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Abstract

BACKGROUND:

Circular RNAs (circRNAs) play an important role in pathogenesis and development of hepatocellular carcinoma (HCC). However, circRNA expression profiles in hepatitis B Virus (HBV)-related HCC remain to be studied.

METHODS:

Total 13 HBV-related HCC patients were enrolled for study. Three HCC and 3 paired adjacent non-tumorous (NT) tissues from 3 patients were performed for microarray. Ten pairs of HCC tissues were used to verify the identified up-regulated and down-regulated circRNAs obtained from the microarray data by quantitative real-time reverse transcription PCR (qRT-PCR). Total RNA was isolated and treated with Rnase R to remove linear RNA, then hybridized to the array to screen for circRNAs. Bioinformatics analyses including clustering, differential expression, annotation of circRNA/microRNA (miRNA) interactions, Go analysis and KEGG pathway analysis, were performed.

RESULTS:

Based on the microarray data, we found significantly up-regulation of 24 circRNAs and down-regulation of 23 circRNAs in the HCC samples compared to NT samples (fold change $\geqslant$ 2.0 and $P<$ 0.05). Of them, 6 candidate circRNAs (hsa_circRNA_102814, 100381, 103489, 101764, 100327, and 103361) were verified by qRT-PCR. Of them, hsa_circRNA 100381, 103489 up-regulation and 101764 down-regulation were found to be significantly different in the 10 validation HCC tissue. Clusters of circRNAs were aberrantly expressed in HCC compared with NT samples. CircRNA_101764 was the largest nodes in circRNA/microRNA co-expression network, especially co-expression with hsa-miR-181 family, which plays an important role in cell network. Annotation of circRNA/miRNA interactions indicated that the biological effects of circRNA may be achieved by binding of miRNAs. GO analysis revealed that numerous target genes were involved in the biological processes, cellular component and molecular function. There was nearly 30 target genes enrichment on KEGG pathways analysis, PI3K-Akt signaling pathway which the most number of genes involved.

CONCLUSION:

In this study, we comprehensively explored the expression of differentially expressed circRNAs in HBV-related HCC, and our results indicate that circRNA_101764 may play an important role in the development of HCC.

Keywords

Hepatocellular carcinoma HBV circular RNA microarray

1. Introduction

Table 1
The clinical information in 13 enrolled subjects

Patients	Gender	Age	HBV DNA	Cirrhosis	Serum AFP	NAs	Histopathological diagnosis and IHC**	Therapy
			IU/ml#		ng/ml	treatment*
ZJF	M	44	$<$ 20	No	7.34	No	Hepatocellular carcinoma, moderatelydifferentiated. HBsAg(+), GPC-3(+),Hepa(+++)	Tumor resection
MFX	M	63	4.01 $\times$ 10 ${}^{6}$	Yes	41.66	No	Hepatocellular carcinoma with cirrhosis,moderately differentiated. HBsAg(+),GPC-3(++), Hepa(+)	Tumor resection +TACE
SPH	M	44	9.85 $\times$ 10 ${}^{2}$	Yes	392.20	Yes	Hepatocellular carcinoma with cirrhosis,poorly differentiated. HBsAg(+),GPC-3(+++), Hepa(++)	Tumor resection
LGX	M	43	$<$ 20	Yes	5.42	No	Hepatocellular carcinoma with cirrhosis,moderately differentiated. HBsAg(+),GPC-3(+), Hepa(+++)	Liver Transplantation
ZAH	F	45	1.98 $\times$ 10 ${}^{3}$	Yes	280.0	No	Hepatocellular carcinoma with cirrhosis,moderately differentiated. HBsAg(+),GPC-3(++), Hepa(+++)	Tumor resection
SPZ	M	59	1.6 $\times$ 10 ${}^{6}$	Yes	1163.0	No	Hepatocellular carcinoma with cirrhosis,poorly differentiated. HBsAg(+),GPC-3(+++), Hepa(-)	Liver Transplantation
XYM	M	52	8620	Yes	1.44	No	Hepatocellular carcinoma with cirrhosis,poorly-moderately differentiated.HBsAg(+), Hepa(+), GPC-3(+)	Liver Transplantation
BWP	M	46	460	Yes	8575.0	No	Hepatocellular carcinoma with cirrhosis,poorly differentiated. HBsAg(+),Hepa(++), GPC-3(+++)	Liver Transplantation
HHY	M	47	4790	Yes	833.0	No	Hepatocellular carcinoma with fatty liverand cirrhosis, moderately differentiated.HBsAg(+), Hepa(++), GPC-3(+++)	Tumor resection
LJF	M	45	6070	Yes	7464.0	No	Hepatocellular carcinoma withcirrhosis, moderately differentiated.HBsAg(+), Hepa(+), GPC-3(++)	Tumor resection
WSY	M	60	$<$ 20	Yes	12540.0	Yes	Hepatocellular carcinoma with cirrhosis,poorly differentiated. HBsAg(+),Hepa(+++), GPC-3(+++)	Tumor resection
ZMX	M	48	1.6 $\times$ 10 ${}^{5}$	Yes	48080.0	No	Hepatocellular carcinoma with cirrhosis,poorly-moderately differentiated.HBsAg(+), Hepa(+), GPC-3(+++)	Liver Transplantation
PLB	M	28	3.6 $\times$ 10 ${}^{4}$	Yes	121000.0	No	Hepatocellular carcinoma with cirrhosis,moderately differentiated. HBsAg(+),Hepa(+++), GPC-3(+)	Tumor resection

