Open Access

CircRNA expression profiling of PBMCs from patients with hepatocellular carcinoma by RNA‑sequencing

  • Authors:
    • Zhiyi Han
    • Wenxing Feng
    • Rui Hu
    • Qinyu Ge
    • Xinfeng Sun
    • Wenfeng Ma
    • Wei Zhang
    • Shaomin Xu
    • Bolin Zhan
    • Lai Zhang
    • Qun Li
    • Xiaozhou Zhou
  • View Affiliations

  • Published online on: October 20, 2021
  • Article Number: 1467
  • Copyright: © Han et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Circular RNAs (circRNAs) are differentially expressed in various cancer types. The present study aimed to investigate the expression and clinical implication of circRNAs in hepatocellular carcinoma (HCC) and to evaluate the potential of circRNAs as diagnostic biomarkers for HCC. CircRNA expression was profiled in 19 patients with HCC and 19 normal controls using ribosomal RNA‑depleted RNAs. Differentially expressed circRNAs (DE‑circRNAs) between HCC and controls were identified using CIRI2 and distinct circRNA expression signatures were screened. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used to predict the potential functions of these DE‑circRNAs and the circRNA‑miRNA‑mRNA regulatory networks were then constructed. Several DE‑circRNAs were selected and confirmed by RT‑qPCR. A total of 40 DE‑circRNAs (27 upregulated and 13 downregulated) were identified between patients with HCC and controls. Functional annotation indicated that these DE‑circRNAs were involved in cellular components, molecular functions and cancer‑associated pathways related to HCC. These included pathways in cancer, TNF signaling pathway, hepatitis B, hepatitis C and hepatocyte differentiation. The circRNA‑miRNA‑mRNA regulatory network was generated based on 11 candidate circRNAs. Receiver operating characteristic curve analysis indicated that Homo sapiens (hsa)_circ_0073239, hsa_circ_007090, hsa_circ_0008304, hsa_circ_0017586, hsa_circ_0000369 and hsa_circ_0001181 may serve as potential biomarkers for HCC. Results from Cell Counting Kit‑8 assay suggested that small interfering RNA targeting hsa_circ_0001181 reduced the proliferation of HepG2 cells, which implicated it as a potential therapeutic target for HCC. Therefore, in the present study, the differential expression pattern and important role of circRNAs in HCC were determined. The present results highlight the diagnostic potential of circRNAs in HCC and provide novel insight into the development of and treatment approaches for HCC.


Hepatocellular carcinoma (HCC) is one of the most common cancer types and a primary cause of cancer-associated mortality worldwide. HCC accounted for 9% of all cancer-related deaths worldwide in 2019 as per the World Health Organization report (1). Although considerable progress has been made in the diagnosis and treatment of liver cancers, the lack of good staging methods and efficient diagnostic markers results in poor prognosis (2-4). Therefore, there is an urgent requirement to identify novel molecular markers and related pathways that underlie the tumorigenesis and progression of HCC.

Recently, circular RNAs (circRNAs) have received increasing attention due to their potential function in gene regulation (5-7). circRNAs are non-coding RNAs with conserved, stable, abundant and tissue/developmental stage-specific expression (8,9). circRNAs are able to mediate rolling circle translation, regulate the transcription of host genes, participate in the formation of alternatively spliced mRNAs and serve as microRNA (miRNA/miR) sponges (8,10-12). circRNAs have key roles in regulating the biological development, pathogenesis and progression of numerous cancer types. Accumulating evidence has indicated that circRNAs are involved in normal physiological processes and in the development of various diseases, including liver cancer (13,14). In addition, aberrant expression of certain circRNAs has been demonstrated to be related to numerous diseases, including cancer, cartilage degradation, heart disease, neurodegenerative diseases and stroke (15-17). However, only a small amount of studies examined the circRNA expression pattern in peripheral blood mononuclear cells (PBMCs) and this has not been thoroughly studied in liver cancers (18).

Numerous studies have reported on the expression of circRNAs in cancer patients (19-21), but the findings are frequently inconsistent and no reliable biomarkers have been identified. These challenges may be attributed to several points and the key factors include individual differences in sampling and detection methods. The sample type constitutes another essential factor, as the commonly used specimens are tumor tissues. It is well known that tumor tissues are complex and contain multiple types of cells, such as immune cells, cancer cells, endothelial cells, fibroblasts and inflammatory cells (22,23). This may be a major reason for the inconsistencies in previous results. Heterogeneity may be eliminated in most studies by analyzing large lumps of tumor tissues (24,25). In the present study, PBMC samples were collected and examined using RNA-sequencing (RNA-seq), and differentially expressed circRNAs (DE-circRNAs) were identified. PBMCs have relatively simple components that reduce tumor heterogeneity considerably (26). In the present study, alterations in circRNA expression patterns in HCC were detected using RNA-seq. A Differential expression profile of circRNAs was identified in PBMCs from 19 clinical patients with HCC and 19 healthy controls. A total of 40 circRNAs with significant differential expression were identified. Considering that circRNA function is related to the function of the parent gene, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to predict the potential functions of the DE-circRNAs in HCC. Based on the results of the sequencing, a Bioinformatics analysis and reverse transcription-quantitative (RT-q)PCR, certain functions of these DE-circRNAs in HCC were revealed. Furthermore, the potential of the selected circRNAs as biomarkers was assessed.

