out using the Kaplan-Meier curve and the log-rank test. Results: Differentially expressed circRNAs in HCC from GEO databases (GSE94508 and GSE97332) were screened and analyzed using the bioinformatics method. We detected a total of 26 differentially expressed circRNAs by qPCR and then selected 6 circRNAs to construct the circRNA-miRNA-mRNA networks. Through prognostic analysis, 3 target hub genes (AURKA, KIF5B, and RHOA) of circRNAs were discovered. Moreover, GSEA and GSVA were used to reveal the functions of AURKA, KIF5B, and RHOA in HCC. Conclusions: We identified three hub genes, and our results suggest that the circHMGCS1/miR-581/ AURKA, circHMGCS1/miR-892a/KIF5B, and circTMCO3/miR-577/RHOA axes may play a vital role in HCC progression.

Keywords: CircRNAs; hepatocellular carcinoma (HCC); competing for endogenous RNA (ceRNA); bioinformatic analysis

Submitted Nov 22, 2019. Accepted for publication Feb 05, 2020. doi: 10.21037/atm.2020.03.06 View this article at: http://dx.doi.org/10.21037/atm.2020.03.06

Introduction in the world (1). Owing to the lack of early diagnostic

Hepatocellular carcinoma (HCC) is the fifth most common biomarkers with high specificity and sensitivity and the malignancy and the main cause of cancer-related death high frequency of tumor metastasis, the overall survival of

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2020;8(6):294 | http://dx.doi.org/10.21037/atm.2020.03.06 Page 2 of 12 Wang et al. HCC-related circular RNA signature patients with HCC is very poor (2). Therefore, identifying were collected from the GEO database (GSE94508 and novel biomarkers and therapeutic targets is of great GSE97332). RNA-sequencing (RNA-seq) data were significance in improving the diagnosis and treatment of downloaded from the TCGA database. miRNA sequencing HCC and could lead to a better understanding of the data included 375 liver cancer tissues and 50 normal tissues, pathology of HCC. and the mRNA sequencing data included 374 liver cancer Accumulating evidence has shown that the dysregulation tissues and 160 normal tissues. We used the Limma R of circRNAs may contribute to tumorigenesis and cancer package to explore the significantly differentially expressed progression in humans (3). CircRNA is a newly discovered genes between tumorous and normal tissues. The cut-off non-coding RNA with a special structure, which is value was |log2FC| >1 and FDR <0.05 (FC, fold change; characterized by a covalent closed-loop without 5’ caps or 3’ FDR, false discovery rate). Due to the public availability of tails (4). In recent years, numerous pieces of evidence have data on the GEO and TCGA databases, ethical approval or revealed that circRNAs could be involved in gene regulation informed consent was not required for this study. by acting as miRNA sponges, regulating splicing and transcription, or interacting with RNA-binding proteins Cell culture and validation of overlapping circRNAs with (RBPs) (5). These findings suggest the potential regulatory real-time Quantitative PCR role of circRNAs in biologic development and the pathogenesis and progression of diseases, especially types SMMC-7721 cells were cultured in Dulbecco’s Modified of cancer (6). Aberrant expression of circRNA also played Eagle Medium (DMEM, Gibco) supplemented with 10% an important role in the initiation and development of fetal bovine serum (FBS, Gibco) at 37 in a 5% CO2 HCC (7). Huang , for example, found that atmosphere in a humidified incubator. et al. ℃ the activation of the mTOR signaling pathway by Total RNAs from SMMC-7721 cells were extracted circRNA-100338/miR-141-3p/RHEB axis is closely using TRIzol (Invitrogen) according to the manufacturer’s associated with poor prognosis of hepatitis B-related protocol. cDNA was synthesized from total RNA using HCC (8). Another study reported that cicrRNA_101368 M-MLV RT (Promega). Then real-time qPCR was applied could function as a sponge of miR-200a to inhibit miR- with SYBR Master Mixture (Takara) on the LightCycler 200a expression, thus promoting the migration of HCC 480 II (Roche) according to standard protocols. The cells through HMGB1/RAGE signaling of miR-200a primer sequences for GAPDH acted as the control in these downstream (9). experiments. Primers used for detecting circRNAs were In this study, we analyzed the expression profiles of synthesized from GeneChem (Shanghai). Primer sequences circRNAs, miRNAs, and mRNAs of HCC downloaded were listed in Table 1. The quantity of each mRNA −ΔΔCt from The Cancer Genome Atlas (TCGA) and the Gene was calculated using the 2 method, and data were Expression Omnibus (GEO). Differentially expressed that normalized using GAPDH as the loading control. circRNAs (DEcircRNAs) were identified and validated. After predicting the sponging of miRNAs by circRNA and Predictions of target miRNA and miRNA target genes miRNA target genes, we established a circRNA-miRNA- The Circular RNA Interactome (CircInteractome) (10) and mRNA network and a circRNA-miRNA-hub gene network Cancer-Specific CircRNA (CSCD) (11) were used to predict for HCC. To predict the possible mechanisms and function target miRNAs and their miRNA binding sites (MREs). of circRNAs in HCC, we also performed functional These target miRNAs were further selected by DEmiRNA enrichment analyses. These results could improve our based on TCGA, and the screened miRNAs were identified understanding of the regulatory mechanisms of circRNAs as potential target miRNAs of the DEcircRNAs. in the pathogenesis and progression of HCC and provide Interactions between miRNA and mRNA were predicted novel therapeutic targets or biomarkers for HCC. based on the TargetScan (12), miRTarBase (13), and miRDB (14) databases. Only the target mRNAs recognized Methods by the three databases were considered as candidate mRNAs. Moreover, intersections with the DEmRNAs Microarray data and RNA sequencing data were identified to screen out the hub genes targeted by the The microarray data of two circRNA expression profiles DEmiRNAs.