#Serum HBV-DNA level was tested by quantified real-time polymerase chain reaction (qPCR, COBAS^® AmpliPrep^®/COBAS^® TaqMan, Roche Diagnostics, Germany). The HBV DNA was undetectability less 20 IU/ml. *The patient was treated using nucleoside(s) analogues (NAs) before diagnosis of hepatocellular carcinoma. **IHC: Immunohistochemical staining for Hepatitis B surface antigen (HBsAg), glypican-3 (GPC-3) and hepatocyte (Hepa) protein were routinely performed.

Table 2

Sequences of oligonucleotides used for RT-qPCR

Gene		Primer sequence	Product size (bp)
$\beta$ -actin	Sense	F: 5 ${}^{\prime}$ GTGGCCGAGGACTTTGATTG3 ${}^{\prime}$	73
	Antisense	R: 5 ${}^{\prime}$ CCTGTAACAACGCATCTCATATT3 ${}^{\prime}$
circRNA_102814	Sense	F: 5 ${}^{\prime}$ GAGAAAGGATTTCATCTGGATGTA3 ${}^{\prime}$	107
	Antisense	R: 5 ${}^{\prime}$ CGAGCTGTTTGTTCTAAACTCTG3 ${}^{\prime}$
circRNA_100381	Sense	F: 5 ${}^{\prime}$ ATTGTATCCTGCTCTGGAGATGG3 ${}^{\prime}$	227
	Antisense	R: 5 ${}^{\prime}$ TGCCCTGAACGAATTGTTGTC3 ${}^{\prime}$
circRNA_103489	Sense	F: 5 ${}^{\prime}$ ATACAAGGCAGCCATAGTTCAC3 ${}^{\prime}$	183
	Antisense	R: 5 ${}^{\prime}$ TGCTTCTACAGGCAGTAGGTTTA3 ${}^{\prime}$
circRNA_101764	Sense	F: 5 ${}^{\prime}$ TTTGACAACAATGGCAACAGAGAC3 ${}^{\prime}$	168
	Antisense	R: 5 ${}^{\prime}$ CATCGGAGGCTGGACCCTT3 ${}^{\prime}$
circRNA_100327	Sense	F: 5 ${}^{\prime}$ GCCCTCAAGCCTATGAACTGC3 ${}^{\prime}$	56
	Antisense	R: 5 ${}^{\prime}$ CAAAGGTCAACCAATGTGCCA3 ${}^{\prime}$
circRNA_103361	Sense	F: 5 ${}^{\prime}$ TTTAACTAGCACTGCTTGTCGGA3 ${}^{\prime}$	84
	Antisense	R: 5 ${}^{\prime}$ ATCACCTTTGGCAGGATCTTCA3 ${}^{\prime}$

Table 3

Annotation of 3 circRNAs and miRNA response elements (MREs)

