Screening of up‑ and downregulation of circRNAs in HBV‑related hepatocellular carcinoma by microarray

  • Authors:
    • Shichang Cui
    • Zhiling Qian
    • Yuhan Chen
    • Lei Li
    • Peng Li
    • Huiguo Ding
  • View Affiliations

  • Published online on: October 25, 2017     https://doi.org/10.3892/ol.2017.7265
  • Pages: 423-432
Metrics: HTML 0 views | PDF 0 views     Cited By (CrossRef): 0 citations

Abstract

The present study describes circular RNA (circRNA) profiles in three pairs of hepatocellular carcinoma (HCC) tissues and the corresponding adjacent non‑tumorous tissues (NTs) by microarray. circRNA is a type of endogenous RNA that serve a crucial role in disease development and aberrantly express in a number of types of cancer. In the present study, 3 paired HCC tissues and paired adjacent NTs were collected from HCC surgical specimens from 3 hepatitis B virus‑infected patients with HCC. With abundant and varied probes accounting for 5,396 circRNAs, a large number of circRNAs are able to be quantitatively determined. Based on the microarray data, 222,567,556 upregulated circRNAs and 125,439,219 downregulated circRNAs were identified respectively. Further analysis revealed 24 upregulated and 23 downregulated significantly circRNAs (fold‑change ≥2; P≤0.05) in HCC tissues compared with NTs. By means of computer analysis and database inquiring, the microRNA (miRNA) response elements associated with the abnormally expressed circRNAs were annotated. The present study showed novel evidence determining genome‑wide circRNA expression patterns in HCC using microarray analysis. The results demonstrated that clusters of circRNAs were aberrantly expressed in HCC compared with NTs. These circRNAs may be involved in the occurrence and development of HCC. Therefore, the results of the present study may provide a novel approach for improving the understanding of the molecular basis of HCC. Furthermore, the identified circRNAs may be potential biomarkers for the diagnosis of HCC.

Introduction

Hepatocellular carcinoma (HCC) is the sixth most common type of cancer worldwide, ranking fifth and eighth in males and females, respectively, and exhibits one of the highest mortality rates (1). Hepatitis B virus (HBV) and hepatitis C virus (HCV) are primary causes of HCC. Chronic HBV infection is a dominant risk factor in the majority of areas of Asia and Sub-Saharan Africa that have a high incidence of HCC (2). The majority of patients with HCC whoexperience HBV infectionexhibit cirrhosis, secondary to the chronic necroinflammation (3). HBV, an oncogenic virus, promotes HCC via indirect (necroinflammation and regeneration injury) and direct (integration of its DNA in the host genome) pathways (4). The aberrant expression of genes and regulatory RNA moleculesare key nodes for the occurrence and development of HCC.

Circular RNAs (circRNA/ciR), initially observed in RNA viruses in the 1970s, have been identified as unique non-coding RNA molecules (5). CircRNAsare a type of endogenous RNA with a stable structure and tissue-specific expression (6) and are widely present in the cytoplasm of eukaryotic organisms, in the circular form (7). CircRNA forms a covalently closed continuous loop by means of unique non-canonical ‘head-to-tail’ splice without a free 3′ or 5′ end (810). CircRNAs derive from non-linear reverse splicing or gene rearrangement and circRNAs dominate the total spliced transcripts (11). High-throughput sequencing has enabled >25,000 types of circRNAs to be discovered in human fibroblasts (12). In addition, circRNAs may be formed in exons and introns, and cirRNAs with either origin may function in the regulation of gene expression (13).

A previous study demonstrated that circRNA served a role in the level of miRNA-mediated regulation of gene expression by sequestering the miRNAs. Furthermore, circRNAs are able to regulate gene expression by acting as competing endogenous RNAs and also termed miRNA ‘sponges’ (14). CircRNAs contain multiple, tandem miRNA binding sites. CircRNAs adsorb and sequester miRNAs to terminate the suppression of their targets, and to modulate the expression levels of other associated RNA molecules which share the same miRNA response elements (MREs) (1416). The interaction between circRNAs and disease-associated miRNAs indicates that circRNAs are important for disease regulation (17).

CircRNAs serve crucial roles in the development of diseases, including nervous system disorders and atherosclerosis (18,19). In addition, circRNAs have been demonstrated to be involved in the neoplastic process (20); however, the molecular mechanisms underlying the association of circRNAs with cancer remain unclear (21).

To the best of our knowledge, a large-scale microarray screening of HCC and the focus of circRNAs as biomarkers of HCC has not been previously reported. The present study screened dysregulated circRNAs expression in HCC tissues using a microarray and annotated them for circRNA/microRNA (miRNA/miR) interactions.

Patients and methods

Patients and clinical specimens

The total three paired HCC tissues and adjacent non-tumorous tissues (NTs; Table I) were collected from HCC surgical specimens between June 2012 and December 2013 at Beijing YouAn Hospital, Capital Medical University (Beijing, China). All tissue specimens were immediately preserved in RNA-fixer reagent (BioTeke Corporation, Beijing, China) following removal from the body and were stored at −80°C until use. The corresponding adjacent NTs were taken 5 cm from the edge of the cancer and contained no obvious tumor cells, as evaluated by an experienced pathologist.

Table I.

Clinical parameters of the three patients with hepatitis B-associated HCC.

Table I.

Clinical parameters of the three patients with hepatitis B-associated HCC.

PatientSexAge, yearsHBV-DNA, IU/mlCirrhosis DifferentiationHCC stage, TNM
1M63 4.01×106YesMiddleT1N0M0
2M44 9.85×102YesPoorT3aN0M0
3F45 1.98×103YesMiddleT1N0M0

[i] HCC, hepatocellular carcinoma; HBV, hepatitis B virus; TNM, tumor-node-metastasis; M, male; F, female.

All three HCC patients were diagnosed with HBV infection. Tumors were staged according to the tumor-node-metastasis (TNM) staging system (22). The three patients were diagnosed with T1N0M0, T1N0M0, and T3aN0M0, respectively (Table I). No radiotherapy, chemotherapy or targeted therapy was administered prior to surgery.

The present study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Ethics Committee of Beijing YouAn Hospital, Capital Medical University (Beijing, China). Written informed consent was obtained from all participants.