Table 1 Primer sequences of circRNAs used for qPCR Gene Forward primer (5’-3’) Reverse primer (5’-3’)

circRNA(hsa_circ_0004913) TCACTCTGACGGAACTTGACA GCTCCTTCTGCTTTGGCTGT

circRNA(hsa_circ_0006248) AAAGACCACATACGCCAACA TCAGCCCAAATACTCCAAGA

circRNA(hsa_circ_0038929) CATGCCTTGGGACCTGTTAA AGCAAAGTTGAGTGCGGAGA

circRNA(hsa_circ_0005428) AACAGCTCTGGCAAGAAACT TTTGATGGAGGAAAGTGGAA

circRNA(hsa_circ_0006913) AACCCAAGAAACTGCTGTCG GAAATCCCTGATGTGCTCACT

circRNA(hsa_circ_0007248) TTCTGCGGGCATTGGTGATA CTCCCTGAGCATTGCATTGGAC

circRNA(hsa_circ_0059342) GGGACCTCAAATCAAAGAAA TCCTGAAGTTGTAATGTGGG

circRNA(hsa_circ_0007762) GGGACCTCAAATCAAAGAAA GCCCACATAGAGCCACTTAC

circRNA(hsa_circ_0059859) AGGTGCGGACTCTTGTTCTT GAGTGCAGATGATGTGGAGG

circRNA(hsa_circ_0056548) AGGAGGAGAAGTTGGAGGCT GAGGCAATGGAGTTAAAGGACA

circRNA(hsa_circ_0043302) TTGACGCCTTAGATAACAGC ATTCAATCCAGGATGCAAAA

circRNA(hsa_circ_0055033) AAGAAGTCCTGCATCGAAAT CACATGATCCTTACTGTCCG

circRNA(hsa_circ_0001359) CCTGGAATCACGAAGCACAG TGGGTAATACTGCCGCTGGT

circRNA(hsa_circ_0067323) ATCCACAACTGATGCACCTA TGCTTCAACAGTGACTACGC

circRNA(hsa_circ_0001360) GTTGTCTCGTCGTCATCGTC GCTTATTAACTGTGCAGGTGT

circRNA(hsa_circ_0000204) CCTCCTAGAAGTAATGCCCACA CAGCACCTCACTGTCAAAGAAT

circRNA(hsa_circ_0009581) CGACTGTGACCTCCTTATGT ACTGTTCAGCCTCCTTGTCT

circRNA(hsa_circ_0072389) TGGCTGCCACTCTGTACTCT TTCCAGCATCTACACCATCA

circRNA(hsa_circ_0007540) CAGCAGTGGAGGCAGAAGTT AAACCAAGGGATGGCATAGA

circRNA(hsa_circ_0027774) TGTGCTGGAATTGATGTTCG CTGAGGGAGGGTCTGTTTGA

circRNA(hsa_circ_0000517) TGAGCTTCGGGGAGGTGAGTT GGGAGAGCCCTGTTAGGGC

circRNA(hsa_circ_0030130) CTGCCTGTAAGTGCCAAGT GAGGCCAGGGTAGTCCATT

circRNA(hsa_circ_0084429) GGCAACTGTATGAGCCACT GGACTAACTCCCTCGAAAT

circRNA(hsa_circ_0031027) GGACCCTATTATCGGAAGC CACCTATGGAGGCGAAGAA

circRNA(hsa_circ_0008160) GTGCTCTTGTTTGGGATTA TTTGAGGCCAATAGGTTAA

circRNA(hsa_circ_0011536) GACAATGCTCAAGTGGTGGTA TGCTGTCAAGGGTAGGAGTTA

GAPDH TGACTTCAACAGCGACACCCA CACCCTGTTGCTGTAGCCAAA

Construction of the CeRNA network annotation and KEGG (16) pathway analyses with the clusterProfiler package in R. P value <0.05 served as the The circRNA-miRNA-mRNA regulatory network was cut-off point to assess the functional pathways. constructed by using a combination of circRNA-miRNA pairs and miRNA-mRNA pairs and visualized by Cytoscape 3.6.1. Gene functional analysis

GSEA (17) is a computational method for assessing GO and KEGG pathway enrichment analysis whether a set of genes defined by a priori show statistically mRNAs in the ceRNA network were evaluated by GO (15) significant, consistent differences between two biological

Type Type Type Type 2 2 A Normal C Normal Tumor Tumor 1 1

0 0

−1 −1

−2 −2 GSM2477192 GSM2477194 GSM247 7196 GSM2477198 GSM2477200 GSM2477191 GSM2477193 GSM2477195 GSM2477197 GSM24771 99 GSM2561829 GSM2561830 GSM2561831 GSM2561832 GSM2561833 GSM2561834 GSM2561835 GSM2561836 GSM2561837 GSM2561838 GSM2561839 GSM2561840 GSM2561841 GSM2561842

GSE94508 GSE97332 6 B D 6 Volcano Volcano 4 4

2 2

0 0 logFC logFC −2 −2

−4 −4

−6 −6 0 2 4 6 8 10 0 2 4 6 8 10 -log10 (adj.P.Val) -log10 (adj.P.Val)

Figure 1 Heat maps and volcano plots for differentially expressed circRNAs in LIHC based on GSE94508 and GSE97332 from the GEO database. Red represents up-regulated circRNAs and green represents down-regulated circRNAs. Black represents circRNAs without statistically significant change (|log2[fold change]| >1 and FDR <0.05). states. GSEA was performed to analyze the enrichment of GSE97332) were downloaded from the GEO database, datasets between high- and low-expression groups of hub and the DEcircRNAs were generated by using the limma genes. A false discovery rate (FDR) <25% and nominal R package. A total of 270 DEcircRNAs (25 up-regulated P value <5% were set as the cut-off criteria. In addition, and 245 down-regulated) were identified in the GSE94508 GSVA (18) was used to find the most relevant pathways of dataset (Figure 1A,B). A total of 869 DEcircRNAs (421 hub genes, according to the gene sets files from the KEGG up-regulated and 448 down-regulated) were identified databases. in the GSE97332 dataset (Figure 1C,D). We found that 26 overlapping DEcircRNAs were obtained from two Results datasets, and these were chosen for further validation. The expressions of these DEcircRNAs in SMMC-7721 Identification of differentially expressed circRNAs and cells were detected using qPCR. Among them, the mRNA miRNAs expression abundance of six DEcircRNAs was validated to Two circRNA microarray datasets (GSE94508 and be up-regulated in SMMC-7721 cells (Figure 2), indicating

Type Type A GEO Normal B 12 Tumor hsa_circ_0001360 GEO hsa_circ_0000204 10 GSE94508 hsa_circ_0027774 GSE97332 8 hsa_circ_0059859

hsa__circ_0009581 6

hsa__circ_0001359

hsa_circ_0031027 25.0 hsa__circ_0011536

hsa__circ_0030130

hsa__circ_0005428 20.0

hsa__circ_0056548

hsa_circ_0008160 15.0 hsa__circ_0055033

hsa__circ_0000517 10.0 hsa__circ_0006913

hsa_circ_0007540 ACt (Target gene-GAPDH) ACt (Target Δ

hsa_circ_0072389 5.0

hsa_circ_0084429

hsa__circ_0004913 0.0 hsa_circ_0004913 hsa_circ_0072389 hsa_circ_0000517 hsa_circ_0031027 hsa_circ_0008160 hsa_circ_0011536 hsa__circ_0007248

hsa_circ_0059342

hsa_circ_0067323

hsa__circ_0006248

hsa__circ_0043302

hsa__circ__0038929

hsa_circ_0007762 GSM2561829 GSM2561830 GSM2561831 GSM2561832 GSM2561833 GSM2561834 GSM2561835 GSM2561836 GSM2561837 GSM2561838 GSM2561839 GSM2561840 GSM2561841 GSM2561842 GSM2477192 GSM2477194 GSM2477196 GSM2477198 GSM2477200 GSM2477191 GSM2477193 GSM2477195 GSM2477197 GSM2477199

Figure 2 Differentially expressed circRNAs in the two microarray datasets. (A) Heat map of overlapping differentially expressed circRNAs in LIHC based on the GSE94508 and GSE97332 from the GEO database; (B) histogram of mRNA expression abundance of the six target circRNAs in SMMC-7721 cell lines by real-time quantitative PCR (Y-axis is ΔCt).