CircRNA list	MRE1	MRE2	MRE3	MRE4	MRE5
circRNA_100381	miR-525-5p	miR-544a	miR-345-3p	miR-577	miR-587
circRNA_103489	miR-654-3p	miR-511-5p	miR-632	miR-643	miR-889-5p
circRNA_101764	miR-181b-5p	miR-181c-5p	miR-181d-5p	miR-181a-5p	miR-329-5p

Circular RNAs (circRNAs), widely expressed in tissue and developmental-stage specific patterns that regulate gene expression in mammals. CircRNAs are a novel class of widespread and diverse endogenous RNAs characterized by the presence of a covalent bond linking the 3 ${}^{\prime}$ and 5 ${}^{\prime}$ ends generated by backsplicing. The majority of circRNAs are conserved across species, are stable, and are resistant to RNase [1, 2]. Functional circRNAs have been shown to act as cytoplasmic microRNA (miRNA) sponges and RNA-binding protein sequestering agents as well as nuclear transcriptional regulators, illustrating the relevance of circRNAs as participants in the regulatory networks governing gene expression [3]. MicroRNAs (miRNAs), a class of small (18–24 nucleotides) non-coding RNAs, which regulated target mRNA at post-transcriptional level [17]. A large number of experimental results exhibited that miRNAs involved in a number of critical biological processes, such as: cell development, differentiation and apoptosis [18]. CircRNAs play a crucial role in disease development, for instance, in nervous system disorders and atherosclerosis [4, 5]. CircRNA may also serve as a novel potential biomarker for HCC diagnosis and prognosis [6].

Chronic hepatitis B virus (HBV) infection is a dominant risk factor in the pathogenesis of hepatocellular carcinoma (HCC) [7]. HBV carcinogenesis through integrating into the host genome, leading to the widespread instability [8]. Aberrant expression of genes, which can involve RNA, is a key node for the occurrence and development of HCC [9]. However, the hepatic expression of circRNAs in HCC tissues remains fully unknown. Therefore, the aim of this study was to screen hepatic circRNA profiles to identify and verify aberrantly expressed circRNAs in HBV-related HCC tissues.

2. Materials and methods

2.1 Patients and liver tissue samples

Total 13 HBV-related HCC patients were enrolled for study. The diagnosis of HCC was performed following AASLD guideline and confirmed by histopathology [10]. All the tissue samples were reviewed by two experienced pathologists. All patients were serum HBSAg positive except other etiologies. Three HCC and 3 paired adjacent non-tumorous (NT) tissues from 3 patients were performed for microarray. Ten of 13 HCC tissues were used to verify the identified up-regulated and down-regulated circRNAs obtained from the microarray data by quantitative real-time reverse transcription PCR (qRT-PCR). The specimens were surgical resections, then immediately preserved in liquid nitrogen. The NT tissues were defined as being 5 cm away from the edge of the tumor and contained no tumor cells when evaluated by an experienced pathologist. The protocol was approved by the Ethics Committee of Beijing Youan Hospital affiliated to Capital Medical University and was conducted in compliance with the Declaration of Helsinki. The written informed consent was obtained from each patient. Patient demographics and clinicopathological data are summarized in Table 1.

2.2 Total RNA extraction

Total RNA was extracted from the all the HCC tissues and paired adjacent NT tissues using TRIzol reagent (Invitrogen, Carlsbad, SC, USA), following the manufacturer’s instructions. Total RNA from each specimen was quantified using a NanoDrop ND-1000 spectrophotometer (Wilmington, DE, USA). RNA integrity and genomic DNA contamination were assessed using standard denaturing agarose gel electrophoresis, and the purity was estimated by the ratio of absorbance at 260 to 280 nm.

Figure 1.

Different expression profiles of circRNA between HCC and NT tissues. The hierarchical clustering shows a distinguishable circRNA expression profiles among 3 HCC tissue and paired adjacent nontumorous tissue (A). Volcano Plots visualized the relationship between fold-changeand statistical significance (B). The vertical lines correspond to 2.0-fold up and down, and the horizontal line represents a $P$ -value of 0.05. The red point represents the differentially expressed circRNAs with statistical significance.

Figure 2.

Microarray and qRT-PCR analysis of selected circRNAs. The top 6 highest change circRNAs (3 up-regulate and 3 down-regulate) were chosen and presented (A). Selected circRNAs shows a distinguishable expression profiles among HCC tissue and NTs through hierarchical clustering (B). qRT-PCR analysis of selected up-regulate (C) and down-regulate (D) circRNAs. * $P<$ 0.05 and ** $P<$ 0.01 compared with NTs.