Total RNA extraction, labeling, hybridization, and array scanning

Total tissue RNA was extracted from the HCC tissues and paired adjacent NTs using TRIzol reagent (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA), following the manufacturer's protocol. CircRNAs were treated with RNase R (Epicentre; Illumina, Inc., San Diego, CA, USA) to remove linear RNAs, according to the manufacturer's protocol. Each sample was amplified and transcribed into fluorescent complementary RNA utilizing a random priming method (Arraystar Super RNA Labeling kit; Arraystar, Inc., Rockville, MD, USA). The labeled circRNAs were hybridized onto the Arraystar Human circRNA Array (5,396 human circRNA probes; cat. no. 6×7K; Arraystar, Inc.).

The labeled circRNAs were purified using an RNeasy Mini kit (Qiagen, Inc., Valencia, CA, USA). The concentration and specific activity of the labeled circRNAs (pmol Cy3/µg circRNA) were measured using NanoDrop ND-1000 spectrophotometer (NanoDrop; Thermo Fisher Scientific, Inc., Wilmington, DE, USA). A total of 1 µg of each labeled circRNA was dispensed into the gasket slide and assembled to the circRNA expression microarray slide. The slides were incubated for 17 h at 65°C in an Agilent Hybridization Oven (Agilent Technologies, Inc., Santa Clara, CA, USA). The hybridized arrays were washed, fixed and scanned using the Axon GenePix 4000B microarray scanner (Molecular Devices, Sunnyvale, CA, USA).

Detection of expression profiling data and differentially expressed data

Scanned images were imported into GenePix Pro version 6.0 software (Axon; Molecular Devices) for grid alignment and raw data extraction. Quantile normalization of raw data and subsequent data processing were performed using the R software package (version 3.1.2; Lucent Technologies, Inc.; Nokia, Espoo, Finland). Low-intensity filtering was performed, and the circRNAs with ≥2 of the 6 samples having ‘flags expressed’ (≥2 times background standard deviation) were retained for further analysis. The analysis outputs were filtered and the differentially expressed circRNAs were ranked according to fold-change and P-value. Differentially expressed circRNAs were filtered and illustrated as a volcano plot. Hierarchical clustering was performed to reveal the distinguishable circRNAs expression pattern among samples.

Annotation for circRNA/miRNA interaction

The circRNA/miRNA interaction was predicted using Target Scan (www.targetscan.org/vert_71) and Miranda (www.microrna.org/microrna/home.do). All differentially expressed circRNAs were annotated in detail using the circRNA/miRNA interaction information.

Statistical analysis

All data were analyzed using SPSS (version 21.0; IBM Corp., Armonk, NY, USA) and all results were presented as the mean ± standard deviation. Differences between two groups were estimated using the Student's t-test, and fold-change ≥2.0 and P≤0.05 were considered to indicate a statistically significant difference.

Results

circRNA expression profiles

A total of 5,396circRNAs were scanned and the array image of each sample was demonstrated. Quantile normalization of raw data and subsequent data processing were performed using the R software package. The data demonstrated that 222,567,556 circRNAs were upregulated (fold-change ≥2) and 125,439,219 circRNAs were downregulated (fold-change ≥2; Fig. 1).

Differentially expressed circRNAs

The differentially expressed circRNAs with statistical significance between the two groups (HCC tissues group vs. NT group) were identified through using volcano plot filtering. A total of 24 upregulatedcircRNAs and 23 downregulated circRNAs were identified to be significant in HCC tissues compared with NTs (fold-change ≥2; P≤0.05; Fig. 2; Tables II and III). The top five upregulated circRNAs were hsa_circRNA_104351, hsa_circRNA_102814, hsa_circRNA_103489, hsa_circRNA_102109 and hsa_circRNA_100381. Furthermore, the top five downregulated circRNAs were hsa_circRNA_100327, hsa_circRNA_101764, hsa_circRNA_101092, hsa_circRNA_001225 and hsa_circRNA_102904.

Table II.

Significantly upregulated circRNAs in HCC vs. NTs.

Table II.

Significantly upregulated circRNAs in HCC vs. NTs.

circRNAAliasaGene symbolFCb P-valuec circRNA_typedChromosomeStrand
hsa_circRNA_103436 hsa_circ_0006884TIMMDC12.37997880.02920409Exonicchr3+
hsa_circRNA_103920 hsa_circ_0001513LNPEP2.11704740.02782662Exonicchr5+
hsa_circRNA_101842 hsa_circ_0007669SLC7A62.97406020.048252979Exonicchr16+
hsa_circRNA_104497 hsa_circ_0008419EXOC42.1081660.028206035Exonicchr7+
hsa_circRNA_103792 hsa_circ_0003037TRIO2.75980560.043619449Exonicchr5+
hsa_circRNA_102814 hsa_circ_0003789TSN11.1219250.008936648Exonicchr2+
hsa_circRNA_101169 hsa_circ_0028630RFC52.07708890.005712231Exonicchr12+
hsa_circRNA_102583 hsa_circ_0003449MEIS32.2211690.00006516883Exonicchr19
hsa_circRNA_102541 hsa_circ_0050834RYR12.27136850.045762338Exonicchr19+
hsa_circRNA_103588 hsa_circ_0068925FAM193A4.7259860.046621193Exonicchr4+
hsa_circRNA_101828 hsa_circ_0039787C16orf702.44413440.004790339Exonicchr16+
hsa_circRNA_104543 hsa_circ_0083172DNAJB62.54536670.028933433Exonicchr7+
hsa_circRNA_000596 hsa_circ_0000661ADAMTS173.13589920.028697139Antisensechr15+
hsa_circRNA_102189 hsa_circ_0006942ATP5H6.20629390.020961504Exonicchr17
hsa_circRNA_102230 hsa_circ_0046215HGS4.18683380.03517735Exonicchr17+
hsa_circRNA_100021 hsa_circ_0009456NPHP42.59351320.026262605Exonicchr1
hsa_circRNA_100381 hsa_circ_0009109DCAF66.91369010.012898864Exonicchr1+
hsa_circRNA_102109 hsa_circ_0003650KPNB17.35688550.028283407Exonicchr17+
hsa_circRNA_100770 hsa_circ_0021506ANO52.35496320.04635107Exonicchr11+
hsa_circRNA_103616 hsa_circ_0069380TBC1D194.16197160.048093616Exonicchr4+
hsa_circRNA_104351 hsa_circ_0008537GLI313.03576760.04722754Exonicchr7
hsa_circRNA_103489 hsa_circ_0067717RNF137.59963660.008247639Exonicchr3+
hsa_circRNA_102630 hsa_circ_0003287NBAS2.8669290.038026903Exonicchr2
hsa_circRNA_101539 hsa_circ_0005402ANXA22.1417760.048215125Exonicchr15

a Alias, circRNA ID in circBase (circbase.mdc-berlin.de).