Table 2 Basic characters of the six hub circRNAs circRNA ID Position Genomic length Strand Best transcript Gene symbol

hsa_circ_0004913 chr17:62248459-62265775 17,316 − NM_018469 TEX2

hsa_circ_0072389 chr5:43294157-43299077 4,920 − NM_001098272 HMGCS1

hsa_circ_0000517 chr14:20811404-20811492 88 − NR_002312 RPPH1

hsa_circ_0031027 chr13:114164552-114193822 29,270 + NM_017905 TMCO3

hsa_circ_0008160 chr21:38439561-38441924 2,363 − NR_028352 PIGP

hsa_circ_0011536 chr1:35824525-35827390 2,865 + NM_005095 ZMYM4

that they were appropriate for further research. Moreover, The construction of the ceRNA network and functional the basic characteristics of the six DEcircRNAs are listed in assessment Table 2. Their basic structural patterns are shown in Figure 3 based on the data from CSCD. To better comprehend the role of circRNA in miRNA Based on the TCGA-LIHC database, we obtained 251 mediated mRNA, we constructed a circRNA-miRNA- differentially expressed miRNAs (DEmiRNAs, 229 up- mRNA (ceRNA) network. Firstly, using the CircInteractome regulated and 22 down-regulated) using edgeR package database, we predicted the miRNAs targeted by the 6 analysis with |log2FC| >1 and FDR <0.05 (Figure S1). DEcircRNAs and identified 100 circRNAs-miRNAs pairs.

MRE (microRNA response element) RBP (RNA binding protein) ORF (open reading frame) A B C

hsa_circ_0004913 hsa_circ_0072389 hsa_circ_0000517

D E F

hsa_cire_0031027 hsa_circ_0008160 hsa_circ_0011536

Figure 3 Structural graphs of the six circRNAs. Red spots represent miRNA response elements, blue spots represent RNA binding protein, and green curves represent the open reading frame.

After intersecting with the DEmiRNAs, only 13 circRNA- cytotoxicity, base excision repair, and cell cycle) (Figure 5E). miRNA pairs remained, including 6 circRNAs and 11 DEmiRNAs. We then identified mRNAs targeted by the The construction of the CircRNA-miRNA-hub gene 11 DEmiRNAs in 3 databases (miRDB, miRTarBase, and network and functional enrichment analysis of hub genes TargetScan). Lastly, we established a ceRNA network based on 6 circRNA nodes, 11 miRNA nodes, and 114 mRNA Based on the thresholds of |log2FC| >1 and FDR <0.05, nodes in HCC (Figure 4). we obtained a total of 2,135 differentially expressed To achieve a better understanding of the underlying mRNAs (DEGs) from the TCGA-LIHC and GTEx biological processes (BP) and pathways associated with the databases (Figure S2). The above results showed that circRNAs-related target genes in the ceRNA network, we 7 DEGs were involved in the ceRNA network. Then, applied the GO and KEGG pathway enrichment analyses. to further explore the functions of these hub genes in The results of BP, cellular component (CC), molecular the network in carcinogenesis and the development of function (MF), and KEGG pathways are indicated in HCC, we also established a CircRNA-miRNA-hub gene Figure 5A,B,C,D (P<0.05). In addition, GSVA further regulatory network. Firstly, we presented 106 circRNAs- confirmed that a high level of circRNAs-related target related target genes and 2,135 DEGs in TCGA and genes was significantly enriched in 9 KEGG terms (thyroid GTEx and generated 7 common target genes DEGs cancer, pathogenic Escherichia coli infection, hypertrophic (CITED2, ACSL4, MARCKS, KIF5B, AURKA, SMO, cardiomyopathy hcm, lysosome, hedgehog signaling and RHOA) using Venn diagram analysis (Figure 6A). pathway, oocyte meiosis, natural killer cell-mediated Then, Cytoscape was used to construct the circRNA-

LRRFIP2 CLIC4

ATRNL1

G3BP1 CLEC12B GXYLT2 PGRMC1

CCNE2 SF1 TMEM98 RHOA DNAJB9 TSNAX

RNF138 HNRNPU PARP1 FKBP14 IGF2BP1 DDX5 STARD3NL

SERTAD3 NOL11 FKBP1A

INTS2 CLIP4 miR-577 TPM3 USP44 LRRC8A SLC10A7 miR-1276 DPP6 PTGS1

hsa_circ_0008160 hsa_circ_0031027 GPBP1 miR-184 GJC2

miR-346 VANGL2 LAMP2 hsa_circ_0004913

hsa_circ_0011536 CRELD1 miR-599

TBL1XR1 ABI2 hsa_ circ_0000517

miR-182 SLC27A4 THBS1 miR-326 UBE4A FBXW7 hsa_circ_0072389 PLXDC2 FLOT1 TP53INP1 BRWD1 miR-527 EPHB3 USH1G FOXF2 NUFIP2 CLOCK HOXA9 CYLD CHL1 DCAF7 miR-1258 ADCY6 CITED2 RECK miR-892a KIAA1468 BDNF NUP50 SMO EVI5 PLEKHA8 ULBP2 TMED5 SESN2 NPTX1 LSM14A NHLRC3 NR3C1 MITF RARG MTSS1 PRUNE2 miR-581 TGOLN2 ENPP5 TCEAL7 FGF9 BCL11B FOXO3 ZBTB4 RHOB RAB14 PRKAA2 KDM5A UBE2D2 RAB8B UBE2A PAFAH1B2 ERMP1 ACSL4 GIMAP1 CEP350 ZMAT3 PHLDA3 MARCKS YWHAZ PLAGL2 SNX5 MRPL17 KLHL23 ZNF805 AURKA AKAP10 GAS7 KLHL15 FZD5

KIF5B NUS1 RAB23 LHFPL2 DICER1

Figure 4 The ceRNA network of circRNA-miRNA-mRNA in LIHC. Red represents circRNAs, green represents differentially expressed miRNA, and sky blue represents mRNA.