2.3 CircRNA labeling and hybridization

Total RNA was treated with Rnase R (Epicentre, Madison, WI, USA) to remove linear RNAs. Each sample was amplified and transcribed into fluorescent complementary RNA (cRNA) utilizing Arraystar Super RNA Labeling Kit. The labeled circRNAs were hybridized onto the Arraystar Human circRNA Array. The labeled circRNAs were purified using the RNeasy Mini Kit (Qiagen, Germantown, MD, USA). The labeled cRNAs (pmol Cy3/ $\mu$ g cRNA) were measured using a NanoDrop ND-1000. One $\mu$ g of each labeled cRNA was fragmented by adding 5 $\mu$ l 10 $\times$ blocking agent and 1 $\mu$ l of 25 $\times$ fragmentation buffer, then heating the mixture to 60 ${}^{\circ}$ C for 30 min. Finally 25 $\mu$ l 2 $\times$ hybridization buffer was added to dilute the labeled cRNA. Fifty $\mu$ l of hybridization solution was dispensed into the gasket slide and assembled to the circRNA expression microarray slide, then incubated for 17 hours at 65 ${}^{\circ}$ C in an Agilent Hybridization Oven (Agilent, USA). The hybridized arrays were washed, fixed, and scanned using the Axon GenePix 4000B microarray scanner (Molecular Devices, Inc., San Jose, CA, USA).

2.4 Raw data collection and expression profiles of circRNAs

Scanned images were imported into GenePix Pro 6.0 software for grid alignment and raw data extraction. Quintile normalization and subsequent data processing were performed using the R software package. Then, low intensity filtering was performed, and circRNAs that were flagged as “expressed” (greater than 2 times the background standard deviation) in at least two out of six samples were retained for further analyses. Differentially expressed circRNAs with statistical significance between two groups were identified using a volcano plot and hierarchical clustering. The statistical significance of the difference be conveniently estimated by t test. CircRNAs having fold changes $\geqslant$ 2 and P $<$ 0.05 were selected as of significantly differential expressions.

2.5 Measurement of circRNAs by qRT-PCR

Based on the circRNA expression profiles, we choose six of the most obvious changes circRNAs for further verification, which included 3 up-regulated(circRNA_102814, 100381, and 103489) and 3 down-regulated (circRNA_101764, 100327, and 103361). Total RNA was extracted from the 10 pair validation HCC tissues using Trizol Reagent (Invitrogen, Carlsbad, SC, USA) and reversely transcribed into cDNA using Super Script TM III Reverse Tran-scriptase (Invitrogen, Carlsbad, SC, USA). The mRNA content was normalized to the housekeeping gene $\beta$ -actin. Data was shown as fold change (2 ${}^{-\Delta\Delta\text{Ct}}$ ). Triplicates were performed for each sample in three independent experiments. All the primer sequences were presented in Table 2.

2.6 Annotation of circRNA/microRNA interactions

CircRNA (circRNA_100381, 103489 and 101764)/ miRNA interactions were predicted with Arraystar miRNA target prediction software based on TargetScan and Miranda [11, 12]. All the results were presented in Table 3. Differentially expressed circRNAs were annotated in detail with the circRNA/miRNA interaction information.

2.7 MicroRNA-target genes prediction and network

To further investigate the functional roles of microRNAs, which predicted to interact with choose circRNA, putative targets of miRNAs were predicted by three soft wares: TargetScan, Mirbase and Miranda softwares. Predict the final result to take three overlapping part of the data to predict the result. MicroRNA – mRNA interaction analysis was conducted by Cytoscape to elucidate correlations between microRNA and target mRNA. The size of each node represents the number of putative microRNA functionally connected to each mRNA.

2.8 MicroRNA target genes bioinformation analysis

GO analysis was performed to explore the functional roles of target genes in terms of biological processes (BP), cellular components (CC) and molecular functions (MF). Pathway analysis is a functional analysis mapping genes to Kyoto Encyclopedia of Genes and Genomes (KEGG). The P-value (EASE-score, Fisher-P value or Hypergeometric-P value) denotes the significance of the pathway correlated to the conditions.