b FC, fold-change, the absolute ratio (no log scale) of normalized intensities between two conditions (threshold, 2.0).

c P-value calculated using a paired t-test (threshold, 0.05).

d CircRNAs were classified into five types: Exonic, circRNA arising from the exons of the linear transcript; intronic, circRNA arising from an intron of the linear transcript. Antisense represents circRNA whose gene locus overlap with linear RNA but transcribed from the opposite strand; intragenic represents circRNA transcribed from the same gene locus as the linear transcript but not classified into ‘exonic’ and ‘intronic’; intergenic represents circRNA located outside a known gene locus. CircRNA, circular RNA; hsa, homo sapiens; chr, chromosome; +, positive strand; -, negative strand.

Table III.

Significantly downregulated circRNAs in HCC vs. NTs.

Table III.

Significantly downregulated circRNAs in HCC vs. NTs.

circRNAAliasaGene symbolFCb P-valuec circRNA_typedChromosomeStrand
hsa_circRNA_101405 hsa_circ_0003670NEK92.47419710.026208247Exonicchr14
hsa_circRNA_101764 hsa_circ_0038645PRKCB5.01184560.014366467Exonicchr16+
hsa_circRNA_104342 hsa_circ_0003162BBS92.52484420.017053943Exonicchr7+
hsa_circRNA_103627 hsa_circ_0069559APBB22.4854440.005793933Exonicchr4
hsa_circRNA_104044 hsa_circ_0075447GMDS2.39018550.030706961Exonicchr6
hsa_circRNA_102446 hsa_circ_0049356CARM13.18625850.013653006Exonicchr19+
hsa_circRNA_104865 hsa_circ_0004928LPAR13.02721780.029853247Exonicchr9
hsa_circRNA_001225 hsa_circ_0000305CELF14.48183740.032503965Intronicchr11
hsa_circRNA_100327 hsa_circ_0014022TARS27.95074280.004638981Exonicchr1+
hsa_circRNA_100147 hsa_circ_0004240EIF3I2.16476790.024721895Exonicchr1+
hsa_circRNA_000963 hsa_circ_0001644BCLAF12.42693920.025372306Intronicchr6
hsa_circRNA_103137 hsa_circ_0061817C2CD23.00631910.0279151Exonicchr21
hsa_circRNA_100904 hsa_circ_0007767ALG82.00979430.016978441Exonicchr11
hsa_circRNA_104349 hsa_circ_0079929CDK133.47633030.026028214Exonicchr7+
hsa_circRNA_102904 hsa_circ_0008459KANSL1L3.85024220.049696658Exonicchr2
hsa_circRNA_100883 hsa_circ_0005918FCHSD23.0281080.042033201Exonicchr11
hsa_circRNA_101092 hsa_circ_0008594SRGAP14.5666340.045816812Exonicchr12+
hsa_circRNA_100705 hsa_circ_0008898OAT2.45665290.001192501Exonicchr10
hsa_circRNA_103361 hsa_circ_0001296SMARCC13.500050.005363723Exonicchr3
hsa_circRNA_100395 hsa_circ_0015278KLHL202.09641020.021512811Exonicchr1+
hsa_circRNA_100748 hsa_circ_0020926STIM13.43847780.007965023Exonicchr11+
hsa_circRNA_102600 hsa_circ_0000958PPP1R12C2.20084340.027502872Exonicchr19
hsa_circRNA_102261 hsa_circ_0046534TBCD2.02513540.028926837Exonicchr17+

a Alias, circRNA ID in circBase (circbase.mdc-berlin.de).

b FC, fold-change, the absolute ratio (no log scale) of normalized intensities between two conditions (threshold, 2.0).

c P-value calculated using a paired t-test (threshold, 0.05).

d CircRNAs were classified into five types: Exonic, circRNA arising from the exons of the linear transcript; intronic, circRNA arising from an intron of the linear transcript. Antisense, represents circRNA whose gene locus overlaps with linear RNA but transcribed from the opposite strand; intragenic represents circRNA transcribed from the same gene locus as the linear transcript but not classified into ‘exonic’ and ‘intronic’; intergenic represents circRNA located outside a known gene locus. CircRNA, circular RNA; hsa, homo sapiens; chr, chromosome; +, positive strand; -, negative strand.

Annotation for circRNA/miRNA interactions

The circRNA/miRNA interaction was predicted using the miRNA target prediction software. All differentially expressed circRNAs (fold-change ≥2; P≤0.05) were annotated in detail using the circRNA/miRNA interaction information (Tables IV and V). The most upregulated circRNA, hsa_circRNA_104351, adjusts its MREs: hsa-miR-490-5p, hsa-miR-876-5p, hsa-miR-619-3p, hsa-miR-619-3p, hsa-miR-331-3p and hsa-miR-411-3p. Similarly, the most downregulated circRNA, hsa_circRNA_100327, targets the following MREs: Hsa-miR-637, hsa-miR-326, hsa-miR-330-5p, hsa-miR-646 and hsa-miR-24-3p.

Table IV.

Upregulated circRNAs annotated with circRNA/miRNA interaction information.

Table IV.