miRNA-hub gene networks (Figure 6B). Moreover, the AURKA, KIF5B, and RHOA in tumor tissues compared Kaplan-Meier curves, which were plotted using the GEPIA with normal tissues (Figure 6F,G,H, P<0.001). In addition, database, indicated that higher expression of AURKA, elevated expressions of KIF5B and RHOA correlated with KIF5B, and RHOA were significantly correlated with the clinical tumor stage (Figure S3, P<0.05), suggesting poorer overall survival (Figure 6C,D,E, P<0.05), however, no LIHCs with high KIF5B and RHOA expression are prone significant prognostic significance was shown in CITED2, to progress to a more advanced stage. ACSL4, MARCKS, or SMO. We further verified the To further study the potential roles of AURKA, KIF5B, expression levels of 7 hub genes using EdgeR software in and RHOA in HCC, we performed an analysis of GSEA TCGA and GTEx data and found that an up-regulation of and GSVA on the TCGA-LIHC database. The results

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2020;8(6):294 | http://dx.doi.org/10.21037/atm.2020.03.06 Page 8 of 12 Wang et al. HCC-related circular RNA signature Nomal Tumor Type 1 2 2 1 0 − − Type KEGG_PURINE_METABOLISM KEGG_MELANOGEINESIS KEGG_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION KEGG_CHEMOKINE_SIGNALING_PATHWAY KEGG_VASCULAR_SMOOTH_MUSCLE_CDNTRACTION ESCHERICHIA_COL INFECTION KEGG_PATHOGENIC KEGG_LYSOSOME KEGG_THYROID_CANCER KEGG_HYPERTROPHIC_CARDIOMYOPATHY_HCM KEGG_BASE_EXCISION_REPAIR KEGG_HEDGEHOG SIGNALING_PATHWAY KEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY KEGG_00CYTE_MEIOSIS KEGG_CELL CYCLE KEGG_PROSTATE_CANCER E (P Value) 10 4 5 6 7 1.8 1.7 1.6 1.5 1.4 (P Value) -log 3.0 2.5 2.0 10 Gene counts 15 20 25 30 -log Gene counts 20 30 Gene ratio Gene ratio 10 2 4 6 0 Cellular components 0 KEGG of circRNA-related target genes. Functional and signaling pathway analysis of these target genes in LIHC according to biological Vesicle Nucleolus Nuclear lumen Organelle lumen Organelle Insoluble fraction bounded organelle Cytoplasmic vesicle Pathways in cancer Pathways in cancer P53 signaling pathway analyses Membrane-enclosed lumen Intracellular organelle lumen Intracellular organelle Intracellular non-membrane- B D (P Value) Non-membrane-bounded organelle 10 1.8 1.7 1.6 1.5 1.4 8 10 12 14 16 (P Value) -log Gene counts 10 4.0 3.5 3.0 2.5 2.0 4 8 12 16 -log Gene counts 15 10 15 Gene ratio Gene ratio 5 0 0 5 10 Biological process Molecular function Pathway enrichment pathway activity activity apoptosis transcription Angiogenesis Celll cycle arrest metabolic process Positive regulation of Positive regulation Negative regulation of Negative regulation Wnt receptor signaling Wnt receptor Regulation of cell death Regulation of apoptosis Transcription activator Transcription Transcription regulator regulator Transcription A C Positive regulation of RNA Positive regulation Blood vessel development Cell projection organization Cell projection Figure 5 Figure was used to analyze these target genes in TCGA. processes (A), cellular components (B), molecular function (C), and KEGG pathway (D). (E) GSVA

A B hsa_circ_0072389

hsa_circ_0011536

miR-527 miR-892a

miR-581 Diff miR-182 MARCKS KIF5B ACSL4 106 Target AURKA

2135 CITED2 SMO RHOA

miR-577 miR-326

hsa_circ_0000517 hsa_circ_0031027 hsa_circ_0004913

C D E Overall Survival Overall Survival Overall Survival

1.0 Low AURKA TPM 1.0 Low KIF5B TPM 1.0 Low RHOA TPM High AURKA TPM High KIF5B TPM High RHOA TPM Logrank P=0.00022 Logrank P=0.047 Logrank P=0.046 HR(high)=1.9 HR(high)=1.4 HR(high)=1.4 0.8 p(HR)=0.00028 0.8 p(HR)=0.048 0.8 p(HR)=0.047 n(high)=181 n(high)=182. n(high)=182 n(low)=181 n(low)=182 n(low)=182 0.6 0.6 0.6

0.4 0.4 0.4 Percent survival Percent survival Percent survival Percent 0.2 0.2 0.2

0.0 0.0 0.0 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Months Months Months

F G H 30 P=5.202e−10 30 P=1.897e−05 250 P=3.641e−10

25 25 200 20 20 150 15 15 100 10 10 KIF5B expression RHOA expression AURKA expression 5 5 50

0 0 0 Normal Tumor Normal Tumor Normal Tumor Type Type Type

Figure 6 The identification of hub genes in LIHC. (A) VennDiagram analysis of circRNA-related target genes and differentially expressed genes in TCGA and GTEx; (B) the CircRNA-miRNA-hubgene network. The network consists of 5 circRNAs, 6 miRNAs, and 7 hub genes. Red represents circRNAs, blue represents miRNAs, and yellow represents differentially expressed mRNAs; (C-E) Kaplan-Meier curves for survival analyses of three hub-genes in patients with TCGA-LIHC. (C) AURKA (P=0.00022); (D) KIF5B (P=0.047); (E) RHOA (P=0.046); (F-H) the box plot showing the mRNA expression levels of hub-genes detected in the TCGA-LIHC database (P<0.05). (F) AURKA; (G) KIF5B; (H) RHOA.

A B C 0.5 0.4 0.4

KEGG_BLADDER_CANCER 0.0 KEGG_BLADDER_CANCER 0.0 KEGG_COLORECTAL_CANCER KEGG_DRUG_METABOLISM_CYTOCHROME_P450 0.0 KEGG_COLORECTAL_CANCER KEGG_ENDOMETRIAL_CANCER KEGG_ENDOMETRIAL CANCER KEGG_COMPLENENT AND_COAGULATION_CASCADES KEGG_FATTY_ACID_METAROLISM KEGG_NON_SMALL_CELL _LUNG_CANGER KEGG_ENDOMETRIAL CANCER KEGG_NON_SMALL_CELL_LUNG_OANCER KEGG_PATHWAYS_IN_CANCER KEGG_GLYCINE_SERINE_AND_THREONNE METABOLISM −0.4 KEGG_PANCREATIC_CANCER KEGG_PRIMARY_BILE_ACID_BIOSYNTHESIS KEGG_NON_SMALL_CELL_LUNG_CANCER