Figure 3.

GO analysis of predicted mRNAs according to the values in the enrichment score under the theme of BP, CC and MF.

2.9 Statistical analysis

All statistical data were analyzed using SPSS v. 21.0 (SPSS, USA). All the results were reported as mean $+$ SD for at least triplicate republication. The differences between two groups were estimated by the Student’s t test. $P<$ 0.05 was considered as statistically significant.

3. Results

3.1 Different expression profiles of circRNA between HCC and NT tissues

A total of 5396 circRNAs were scanned under the microarray analysis, and the hierarchical clustering results show that there is a distinguishable circRNA expression profiles between HCC tissues and NTs (Fig. 1A). Volcano plots which are useful for visualizing differential expression between two different groups results exhibits that there were 24 up-regulated and 23 down-regulated cicrRNAs in all the tissues (Fold changes $\geqslant$ 2 and $P<$ 0.05) (Fig. 1B).

3.2 Verification of selected circRNAs

Based on fold change, we choose the top 6 highest change circRNAs summarized in Fig. 2A. Hierarchical cluster analysis shows that chose circaRNAs were significant expression when comparing NTs (Fig. 2B). To further verify the microarray results, qRT-PCR was applied among the 10 HBV-related HCC tissues and paired NTs. The circRNA_102814 had the highest increased fold of 11.121 in microarray, but not significance in qRT-PCR(Fold changes $=$ 1.06 and $P=$ 0.85). Similar results were founded in the circRNA_100327 and 103361. It is noteworthy that the circRNA_101764 was very significance (Fold changes $=$ 0.36 and $P=$ 0.00) among the HCC and NT tissues. Finally, though the data from microarray and qRT-PCR showed the same change tendency of circRNA_101764, 100381 and 103489, only these three reached statistical significance and become candidates for further study (Fig. 2C and D).

Figure 4.

Pathway analysis of predicted mRNAs. The dot plot shows the gene ratio value of the top ten most significant enrichment pathways.

Figure 5.

CircRNA/miRNA interactions and miRNA/mRNA network. The networke shown the relationship of miRNA181 family with target mRNA. Bule nodes are target mRNA, and red nodes are miRNA.

3.3 Computational analysis of significantly dys-regulated circRNAs and predicted mRNAs

The significantly dys-regulated circRNAs were further categorized into different subgroups according to their genomic positions and effects. There were 1 antisense and 23 exonic circRNAs of increased circRNAs and 21 exonic and 2 intronic circRNAs of decreased circRNAs (data not shown). Verified three cicrRNAs then predict mRNAs to further analysis. The results of GO analysis of these mRNAs according to the values in the enrichment score under the theme of BP (Biological process), CC (cellular component) and MF(molecular function), where the top 3 processes were macromolecule metabolic process, macromolecule metabolic process and cellular process in BP; intracellular part, intracellular and nuclear lumen in CC; protein binding, receptor binding and kinase regulator activity in MF (Fig. 3).

3.4 Pathway analysis of predicted mRNAs

Thirty-four KEGG pathways were identified in all the mRNAs (data not shown). The top 10 KEGG pathways were shown in Fig. 4 based on GeneRatio. KEGG results shows that pathway in cancer was associated with 23 genes, which was associated with the largest number of genes.

3.5 Potential of circRNA/miRNA interactions and miRNA/mRNA network

All the three circRNAs (circRNA_101764, 100381 and 103489) were annotated in detail with circRNA/ miRNA interactions using Arraystar miRNA target prediction software (Table 3). It is interesting that circRNA_101764 mainly interact with miRNA181 family. The miRNA/mRNA network shows the relationship of miRNA with its target genes (data not shown). In consideration of miRNA181 family is very large, so we just show the miRNA181 family network in the Fig. 5.

4. Discussion

As we known that the most of the species in the transfer of genetic information are subject to the central dogma, which is DNA translated into protein via transcription into RNA. CircRNA, as is the case in general with non-coding RNAs, the question to answer is, “what do they do?” Individual examples of circRNAs have been sparsely described in the early 1990s. The examples of functional circRNAs were firstly found to act as microRNA sponges in 2013. CircRNA expression is a feature of essentially all eukaryotes known today [13]. Increasing experments suggests that circRNAs are involved in many disease, including cancer [14]. In currently, therefore, circRNAs emerging functions information on the mechanism of circRNA biogenesis, regulation and expression patterns are implicating them in multiple aspects of biology and disease, including potential roles in cancer, heart disease, synaptic transmission, and aging. Based on circRNAs was more stable than miRNA and LncRNA and therefore, they may be promising as diagnostic or predictive biomarkers [15].