Upregulated circRNAs annotated with circRNA/miRNA interaction information.

circRNAMRE1MRE2MRE3MRE4MRE5
hsa_circRNA_103436hsa-miR-363-3phsa-miR-32-5phsa-miR-501-3phsa-miR-600hsa-miR-502-3p
hsa_circRNA_103920 hsa-miR-1298-3p hsa-miR-29b-1-5phsa-miR-452-3phsa-miR-34c-5phsa-miR-9-5p
hsa_circRNA_101842hsa-miR-489-3phsa-miR-892b hsa-miR-449c-5phsa-miR-665hsa-miR-138-5p
hsa_circRNA_104497hsa-miR-139-5phsa-miR-141-5phsa-miR-597-3phsa-miR-632hsa-miR-648
hsa_circRNA_103792hsa-miR-215-3phsa-miR-627-5phsa-miR-532-3p hsa-miR-181b-5phsa-miR-630
hsa_circRNA_102814 hsa-miR-516a-5phsa-miR-224-5phsa-miR-501-5phsa-miR-429 hsa-miR-500a-5p
hsa_circRNA_101169hsa-miR-591hsa-miR-1264 hsa-miR-517b-3p hsa-miR-517a-3phsa-miR-22-5p
hsa_circRNA_102583hsa-miR-762hsa-miR-23b-3phsa-miR-765hsa-miR-675-5phsa-miR-423-5p
hsa_circRNA_102541hsa-miR-486-3phsa-miR-328-5p hsa-miR-125a-3phsa-miR-296-5phsa-miR-873-5p
hsa_circRNA_103588hsa-miR-127-3phsa-miR-452-5phsa-miR-22-5p hsa-miR-219a-2-3phsa-miR-509-5p
hsa_circRNA_101828hsa-miR-619-3phsa-miR-877-5p hsa-miR-520f-3phsa-miR-452-5phsa-miR-490-3p
hsa_circRNA_104543 hsa-miR-196b-3p hsa-miR-1298-3phsa-miR-345-3phsa-miR-1-3p hsa-miR-1224-3p
hsa_circRNA_000596hsa-miR-647
hsa_circRNA_102189hsa-miR-588hsa-miR-659-3phsa-miR-490-3phsa-miR-152-5phsa-miR-330-3p
hsa_circRNA_102230hsa-miR-588hsa-miR-10b-3phsa-miR-657hsa-miR-150-5phsa-miR-636
hsa_circRNA_100021hsa-miR-593-5phsa-miR-93-3phsa-miR-30b-3phsa-miR-766-3phsa-miR-484
hsa_circRNA_100381hsa-miR-525-5phsa-miR-544ahsa-miR-345-3phsa-miR-577hsa-miR-587
hsa_circRNA_102109 hsa-miR-1301-3phsa-miR-20b-3phsa-miR-505-5phsa-miR-616-3phsa-miR-761
hsa_circRNA_100770 hsa-miR-19b-2-5p hsa-miR-19b-1-5phsa-miR-767-3phsa-miR-506-5p hsa-miR-550a-3p
hsa_circRNA_103616hsa-miR-629-3phsa-miR-761hsa-miR-603hsa-miR-150-5phsa-miR-186-5p
hsa_circRNA_104351hsa-miR-490-5phsa-miR-876-5phsa-miR-619-3phsa-miR-331-3phsa-miR-411-3p
hsa_circRNA_103489hsa-miR-654-3phsa-miR-511-5phsa-miR-632hsa-miR-643hsa-miR-889-5p
hsa_circRNA_102630 hsa-miR-19b-2-5phsa-miR-105-5p hsa-miR-29b-1-5phsa-miR-15b-3phsa-miR-29a-5p
hsa_circRNA_101539hsa-miR-224-5phsa-miR-182-5p hsa-miR-208a-5phsa-miR-9-5phsa-miR-33b-5p

[i] CircRNA, circular RNA; hsa, homo sapiens; MRE, miRNA response element; miR/miRNA, microRNA.

Table V.

Downregulated circRNAs annotated with circRNA-miRNA interaction information.

Table V.

Downregulated circRNAs annotated with circRNA-miRNA interaction information.

circRNAMRE1MRE2MRE3MRE4MRE5
hsa_circRNA_101405hsa-miR-646hsa-miR-452-5phsa-miR-504-5phsa-miR-661hsa-miR-383-3p
hsa_circRNA_101764 hsa-miR-181b-5p hsa-miR-181c-5p hsa-miR-181d-5p hsa-miR-181a-5phsa-miR-329-5p
hsa_circRNA_104342hsa-miR-411-5p hsa-miR-520c-5phsa-miR-526a hsa-miR-518d-5p hsa-miR-518f-5p
hsa_circRNA_103627hsa-miR-215-3phsa-miR-105-3phsa-miR-877-3phsa-miR-605-5phsa-miR-615-5p
hsa_circRNA_104044hsa-miR-188-3phsa-miR-335-3phsa-miR-580-5phsa-miR-659-5phsa-miR-139-5p
hsa_circRNA_102446hsa-miR-377-5phsa-miR-658hsa-miR-889-5phsa-miR-23b-5phsa-let-7i-5p
hsa_circRNA_104865 hsa-miR-135a-3phsa-miR-7-5phsa-miR-588hsa-miR-383-5phsa-miR-620
hsa_circRNA_001225hsa-miR-30b-3phsa-miR-887-5phsa-miR-26b-3phsa-miR-485-5phsa-miR-363-5p
hsa_circRNA_100327hsa-miR-637hsa-miR-326hsa-miR-330-5phsa-miR-646hsa-miR-24-3p
hsa_circRNA_100147 hsa-miR-219a-1-3phsa-miR-486-3phsa-miR-493-3p hsa-miR-92a-2-5phsa-miR-495-3p
hsa_circRNA_000963hsa-miR-324-3phsa-miR-214-5phsa-miR-423-3p
hsa_circRNA_103137hsa-miR-497-5p hsa-miR-487b-3phsa-miR-25-3phsa-miR-92a-3phsa-miR-92b-3p
hsa_circRNA_100904hsa-miR-627-3phsa-miR-539-5phsa-let-7i-5phsa-miR-190bhsa-miR-98-5p
hsa_circRNA_104349hsa-miR-212-5p hsa-miR-26a-1-3p hsa-miR-26a-2-3phsa-miR-639 hsa-miR-1271-3p
hsa_circRNA_102904 hsa-miR-519c-3p hsa-miR-148a-3p hsa-miR-519a-3phsa-miR-567hsa-miR-205-3p
hsa_circRNA_100883hsa-miR-217 hsa-miR-376a-2-5phsa-miR-92b-3phsa-miR-511-5phsa-miR-92a-3p
hsa_circRNA_101092hsa-miR-631hsa-miR-612hsa-miR-221-5phsa-miR-889-3p hsa-miR-1298-3p
hsa_circRNA_100705 hsa-miR-365b-3p hsa-miR-365a-3phsa-miR-670-3phsa-miR-34b-5phsa-miR-101-3p
hsa_circRNA_103361 hsa-miR-449c-3phsa-miR-582-5phsa-miR-509-3phsa-miR-510-5phsa-miR-369-5p
hsa_circRNA_100395hsa-miR-141-3phsa-miR-588hsa-miR-660-3phsa-miR-136-5p hsa-miR-200a-3p
hsa_circRNA_100748hsa-miR-136-3phsa-miR-598-3phsa-miR-556-3phsa-miR-335-3p hsa-miR-499a-3p
hsa_circRNA_102600hsa-miR-214-3phsa-miR-324-3phsa-miR-770-5phsa-miR-484 hsa-miR-513a-5p
hsa_circRNA_102261hsa-miR-510-5p hsa-miR-1271-3phsa-miR-604hsa-miR-339-5p hsa-miR-146b-3p