Enrichment Score −0.5 −0.4 KEGG_PROSTATE_CANCER KEGG_RENAL_CELL_CARCINOMA KEGG_PANCREATIC_CANCER KEGG_RENAL_CELL_CARCINOMA KEGG_RETINOL_METABOLISM KEGG_SMALL_CELL_LUNG_CANCER

Enrichment Score KEGG_STEROID_HORMONE_BIOSYNTHESIS KEGG_SMALL_CELL_LUNG_CANCER KEGG_THYROID_CANCER Enrichment Score KEGG_TRYPTOPHAN_ME TABOLISM −0.8 KEGG_THYROID_CANCER KEGG_THYROID_CANCER −0.8

High expression Low expression High expression Low expression High expression Low expression D E F group group group group group group KEGG_PANTOTHENATE_AND_COA BIOSYNTHESIS high KEGG_BLADDER_CANCER high KEGG_MISMATCH_REPAIR high KEGG_PRIMARY_BILE_ACID_BIOSYNTHESIS KEGG_CELL_CYCLE KEGG_MISMATCHL_REPAIR KEGG_FOLATE_BIOSYNTHESIS 0.5 low 0.5 low KEGG_DNA_REPLICATION 0.5 low KEGG_GLYCINE_SERINE_AND_THREONINE_METABOUISM KEGG_OOCYTE_MEIOSIS KEGG_00CYTE_MEIOSIS KEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOLISM KEGG_CELL_CYCLE KEGG_PROGESTERONE_MEDLATED_OOCYTE_MATURATION KEGG_NITROGEN_METABOLISM KEGG_UBIQUITIN_MEDIATED_PROTEOLYSIS KEGG_GLYCEROLIPID_METABOLISM KEGG_DNA_REPLICATION KEGG_P53_SIGNALING_PATHWAY KEGG_ABC_TRANSPORTERS 0 KEGG_HEDGEHOG_SIGNALING_PATHWAY 0 KEGG_BLADDER_CANCER 0 KEGG_LYSINE_DEGRADATION KEGG_ECM_RECEPTOR_JINTERACTION KEGG_NON_SMALL_CELL_LUNG_CANCER KEGG_GLYOXYLATE_AND_DICARBOXYLATE_METABOLISM KEGG_HEDGEHOG_SIGNALING_PATHWAY KEGG_ONE_CARBON POOL_BY_FOUATE KEGG_FOCAL_ADHESION KEGG_TRYPTOPHAN_METABOLISM KEGG_ECM_RECEPTOR_JINTERACTION KEGG_PATHWAYS_JIN_CANCER KEGG_VALINE_LEUCINE_AND_ISOLEUCINE_DEGRADATION KEGG_FOCAL_ADHESION KEGG_PROPANOATE_METABOLUSM −0.5 KEGG_SMALL_CELL_LUNG_CANCER −0.5 KEGG_PATHWAYS_JIN_CANCER −0.5 KEGG_HISTIDINE_METABOUSM KEGG_SMALL_CELL_LUNG_CANCER KEGG_TIGHT_JUNCTION KEGG_NEUROACTIVE_LIGAND_RECEPTOR JINTERACTION KEGG_TIGHT_JUNCTION KEGG_GLYCEROPHOSPHOLIPID_METABOUISM KEGG_ARRHYTHMOGENIC_RIGHT_VENTRICULAR_CARDIOMYOPATHY_ARVC KEGG_ARRHYTHMOGENIC_RIGHT_VENTRICULAR_CARDIOMYOPATHY_ARVC KEGG_ARACHIDONIC_ACID_METABOUISM KEGG_REGULATION_OF_ACTIN_CYTOSKELETON KEGG_REGULATION_OF_ACTIN_CYTOSKELETON KEGG_LINOLEIC_ACID_METABOUISM KEGG_GAP_JUNCTION KEGG_BUTANOATE_METABOLISM KEGG_CAP_JUNCTION KEGG_PATHOGENIC_ESCHERICHIA_COLL_JINFECTION KEGG_BETA_ALANINE_METABOUISM KEGG_PATHOGENIC_ESCHERICHIA_COLL_INFECTION KEGG_CARDIAC_MUSCLE_CONTRACTION KEGG_FATTY_ACID_METABOUISM KEGG_CARDIAC_MUSCLE_CONTRACTION KEGG_LYSOSOME KEGG_PPAR_SIGNALING_PATHWAY KEGG_VIBRIO_CHOLERAE_JINFECTION KEGG_STEROID_HORMONE_BIOSYNTHESIS KEGG_LYSOSOME KEGG_SPHINGOLIPID_METABOLISM KEGG_RETINOL_METABOUSM KEGG_VIBRIO_CHOLERAE_JINFECTION KEGG_DRUG_METABOUISM_CYTOCHROME_P450 KEGG_OTHER_,GLYCAN_DEGRADATION KEGG_ME_TABOL_ISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450 KEGG_SPHINGOLIPID_METABOUISM KEGG_EPITHELIAL_CELL_SIGNALING_JIN_HELICOBACTER_PYLORI_JINFECTION KEGG_GLYCOSYLPHOSPHATIOYLINOSITOL_GPL_ANCHOR_BIOSYNTHESIS KEGG_TYROSINE_METABOLISM KEGG_GLYCOSYLPHOSPHATIOYLINOSITOL_GPI_ANCHOR_BIOSYNTHESIS KEGG_GLYCOLYSIS_GLUCONEOGENESIS KEGG_NOTCHL_SIGNALING_PATHWAY KEGG_CITRATE_CYCLE_TCA_CYCLE KEGG_NOTCH_SIGNALING_PATHWAY KEGG_INTESTINAL_IMMUNE_NE_TWORK_FOR_IGA_PRODUCTION KEGG_COMPLEMENT_AND_COAGULATIONL_CASCADES KEGG_SNARE_JINTERACTIONS_JN_VESICULAR_TRANSPORT KEGG_LEISHMANIA_JINFECTION KEGG_CIRCADIAN_RHYTHML_MAMMAL KEGG_ASTHMA KEGG_INTESTINAL_JIMMUNE_NE_TWORK_FOR_JGA_PRODUCTION KEGG_TAURINE_AND_HYPOTAURINE_METABOLISM KEGG_HEMATOPOIETIC_CELL_LINEAGE KEGG_SELENOAMINO_ACID_METABOLISM KEGG_LEISHMANIA_JINFECTION KEGG_TYPE_L_DABETES_MELLITUS KEGG_HOMOLOGOUS_RECOMBINATION KEGG_ASTHMA KEGG_GRAFT_VERSUS_HOST_DISEASE KEGG_MTOR_SIGNALING_PATHWAY KEGG_AUTOIMMUNE_THYROID_DISEASE KEGG_REGULATION_OF_AUTOPHAGY KEGG_HEMATOPOIETIC_CELL_LINEAGE KEGG_ALLOGRAFT_REJECTION KEGG_AMINO_SUGAR_AND_NUCLEOTIDE_SUGAR_METABOUISM KEGG_TYPE_L_DIABETES_MELUITUS KEGG_ANTIGEN_PROCESSING_AND_PRESENTATION KEGG_NOD_LIKE_RECEPTOR_SIGNALING_PATHWAY KEGG_NICOTINATE_AND_NICOTINAMIDE_METABOUISM KEGG_GRAFT_VERSUS_HOST_DISEASE KEGG_CELL_ADHESION_MOLECULES_CAMS