The data gathered in this study suggest that prolfiles of circRNAs were very different between HCC and NT tissues. Based on the microarray data, we found significant up-regulation of 24 circRNAs and down-regulation of 23 circRNAs in the HCC samples compared to NT samples. Of them, hsa_circRNA 100381, 103489 upregulation and 101764 downregulation were firstly found to be significantly different. Fu et al. group found that circ_0004018 was significantly lower in HCC compare with para-tumorous tissue. Further data were found that this circRNA might involve in HCC carcinogenesis and metastasis through binding with miR-30-5e [16]. Other group found that circ_0001649 was significantly downregulated in HCC tissues compaire with para-tumorous tissue [17]. And Shang et al. group found that circ_0005075 could as a new potential HCC biomarker [18]. Huang et al. group found another cirRNA (circRNA_100338) was specific high expression in correlated with HBV-related HCC. The further data showed that CircRNA_100338 regulated of HCC invasion through sponging with miR-141-3p [19]. And in this study, we did not detect these circRNAs, maybe it’s because we used HCC tissues were all HBV-realted HCC and different circRNA chips.

CicrRNA research hot spot after one interesting found, which is the function of circRNAs mainly through be miRNA sponges to adsorption miRNA to affect the miRNA function [20]. Then some recent studies have demonstrated that circRNAs regulate gene expression by acting as competing endogenous RNAs, and verified they contain multiple, tandem miRNA binding sites [21]. In our study, bioinformatics analysis was used to annotate MREs and predict target mRNA related to the abnormally expressed circRNAs. We found that 34 KEGG pathways were identified in all the mRNAs which shows that pathway in cancer was associated with 23 genes, which was associated with the largest number of genes. This result shows that the three selected cicrRNAs have close relationship with cancer.

When we annotated in detail with circRNA/miRNA interactions by prediction software, we found that circRNA_101764 mainly interact with miRNA181 family which was correlates with many liver diseases. Chen found that miRNA181 family was very correlates with histological features of progression and harbors the potential to emerge as a diagnostic biomarkers for all types of liver cirrhosis [22]. Another research found that miRNA181a decreased the OPN (Osteopontin) expression to inhibit the liver cancer cells transfer and invasive ability [23].

In this research, we have identified significant up-regulation of 24 circRNAs and down-regulation of 23 circRNAs in the HCC samples compared to NT samples. The hsa_circRNA 100381, 103489 and101764 were confirmed to be significantly different. Those circRNAmay represent potential novel biomarkers for HCC early diagnosis and prognosis. In the future, we will expand the sample number for revalidations these circRNAs as potential biomarkers and functional studies.

Footnotes

Acknowledgments

This work was supported by the National ScienceFund (grant no. 81401970), Capital Science and Technology Development Fund (2014-1-2181), BeijingMunicipal Administration of Hospitals Clinical Medicine Development of Special Funding (ZYLX201610), and the Beijing Municipal Administration of Hospitals’ Ascent Plan (DFL20151602).

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Screening and bioinformatics analysis of circular RNA expression profiles in hepatitis B-related hepatocellular carcinoma

Abstract

BACKGROUND:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

Table 1 The clinical information in 13 enrolled subjects

2.1 Patients and liver tissue samples

2.2 Total RNA extraction

2.4 Raw data collection and expression profiles of circRNAs

2.5 Measurement of circRNAs by qRT-PCR

2.6 Annotation of circRNA/microRNA interactions

2.7 MicroRNA-target genes prediction and network

2.8 MicroRNA target genes bioinformation analysis

3. Results

3.1 Different expression profiles of circRNA between HCC and NT tissues

3.2 Verification of selected circRNAs

3.4 Pathway analysis of predicted mRNAs

3.5 Potential of circRNA/miRNA interactions and miRNA/mRNA network

4. Discussion

Footnotes

Acknowledgments

References

Table 1
The clinical information in 13 enrolled subjects