[i] CircRNA, circular RNA; hsa, homo sapiens; MRE, miRNA response element; miR/miRNA, microRNA.

Discussion

Previously, circRNAs have been identified to serve a role in a number of types of disease, including cancer. The majority of circRNAs exhibit distinct tissue/developmental-stage and diseases-specific expression in the process of organismal differentiation, development and diseases (6). CircRNAs regulate cancer development via a number of mechanisms, including miRNA sponges, modulating the Wnt signaling pathway and epithelial-mesenchymal transition (23). The abnormal expression levels of circRNAs have been observed in a number of types of cancer (24), including: glioma (2527), renal cell carcinoma (28), bladder carcinoma (29,30), laryngeal cancer (31), lung cancer (32,33), breast cancer (34,35), esophageal cancer (36,37), gastric cancer (3840), colorectal cancer (4146), pancreatic ductal adenocarcinoma (47), cutaneous squamous cell carcinoma (48), basal cell carcinoma (49) and ovarian cancer (50).

Abnormal circRNAs have been identified to be involved in the occurrence and development of HCC (51). In a previous study, the RNA-seq data from 50 paired HCC tissues and NTs were analyzed to identify the function of circRNAs in HCC (51). Protein-coding genes (PCGs) associated with the 2091 circRNAs were identified to be enriched predominantly on liver/cardiovascular-related diseases, and participated in a number of metabolic processes (51). A total of 45 circRNAs and 23 PCGs exhibited significant expression alterations between HCC and normal tissues (51).

Representative circRNA, antisense to cerebellar degeneration-related protein 1 (Cdr1as, also termed ciRS-7), has been identified to act as an oncogene through targeting miR-7 in HCC (52). Cdr1as expression was upregulated and miR-7 expression was downregulated in HCC tissues. Knockdown of Cdr1as downregulated the expression of miR-7, and inhibited the expression of Cyclin E1 (CCNE1) and phosphatidylinositol 3-kinase catalytic subunit delta (PIK3CD), resulting in the suppression of proliferation and invasion of HCC cells through targeting miR-7 (52). Increased Cdr1as expression was significantly associated with hepatic microvascular invasion (MVI), alpha-fetoprotein (AFP) level, younger age and deterioration of HCC. The expression of Cdr1as in HCC tissues with concurrent MVI was inversely associated with miR-7 and positively associated with two miR-7-targeted genes (PIK3CD and p70S6K) (53).

The expression of circZKSCAN1 (zinc finger with KRAB and SCAN domains 1) and ZKSCAN1mRNA was significantly decreased in the HCC samples compared with matched adjacent non-tumorous tissues. The circZKSCAN1 levels varied in patients with tumor numbers, cirrhosis, vascular invasion and the tumor grade. ZKSCAN1mRNA primarily regulated cellular metabolism, whereas circZKSCAN1 mediated a number of cancer-related signaling pathways, suggesting a non-redundant role for ZKSCAN1mRNA and circZKSCAN1 (54).

CircRNAs are abundant, evolutionally conserved and relatively stable in the cytoplasm and therefore may be valuable for cancer diagnosis (12,14). A previous study demonstrated that a total of 174 and 353 circRNAs were upregulated and downregulated in HCC tissues, respectively, according to microarray analysis (55). hsa_circ_0004018 may be involved in cancer-related pathways via interactions with miRNAs (55). hsa_circ_0004018 is downregulated in HCC tissues and HCC lines, and a decreased hsa_circ_0004018 level is associated with serum AFP level, tumor diameters, differentiation, Barcelona Clinic Liver Cancer stage (56) and TNM stage. An additional study identified that hsa_circ_0001649 expression was significantly downregulated in HCC tissues, using the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) (57). hsa_circ_0001649 expression was associated with tumor size and the occurrence of tumor embolus in HCC; therefore, hsa_circ_0001649 may function in the tumorigenesis and metastasis of HCC and serve as a novel potential biomarker (57). In addition, GO analysis revealed that hsa_circ_0005075 may participate in cell adhesion during HCC development. Upregulated hsa_circ_0005075 exhibited an association with HCC tumor size and revealed diagnostic potential (58).

The present study compared the circRNA expression profiles between HCC tissue and adjacent NTs using microarray analysis with 5,396 circRNA probes. On the basis of the microarray data, 222,567,556 upregulated circRNAs and 125,439,219 downregulated circRNAs were identified in HCC tissues compared with adjacent NTs. Further analysis identified 24 upregulated and 23 downregulated significantly circRNAs (fold-change ≥2; P≤0.05) in HCC tissues compared with NTs. The results of the present study demonstrated that the circRNA expression profiles of HCC tissues differ from that of NTs.