KEGG_VIRAL_MYOCARDITIS KEGG_INSULIN_SIGNALING_PATHWAY KEGG_AUTOIMMUNE_THYROID_DISEASE KEGG_JAK_,STAT_SIGNALING_PATHWAY KEGG_SNARE_JINTERACTIONS_IN_VESICULAR_TRANSPORT KEGG_B_CELL_RECEPTORL_SIGNALING_PATHWAY KEGG_ALLOGRAFT_REJECTION KEGG_PENTOSE_PHOSPHATE_PATHWAY KEGG_INTESTINAL_JIMMUNE_NETWORK_FOR_JIGA_PRODUCTION KEGG_ANTIGEN_PROCESSING_AND_PRESENTATION KEGG_PANTOTHENATE_AND_COA_BIOSYNTHESIS KEGG_LEISHMANLA_JINFECTION KEGG_ARACHIDONIC_ACID_METABOLISM KEGG_CELL_ADHESION_MOLECULES_CAMS KEGG_ASTHMA KEGG_LINOLEIC_ACID_METABOUISM KEGG_HEMATOPOIETIC_CELL_LINEAGE KEGG_VIRAL_MYOCARDITIS KEGG_RE_TINOL_METABOLISM KEGG_TYPE_1_DIABETES_MELLITUS KEGG_HOMOLOGOUS_RECOMBINATION KEGG_DRUG_METABOLISM_CYTOCHROME_P450 KEGG_GRAFT_VERSUS_HOST_DISEASE KEGG_METABOUISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450 KEGG_AUTOIMIMUNE_THYROID_DISEASE KEGG_PROTEASOME KEGG_TYROSINE_METABOLISM KEGG_ALLOGRAFT_RE_JECTION KEGG_RIBOSOME KEGG_GLYCOLYSIS_GLUCONEOGENESIS KEGG_ANTIGEN_PROCESSING_AND_PRESENTATION KEGG_LYSINE_DEGRADATION KEGG_CELL_ADHESION_MOL_ECULES_CAMS KEGG_SELENOAMINO_ACIO_METABOUISM KEGG_BETA_ALANINE_METABOLISM KEGG_VIRAL_MYOCARDITIS KEGG_TAURINE_AND_HYPOTAURINE_METABOLISM KEGG_PENTOSE_PHOSPHATE_PATHWAY KEGG_FATTY_ACID_METABOLSM KEGG_SNARE_JINTERACTIONS_JIN_VE_SICULAR_TRANSPORT KEGG_AMINO_SUGAR_AND_NUCLEOTIDE_SUGAR_METABOLISM KEGG_,PPAR_SIGNALING_PATHWAY KEGG_JINOSITOL_PHOSPHATE_METABOLISM KEGG_NICOTINATE_AND_NICOTINAMIDE_METABOLISM KEGG_ABC_TRANSPORTERS KEGG_ETHER_LIPID_METABOLISM KEGG_GLYOXYLATE_AND_DICARBOXYLATE_METABOLISM KEGG_MTOR_SIGNALING_PATHWAY KEGG_PROTEIN_EXPORT KEGG_ONE_CARBON_POOL_BY_FOLATE KEGG_HEDGEHOG_SIGNALING_PATHWAY KEGG_REGULATION_OF_AUTOPHAGY KEGG_ALPHA_LINOLENIC_ACID_METABOLISM KEGG_MISMATCH_REPAIR KEGG_PANTOTHENATE_AND_COA_BIOSYNTHESIS KEGG_FOLATE_BIOSYNTHESIS KEGG_CELL_CYCLE KEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOUISM KEGG_GLYOXYLATE_AND_DICARBOXYUATE_METABOLISM KEGG_ONA_REPUICATION KEGG_NITROGEN_METABOLISM KEGG_00CYTE_MEIOSIS KEGG_ONE_CARBON_POOL_BY_FOLATE KEGG_GLYCE_ROLIPID_METABOLISM KEGG_PROGESTE_RONE_MEDIATED_00CYTE_MATURATION KEGG_NEUROACTIVE_LIGAND_RECEPTOR_JINTERACTION KEGG_P53_SIGNALING_PATHWAY KEGG_ABC_TRANSPORTERS KEGG_GLYCEROPHOSPHOLIPID_METABOLISM KEGG_UBIQUITIN_MEDIATED_PROTEOLYSIS KEGG_TYROSINE_METABOLISM KEGG_TRYPTOPHAN_METABOLSM KEGG_BLADDER_CANCER KEGG_DRUG_METABOLISML_CYTOCHROME_P450 KEGG_NON_SMALL_CELL_LUNG_CANCER KEGG_VALINE_LEUCINE_AND_JISOLEUCINE_DEGRADATION KEGG_ECM_RECEPTOR_JINTERACTION KEGG_RETINOL_METABOLISM KEGG_PROPANOATE_METABOLISM KEGG_PATHWAYS_JIN_CANCER KEGG_GLYCINE_SERINE_AND_THREONINE_METABOLISM KEGG_HISTIDINE_METABOLISM KEGG_SMALL_CELL_LUNG_CANCER KEGG_HISTIDINE_METABOLISM KEGG_TIGHT_JUNCTION KEGG_NEUROACTIVE_LIGAND_RECEPTOR_JINTERACTION KEGG_ARGININE_AND_PROLINE_METABOUISM KEGG_ARRHYTHMOGENIC_RIGHT_VENTRICUL_AR_CAROIOMYOPATHY_ARVC KEGG_VALINE_LEUCINE_AND_JSOLEUCINE_DEGRADATION KEGG_CIRCADIAN_RHYTHM_MAMMAL KEGG_GAP_JUNCTION KEGG_HOMOLOGOUS_RECOMBINATION KEGG_PROPANOATE_METABOUISM KEGG_REGUL_ATION_OF_ACTIN_CYTOSKELE_TON KEGG_RIBOSOME KEGG_PATHOGENIC_ESCHE_RICHIA_COLL_JINFECTION KEGG_TRYPTOPHAN_METABOUSM KEGG_SELENOAMINO_ACID_METABOLISM KEGG_CAROLAC_MUSCLE_CONTRACTION KEGG_TAURINE_AND_JHYPOTAURINE_METABOLISM KEGG_LYSOSOME KEGG_ARACHIDONIC_ACID_METABOLISM KEGG_AMINO_SUGAR_AND_NUCLEOTIDE_SUGAR_METABOUISM KEGG_VIBRIO_CHOLERAE_JINFECTION KEGG_FOLATE_BIOSYNTHESIS KEGG_NICOTINATE_AND_NICOTINAMIDE_METABOLISM KEGG_SPHINGOLIPID_METABOLISM KEGG_GLYCEROLIPID_METABOLISM KEGG_INSULIN_SIGNALNG_PATHWAY KEGG_GLYCOSYLPHOSPHATIOYLINOSITOL_GPL_ANCHOR_BIOSYNTHESIS KEGG_PYRIMIDINE_METABOLISM KEGG_GLYCINE_SERINE_AND_THREONINE_METABOLISM KEGG_JAK_STAT_SIGNALING_PATHWAY KEGG_BASE_EXCISION_REPAIR KEGG_B_CELL_RECEPTOR_SIGNALING_PATHWAY KEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOUSM KEGG_AMINOACYL_TRNA_BIOSYNTHESIS KEGG_MTOR_SIGNALING_PATHWAY KEGG_RNA_DEGRADATION KEGG_NITROGEN_METABOUISM KEGG_REGULATION_OF_AUTOPHAGY