Computer and database analysis annotated the MREs associated with the abnormally expressed circRNAs. The upregulated circRNAs may suppress miRNA expression. On the contrary, downregulated circRNAs may increase miRNA expression. The circRNAs, as a sponge for miRNAs, may be associated with the occurrence and progression of HCC, and may provide a novel approach to identify the underlying molecular basis of HCC. Furthermore, the identified differentially expressed circRNAs may be used as biomarkers for HCC.

In the present study, the abnormal expression levels of three paired HCC tissues were analyzed. Additional studies, with larger cohorts and using RT-qPCR, are required to validate the results from the present study. The data from the present study indicated that abnormal expression of certain circRNAs in HCC tissues and abnormal circRNAs may be novel biomarkers for the diagnosis of HCC.

Acknowledgements

The present study was supported by the Capital Science and Technology Development Fund (grant no. 2014-1-2181), the Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding (grant no. ZYLX201610), the Liver and AIDS Fund of Beijing YouAn Hospital (grant no. BJYAH-2011-032) and National Sci-Tech Support Plan (grant no. 2015BAI02B00).

References

1 

Zhu RX, Seto WK, Lai CL and Yuen MF: Epidemiology of hepatocellular carcinoma in the Asia-Pacific region. Gut Liver. 10:332–339. 2016. View Article : Google Scholar : PubMed/NCBI

2 

de Martel C, Maucort-Boulch D, Plummer M and Franceschi S: World-wide relative contribution of Hepatitis B and C viruses in hepatocellular carcinoma. Hepatology. 62:1190–1200. 2015. View Article : Google Scholar : PubMed/NCBI

3 

El-Serag HB: Epidemiology of viral hepatitis and hepatocellular carcinoma. Gastroenterology. 142:1264–1273.e1. 2012. View Article : Google Scholar : PubMed/NCBI

4 

Michielsen P and Ho E: Viral hepatitis B and hepatocellular carcinoma. Acta Gastroenterol Belg. 74:4–8. 2011.PubMed/NCBI

5 

Sanger HL, Klotz G, Riesner D, Gross HJ and Kleinschmidt AK: Viroids are single-stranded covalently closed circular RNA molecules existing as highly base-paired rod-like structures. Proc Natl Acad Sci USA. 73:pp. 3852–3856. 1976; View Article : Google Scholar : PubMed/NCBI

6 

Memczak S, Jens M, Elefsinioti A, Torti F, Krueger J, Rybak A, Maier L, Mackowiak SD, Gregersen LH, Munschauer M, et al: Circular RNAs are a large class of animal RNAs with regulatory potency. Nature. 495:333–338. 2013. View Article : Google Scholar : PubMed/NCBI

7 

Hsu MT and Coca-Prados M: Electron microscopic evidence for the circular form of RNA in the cytoplasm of eukaryotic cells. Nature. 280:339–340. 1979. View Article : Google Scholar : PubMed/NCBI

8 

Hentze MW and Preiss T: Circular RNAs: Splicing's enigma variations. EMBO J. 32:923–925. 2013. View Article : Google Scholar : PubMed/NCBI

9 

Wilusz JE and Sharp PA: Molecular biology. A circuitous route to noncoding RNA. Science. 340:440–441. 2013. View Article : Google Scholar : PubMed/NCBI

10 

Wu Q, Wang Y, Cao M, Pantaleo V, Burgyan J, Li WX and Ding SW: Homology-independent discovery of replicating pathogenic circular RNAs by deep sequencing and a new computational algorithm. Proc Natl Acad Sci USA. 109:pp. 3938–3943. 2012; View Article : Google Scholar : PubMed/NCBI

11 

Salzman J, Gawad C, Wang PL, Lacayo N and Brown PO: Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. PLoS One. 7:e307332012. View Article : Google Scholar : PubMed/NCBI

12 

Jeck WR, Sorrentino JA, Wang K, Slevin MK, Burd CE, Liu J, Marzluff WF and Sharpless NE: Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA. 19:141–157. 2013. View Article : Google Scholar : PubMed/NCBI

13 

Zhang Y, Zhang XO, Chen T, Xiang JF, Yin QF, Xing YH, Zhu S, Yang L and Chen LL: Circular intronic long noncoding RNAs. Mol Cell. 51:792–806. 2013. View Article : Google Scholar : PubMed/NCBI

14 

Hansen TB, Jensen TI, Clausen BH, Bramsen JB, Finsen B, Damgaard CK and Kjems J: Natural RNA circles function as efficient microRNA sponges. Nature. 495:384–388. 2013. View Article : Google Scholar : PubMed/NCBI

15 

Guo JU, Agarwal V, Guo H and Bartel DP: Expanded identification and characterization of mammalian circular RNAs. Genome Biol. 15:4092014. View Article : Google Scholar : PubMed/NCBI

16 

Wilusz JE and Sharp PA: Molecular biology. A circuitous route to noncoding RNA. Science. 340:440–441. 2013. View Article : Google Scholar : PubMed/NCBI

17 

Ghosal S, Das S, Sen R, Basak P and Chakrabarti J: Circ2Traits: A comprehensive database for circular RNA potentially associated with disease and traits. Front Genet. 4:2832013. View Article : Google Scholar : PubMed/NCBI

18 

Burd CE, Jeck WR, Liu Y, Sanoff HK, Wang Z and Sharpless NE: Expression of linear and novel circular forms of an INK4/ARF-associated non-coding RNA correlates with atherosclerosis risk. PLoS Genet. 6:e10012332010. View Article : Google Scholar : PubMed/NCBI

19 

Chen YT, Rettig WJ, Yenamandra AK, Kozak CA, Chaganti RS, Posner JB and Old LJ: Cerebellar degeneration-related antigen: A highly conserved neuroectodermal marker mapped to chromosomes X in human and mouse. Proc Natl Acad Sci USA. 87:pp. 3077–3081. 1990; View Article : Google Scholar : PubMed/NCBI

20 

Hansen TB, Kjems J and Damgaard CK: Circular RNA and miR-7 in cancer. Cancer Res. 73:5609–5612. 2013. View Article : Google Scholar : PubMed/NCBI

21 

Li J, Yang J, Zhou P, Le Y, Zhou C, Wang S, Xu D, Lin HK and Gong Z: Circular RNAs in cancer: Novel insights into origins, properties, functions and implications. Am J Cancer Res. 5:472–480. 2015.PubMed/NCBI

22 

Okuda K, Ohtsuki T, Obata H, Tomimatsu M, Okazaki N, Hasegawa H, Nakajima Y and Ohnishi K: Natural history of hepatocellular carcinoma and prognosis in relation to treatment. Study of 850 patients. Cancer. 56:918–928. 1985. View Article : Google Scholar : PubMed/NCBI

23 

He J, Xie Q, Xu H, Li J and Li Y: Circular RNAs and cancer. Cancer Lett. 396:138–144. 2017. View Article : Google Scholar : PubMed/NCBI

24 

Rong D, Tang W, Li Z, Zhou J, Shi J, Wang H and Cao H: Novel insights into circular RNAs in clinical application of carcinomas. Onco Targets Ther. 10:2183–2188. 2017. View Article : Google Scholar : PubMed/NCBI

25 

Song X, Zhang N, Han P, Moon BS, Lai RK, Wang K and Lu W: Circular RNA profile in gliomas revealed by identification tool UROBORUS. Nucleic Acids Res. 44:e872016. View Article : Google Scholar : PubMed/NCBI

26 

Yang P, Qiu Z, Jiang Y, Dong L, Yang W, Gu C, Li G and Zhu Y: Silencing of cZNF292 circular RNA suppresses human glioma tube formation via the Wnt/β-catenin signaling pathway. Oncotarget. 7:63449–63455. 2016. View Article : Google Scholar : PubMed/NCBI

27 

Barbagallo D, Condorelli A, Ragusa M, Salito L, Sammito M, Banelli B, Caltabiano R, Barbagallo G, Zappalà A, Battaglia R, et al: Dysregulated miR-671-5p/CDR1-AS/CDR1/VSNL1 axis is involved in glioblastoma multiforme. Oncotarget. 7:4746–4759. 2016. View Article : Google Scholar : PubMed/NCBI

28 

Wang K, Sun Y, Tao W, Fei X and Chang C: Androgen receptor (AR) promotes clear cell renal cell carcinoma (ccRCC) migration and invasion via altering the circHIAT1/miR-195-5p/29a-3p/29c-3p/CDC42 signals. Cancer Lett. 394:1–12. 2017. View Article : Google Scholar : PubMed/NCBI

29 

Zhong Z, Lv M and Chen J: Screening differential circular RNA expression profiles reveals the regulatory role of circTCF25-miR-103a-3p/miR-107-CDK6 pathway in bladder carcinoma. Sci Rep. 6:309192016. View Article : Google Scholar : PubMed/NCBI

30 

Huang M, Zhong Z, Lv M, Shu J, Tian Q and Chen J: Comprehensive analysis of differentially expressed profiles of lncRNAs and circRNAs with associated co-expression and competing endogenous RNAs networks in bladder carcinoma. Oncotarget. 7:47186–47200. 2016. View Article : Google Scholar : PubMed/NCBI

31 

Xuan LJ, Qu LM, Zhou H, Wang P, Yu H, Wu T, Wang X, Li Q, Tian L, Liu M and Sun Y.: Circular RNA: A novel biomarker for progressive laryngeal cancer. Am J Transl Res. 8:932–939. 2016.PubMed/NCBI

32 

Yao JT, Zhao SH, Liu QP, Lv MQ, Zhou DX, Liao ZJ and Nan KJ: Over-expression of CircRNA-100876 in non-small cell lung cancer and its prognostic value. Pathol Res Pract. 213:453–456. 2017. View Article : Google Scholar : PubMed/NCBI

33 

Wan L, Zhang L, Fan K, Cheng ZX, Sun QC and Wang JJ: Circular RNA-ITCH suppresses lung cancer proliferation via inhibiting the Wnt/β;-catenin pathway. Biomed Res Int. 2016:15794902016. View Article : Google Scholar : PubMed/NCBI

34 

Lü L, Sun J, Shi P, Kong W, Xu K, He B, Zhang S and Wang J: Identification of circular RNAs as a promising new class of diagnostic biomarkers for human breast cancer. Oncotarget. 8:44096–44107. 2017.PubMed/NCBI

35 

Nair AA, Niu N, Tang X, Thompson KJ, Wang L, Kocher JP, Subramanian S and Kalari KR: Circular RNAs and their associations with breast cancer subtypes. Oncotarget. 7:80967–80979. 2016.PubMed/NCBI

36 

Su H, Lin F, Deng X, Shen L, Fang Y, Fei Z, Zhao L, Zhang X, Pan H, Xie D, et al: Profiling and bioinformatics analyses reveal differential circular RNA expression in radioresistant esophageal cancer cells. J Transl Med. 14:2252016. View Article : Google Scholar : PubMed/NCBI

37 

Li F, Zhang L, Li W, Deng J, Zheng J, An M, Lu J and Zhou Y: Circular RNA ITCH has inhibitory effect on ESCC by suppressing the Wnt/β-catenin pathway. Oncotarget. 6:6001–6013. 2015. View Article : Google Scholar : PubMed/NCBI

38 

Li P, Chen S, Chen H, Mo X, Li T, Shao Y, Xiao B and Guo J: Using circular RNA as a novel type of biomarker in the screening of gastric cancer. Clin Chim Acta. 444:132–136. 2015. View Article : Google Scholar : PubMed/NCBI

39 

Li P, Chen H, Chen S, Mo X, Li T, Xiao B, Yu R and Guo J: Circular RNA 0000096 affects cell growth and migration in gastric cancer. Br J Cancer. 116:626–633. 2017. View Article : Google Scholar : PubMed/NCBI

40 

Chen S, Li T, Zhao Q, Xiao B and Guo J: Using circular RNA hsa_circ_0000190 as a new biomarker in the diagnosis of gastric cancer. Clin Chim Acta. 466:167–171. 2017. View Article : Google Scholar : PubMed/NCBI

41 

Tang W, Ji M, He G, Yang L, Niu Z, Jian M, Wei Y, Ren L and Xu J: Silencing CDR1as inhibits colorectal cancer progression through regulating microRNA-7. Onco Targets Ther. 10:2045–2056. 2017. View Article : Google Scholar : PubMed/NCBI

42 

Huang G, Zhu H, Shi Y, Wu W, Cai H and Chen X: cir-ITCH plays an inhibitory role in colorectal cancer by regulating the Wnt/β-catenin pathway. PLoS One. 10:e01312252015. View Article : Google Scholar : PubMed/NCBI

43 

Wang X, Zhang Y, Huang L, Zhang J, Pan F, Li B, Yan Y, Jia B, Liu H, Li S and Zheng W: Decreased expression of hsa_circ_001988 in colorectal cancer and its clinical significances. Int J Clin Exp Pathol. 8:16020–16025. 2016.