Figure 7 The functional analysis of hub genes in the TCGA-LIHC dataset. (A-C) GSEA applied to validate the hub gene signatures. (A) AURKA; (B) KIF5B; (C) RHOA; (D-F) GSVA-derived clustering heat maps of differentially expressed pathways for single hub genes. (D) AURKA; (E) KIF5B; (F) RHOA.

of GSEA showed that the hub genes with the highest participation of circRNAs in HCC progression remain elusive. enrichment score were closely associated with signaling To explore the molecular regulatory mechanisms pathways related to cancer (Figure 7A,B,C). Furthermore, of HCC, we studied circRNA microarray profiles and GSVA confirmed that many cell cycle-related KEGG identified 26 DEcircRNAs in HCC tissues and paired pathways, including DNA replication and mismatch repair, normal tissues. Then, qPCR was applied to detect the were enriched in the groups with high-expression of mRNA expression abundance of the 26 DEcircRNAs in AURKA, KIF5B, and RHOA, further suggesting that the SMMC-7721 cell lines. Finally, hsa_circ_0004913, hsa_ activation of these hallmark genes might participate in the circ_0072389, hsa_circ_0000517, hsa_circ_0031027), hsa_ process of tumor proliferation (Figure 7D,E,F). circ_0008160, and hsa_circ_0011536 were confirmed by qPCR to be significantly dysregulated in HCC cells. In addition, studies have indicated that circRNAs could act as Discussion promising biomarkers for cancer diagnosis (23). Therefore, Over recent decades, studies have emerged, showing the our findings indicated that circTEX2 (hsa_circ_0004913), abnormal expression profiles of non-coding RNAs in HCC circHMGCS1 (hsa_circ_0072389), circRPPH1 (hsa_ (19,20). Some investigations have demonstrated circRNA circ_0000517), circTMCO3 (hsa_circ_0031027), circPIGP to be closely associated with the occurrence of HCC and (hsa_circ_0008160), and circZMYM4 (hsa_circ_0011536) its dysfunction to inhibit tumor growth and metastasis could represent potentially valuable diagnostic biomarkers (21,22). However, the molecular mechanisms underlying the for HCC treatment. The previous study demonstrated

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2020;8(6):294 | http://dx.doi.org/10.21037/atm.2020.03.06 Annals of Translational Medicine, Vol 8, No 6 March 2020 Page 11 of 12 that circRNAs had been deemed to possess the function Acknowledgments of miRNA sponges, which can suppress the binding of Funding: This study was supported by the clinical medical miRNAs to target genes, regulating their expression (24). science and technology of Jiangsu Technical Department Therefore, we proposed the hypothesis that the circRNA- (BL2014060) and the “Medical Leading Talent and miRNA-mRNA axis might be the possible molecular Innovation Team” project (LJ201134) of Jiangsu Province. regulatory mechanism underlying HCC. In this study, we constructed a ceRNA network based on the above 6 circRNAs and identified 11 DEmiRNAs and 114 mRNAs Footnote in this network. Through GO and KEGG pathway analysis, Conflicts of Interest: The authors have no conflicts of interest these mRNAs were suggested to participate in many crucial to declare. cancer-related biological functions and pathways. To further identify the crucial circRNAs involving in the : The authors are accountable for all network, we screened 7 hub genes, and thereby established Ethical Statement the circRNA-miRNA-hub gene network. Among this aspects of the work in ensuring that questions related network, several miRNAs have been identified to play vital to the accuracy or integrity of any part of the work are roles in the development and progression of HCC, such as appropriately investigated and resolved. miR-577 (25), miR-326 (26), miR-182 (27), miR-892a (28), and miR-581 (29). Moreover, using the TCGA and GTEx Open Access Statement: This is an Open Access article databases, we detected the expression of 7 hub genes and distributed in accordance with the Creative Commons found that the expression levels of AURKA, KIF5B, and Attribution-NonCommercial-NoDerivs 4.0 International RHOA were elevated in tumor tissues compared with License (CC BY-NC-ND 4.0), which permits the non- normal tissues and that higher expression of these genes was commercial replication and distribution of the article with dramatically associated with poorer overall survival. Previous the strict proviso that no changes or edits are made and the studies have shown that circRNAs could regulate host original work is properly cited (including links to both the gene expression or work as a miRNA sponge, regulating formal publication through the relevant DOI and the license). miRNA-mediated gene expression and thus leading to See: https://creativecommons.org/licenses/by-nc-nd/4.0/. tumor progression and development (30). In this study, we investigated DEcircRNAs in HCC and then selected the References DEcircRNAs that had host genes that were differentially expressed in TCGA-LIHC data and correlated with the 1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer prognosis as a candidate circRNAs. Following this, we statistics 2018: GLOBOCAN estimates of incidence and speculated that these candidate DEcircRNAs (circHMGCS1 mortality worldwide for 36 cancers in 185 countries. CA and circTMCO3) might participate in the pathogenesis of Cancer J Clin 2018;68:394-424. HCC. In addition, GSEA and GSVA analyses revealed that 2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. the functions of AURKA, KIF5B, and RHOA were also CA Cancer J Clin 2016;66:7-30. closely associated with cancer progression, which indicated 3. Memczak S, Jens M, Elefsinioti A, et al. Circular RNAs that the circRNAs were meaningfully associated with HCC are a large class of animal RNAs with regulatory potency. metastasis. Nature 2013;495:333-8. 4. Lasda E, Parker R. Circular RNAs: diversity of form and function. RNA 2014;20:1829-42. Conclusions 5. Li X, Yang L, Chen LL. The Biogenesis, Functions, and In conclusion, our research revealed circHMGCS1/miR- Challenges of Circular RNAs. Mol Cell 2018;71:428-42. 581/AURKA, circHMGCS1/miR-892a/KIF5B, and 6. Yin Y, Long J, He Q, et al. Emerging roles of circRNA circTMCO3/miR-577/RHOA regulatory axes, which could in formation and progression of cancer. J Cancer serve as novel biomarkers in the detection of HCC. Finally, 2019;10:5015-21. bioinformatics analysis indicated that the interaction pairs 7. Yu J, Xu QG, Wang ZG, et al. Circular RNA cSMARCA5 of the circRNAs mentioned above might be involved in the inhibits growth and metastasis in hepatocellular carcinoma. initiation and development of HCC. J Hepatol 2018;68:1214-27.