44 

Xie H, Ren X, Xin S, Lan X, Lu G, Lin Y, Yang S, Zeng Z, Liao W, Ding Y and Liang L: Emerging roles of circRNA_001569 targeting miR-145 in the proliferation and invasion of colorectal cancer. Oncotarget. 7:26680–26691. 2016. View Article : Google Scholar : PubMed/NCBI

45 

Zhu M, Xu Y, Chen Y and Yan F: Circular BANP, an upregulated circular RNA that modulates cell proliferation in colorectal cancer. Biomed Pharmacother. 88:138–144. 2017. View Article : Google Scholar : PubMed/NCBI

46 

Dou Y, Cha DJ, Franklin JL, Higginbotham JN, Jeppesen DK, Weaver AM, Prasad N, Levy S, Coffey RJ, Patton JG and Zhang B: Circular RNAs are down-regulated in KRAS mutant colon cancer cells and can be transferred to exosomes. Sci Rep. 6:379822016. View Article : Google Scholar : PubMed/NCBI

47 

Qu S, Song W, Yang X, Wang J, Zhang R, Zhang Z, Zhang H and Li H: Microarray expression profile of circular RNAs in human pancreatic ductal adenocarcinoma. Genom Data. 5:385–387. 2015. View Article : Google Scholar : PubMed/NCBI

48 

Sand M, Bechara FG, Gambichler T, Sand D, Bromba M, Hahn SA, Stockfleth E and Hessam S: Circular RNA expression in cutaneous squamous cell carcinoma. J Dermatol Sci. 83:210–218. 2016. View Article : Google Scholar : PubMed/NCBI

49 

Sand M, Bechara FG, Sand D, Gambichler T, Hahn SA, Bromba M, Stockfleth E and Hessam S: Circular RNA expression in basal cell carcinoma. Epigenomics. 8:619–632. 2016. View Article : Google Scholar : PubMed/NCBI

50 

Ahmed I, Karedath T, Andrews SS, Al-Azwani IK, Mohamoud YA, Querleu D, Rafii A and Malek JA: Altered expression pattern of circular RNAs in primary and metastatic sites of epithelial ovarian carcinoma. Oncotarget. 7:36366–36381. 2016. View Article : Google Scholar : PubMed/NCBI

51 

Li Y, Dong Y, Huang Z, Kuang Q, Wu Y, Li Y and Li M: Computational identifying and characterizing circular RNAs and their associated genes in hepatocellular carcinoma. PLoS One. 12:e01744362017. View Article : Google Scholar : PubMed/NCBI

52 

Yu L, Gong X, Sun L, Zhou Q, Lu B and Zhu L: The circular RNA Cdr1as act as an oncogene in hepatocellular carcinoma through targeting miR-7 expression. PLoS One. 11:e01583472016. View Article : Google Scholar : PubMed/NCBI

53 

Xu L, Zhang M, Zheng X, Yi P, Lan C and Xu M: The circular RNA ciRS-7 (Cdr1as) acts as a risk factor of hepatic microvascular invasion in hepatocellular carcinoma. J Cancer Res Clin Oncol. 143:17–27. 2017. View Article : Google Scholar : PubMed/NCBI

54 

Yao Z, Luo J, Hu K, Lin J, Huang H, Wang Q, Zhang P, Xiong Z, He C, Huang Z, et al: ZKSCAN1 gene and its related circular RNA (circZKSCAN1) both inhibit hepatocellular carcinoma cell growth, migration, and invasion but through different signaling pathways. Mol Oncol. 11:422–437. 2017. View Article : Google Scholar : PubMed/NCBI

55 

Fu L, Yao T, Chen Q, Mo X, Hu Y and Guo J: Screening differential circular RNA expression profiles reveals hsa_circ_0004018 is associated with hepatocellular carcinoma. Oncotarget. 8:58405–58416. 2017.PubMed/NCBI

56 

Bruix J and Sherman M: American Association for the Study of Liver Diseases: Management of hepatocellular carcinoma: An update. Hepatology. 53:1020–1022. 2011. View Article : Google Scholar : PubMed/NCBI

57 

Qin M, Liu G, Huo X, Tao X, Sun X, Ge Z, Yang J, Fan J, Liu L and Qin W: Hsa_circ_0001649: A circular RNA and potential novel biomarker for hepatocellular carcinoma. Cancer Biomark. 16:161–169. 2016. View Article : Google Scholar : PubMed/NCBI

58 

Shang X, Li G, Liu H, Li T, Liu J, Zhao Q and Wang C: Comprehensive circular RNA profiling reveals that hsa_circ_0005075, a new circular RNA biomarker, is involved in hepatocellular carcinoma development. Medicine. 95:e38112016. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

January 2018
Volume 15 Issue 1

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
APA
Cui, S., Qian, Z., Chen, Y., Li, L., Li, P., & Ding, H. (2018). Screening of up‑ and downregulation of circRNAs in HBV‑related hepatocellular carcinoma by microarray. Oncology Letters, 15, 423-432. https://doi.org/10.3892/ol.2017.7265
MLA
Cui, S., Qian, Z., Chen, Y., Li, L., Li, P., Ding, H."Screening of up‑ and downregulation of circRNAs in HBV‑related hepatocellular carcinoma by microarray". Oncology Letters 15.1 (2018): 423-432.
Chicago
Cui, S., Qian, Z., Chen, Y., Li, L., Li, P., Ding, H."Screening of up‑ and downregulation of circRNAs in HBV‑related hepatocellular carcinoma by microarray". Oncology Letters 15, no. 1 (2018): 423-432. https://doi.org/10.3892/ol.2017.7265