8. Huang XY, Huang ZL, Zhang PB, et al. CircRNA-100338 biomarkers and therapeutic targets. J Hepatol Is Associated With mTOR Signaling Pathway and Poor 2017;67:603-18. Prognosis in Hepatocellular Carcinoma. Front Oncol 20. O'Brien A, Zhou T, Tan C, Alpini G, Glaser S. Role of 2019;9:392. Non-Coding RNAs in the Progression of Liver Cancer: 9. Li S, Gu H, Huang Y, et al. Circular RNA 101368/miR- Evidence from Experimental Models. Cancers (Basel) 200a axis modulates the migration of hepatocellular 2019. doi: 10.3390/cancers11111652. carcinoma through HMGB1/RAGE signaling. Cell Cycle 21. Han D, Li J, Wang H, et al. Circular RNA circMTO1 acts 2018;17:2349-59. as the sponge of microRNA-9 to suppress hepatocellular 10. Dudekula DB, Panda AC, Grammatikakis I, et al. carcinoma progression. Hepatology 2017;66:1151-64. CircInteractome: A web tool for exploring circular RNAs 22. Chen Q, Chen Z, Cao S, et al. Role of CircRNAs_100395 and their interacting proteins and microRNAs. RNA Biol in Proliferation and Metastases of Liver Cancer. Med Sci 2016;13:34-42. Monit 2019;25:6181-92. 11. Xia S, Feng J, Chen K, et al. CSCD: a database for 23. Bach DH, Lee SK, Sood AK. Circular RNAs in Cancer. cancer-specific circular RNAs. Nucleic Acids Res Mol Ther Nucleic Acids 2019;16:118-29. 2018;46:D925-9. 24. Xiong DD, Dang YW, Lin P, et al. A circRNA-miRNA- 12. Tan LP, Seinen E, Duns G, et al. A high throughput mRNA network identification for exploring underlying experimental approach to identify miRNA targets in pathogenesis and therapy strategy of hepatocellular human cells. Nucleic Acids Res 2009;37:e137. carcinoma. J Transl Med 2018;16:220. 13. Chou CH, Shrestha S, Yang CD, et al. miRTarBase update 25. Wang LY, Li B, Jiang HH, et al. Inhibition effect of miR- 2018: a resource for experimentally validated microRNA- 577 on hepatocellular carcinoma cell growth via targeting target interactions. Nucleic Acids Res 2018;46:D296-302. β-catenin. Asian Pac J Trop Med 2015;8:923-9. 14. Wong N, Wang X. miRDB: an online resource for 26. Hu S, Ran Y, Chen W, et al. MicroRNA-326 inhibits microRNA target prediction and functional annotations. cell proliferation and invasion, activating apoptosis in Nucleic Acids Res 2015;43:D146-52. hepatocellular carcinoma by directly targeting LIM and 15. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: SH3 protein 1. Oncol Rep 2017;38:1569-78. tool for the unification of biology. The Gene Ontology 27. Cao MQ, You AB, Zhu XD, et al. miR-182-5p promotes Consortium. Nat Genet 2000;25:25-9. hepatocellular carcinoma progression by repressing 16. Kanehisa M, Furumichi M, Tanabe M, et al. KEGG: new FOXO3a. J Hematol Oncol 2018;11:12. perspectives on genomes, pathways, diseases and drugs. 28. Jia B, Tan L, Jin Z, et al. MiR-892a Promotes Nucleic Acids Res 2017;45:D353-61. Hepatocellular Carcinoma Cells Proliferation and 17. Reimand J, Isserlin R, Voisin V, et al. Pathway enrichment Invasion Through Targeting CD226. J Cell Biochem analysis and visualization of omics data using g:Profiler, 2017;118:1489-96. GSEA, Cytoscape and EnrichmentMap. Nat Protoc 29. Wang YQ, Ren YF, Song YJ, et al. MicroRNA-581 2019;14:482-517. promotes hepatitis B virus surface antigen expression 18. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set by targeting Dicer and EDEM1. Carcinogenesis variation analysis for microarray and RNA-seq data. BMC 2014;35:2127-33. Bioinformatics 2013;14:7. 30. Kristensen LS, Hansen TB, Venø MT, et al. Circular 19. Klingenberg M, Matsuda A, Diederichs S, et al. Non- RNAs in cancer: opportunities and challenges in the field. coding RNA in hepatocellular carcinoma: Mechanisms, Oncogene 2018;37:555-65.

Cite this article as: Wang F, Xu X, Zhang N, Chen Z. Identification and integrated analysis of hepatocellular carcinoma-related circular RNA signature. Ann Transl Med 2020;8(6):294. doi: 10.21037/atm.2020.03.06

Type Type 20 Normal Tumor 15

Volcano 10

Sig

FC Down 2 0 Not Up -log

−5

−10 0 20 40 60 80

-log10 (FDR) Figure S1 Heat maps and volcano plots of the differentially expressed miRNAs between LIHC tissues and normal controls in the TCGA dataset. Type 14 Type Normal 12 Tumor 10 8 6 4 2 0

Volcano 70

40 (fdr) 10 30 -log

-6 -4 -2 0 2 4 6 logFC

Figure S2 Heat maps and volcano plots of the DEGs between LIHC tissues and normal controls in the TCGA and GTEx datasets. A 25 P=0.063 B B 250 50 P=0.009 P=0.033 20 40 200 15 30 150 10 20 100 KIF5B expression 5 RHOA expression AURKA expression 10 0 50 Stage I Stage II Stage II Stage IV Stage I Stage II Stage II Stage IV Stage I Stage II Stage II Stage IV clinical clinical clinical

Figure S3 The association between the expression of hub genes and clinical stage (P<0.05). (A) AURKA; (B) KIF5B; (C) RHOA.