Materials and methods

Patients and sample collection

A total of 22 healthy individuals and 28 patients with HCC who visited Shenzhen Traditional Chinese Medicine Hospital (Shenzhen, China) from July 2018 to May 2019 were enrolled in the study and fresh blood samples were collected in EDTA tubes after obtaining written informed consent. All patients with HCC were diagnosed according to Guidelines for Diagnosis and Treatment of Primary Liver Cancer in China (2017 Edition) (27) and most of the patients received surgery or interventional therapy before blood sampling. Patients were excluded if they were pregnant or lactating, had a previous history of or currently undergoing for other malignant tumors, had cardiac disease or serious and active infections. However, patients with hepatitis B virus (HBV) and hepatitis C virus (HCV) infections were not excluded. Individuals in the control group were enrolled from The Health Examination Center of Shenzhen Traditional Chinese Medicine Hospital at random during the same period, who were selected from the Health Examination Center database with recorded medical history after completing the informed consent form. All available information and clinical characteristics of the patients with HCC are listed in Table SI. The samples of 19 controls and 19 patients with HCC were measured using RNA-seq and assayed by reverse transcription-quantitative PCR. The patients with HCC were aged 14-82 years, with an average age of 55±18 years, whereas the healthy controls were aged 21-75 years, with an average age of 40±15 years. Among the 28 patients with HCC enrolled in this study, 20 were male and 8 were female. Of the 22 controls, 17 were male and 5 were female. The study was approved by the Ethics Committee of the Shenzhen Traditional Chinese Medicine Hospital (Shenzhen, China). All experiments were performed in accordance with the relevant guidelines and regulations established by the Ethics Committee.

Processing of blood and RNA extraction

Peripheral blood (2 ml, with EDTA anticoagulant) was collected from patients and matched healthy controls. Ficoll-Paque PREMIUM (Cytiva) was used for the isolation of PBMCs according to the manufacturer's instructions. Total RNA was isolated using TRIzol® (Invitrogen; Thermo Fisher Scientific, Inc.) reagent following standard procedures as previously described (28). The quality of RNA was determined by measuring the absorbance at 260 nm (A260) and the A280 using a NanoDrop ND-1000 (Thermo Fisher Scientific, Inc.), and RNA integrity was determined based on the RNA integrity number derived from the peak area of 28s RNA, 18s RNA and 5s RNA (RIN ≥7; Agilent 2100 RIN Beta Version Software; Agilent Technologies, Inc.).


A total of 2 µg of RNA sample from each subject was prepared as the input for RNA-seq. In the first step, ribosomal RNA (rRNA) was removed using Epicenter Ribozero™ rRNA Removal kit (Epicenter; Illumina, Inc.) and rRNA-free components were cleaned by ethanol precipitation. Next, the RNAs were submitted for library preparation according to the method described in a previous study (29). It was prepared using the VAHTS Total RNA-seq (H/M/R) Library Prep kit for Illumina (cat. no. NR603-01; Vazyme Biotech Co., Ltd.) according to the manufacturer's protocols. In brief, the following steps, including fragmentation, reverse transcription, adaptor ligation and preamplification, were performed sequentially. Finally, the libraries were purified, quality-tested and quantified using the Agilent Bioanalyzer 2100 system (Agilent Technologies, Inc.). The libraries were sequenced on Illumina HiSeq X Ten System by using HiSeq X Ten Reagent Kit (cat. no. FC-501-2501) with 2x150 bp paired-end technology.

Bioinformatics analysis

The quality of raw sequencing data in fastq format was assessed using FastQC software (v 0.11.5; SOAPnuke software (v2.0) was used to filter low-quality data (30). BWA software (v0.6; was used to align the clean reads with high quality to the human reference genome (GRCh38/hg38) (31,32) with default parameters. The unaligned sequencing data were then subjected to circRNA analysis by recognition of the reverse splicing event using the CIRI2 program (33). The CIRI algorithm identified circRNA candidates using two-step filtering. First, the paired chiastic clipping signals were collected and then the false-positive junctions were removed.

Reads per million mapped reads was used for quantification of circRNA expression (34). P-values for the differences in gene expression were adjusted by false discovery rate using the Benjamini-Hochberg procedure. P<0.05 and |log2 (fold-change)| >1 were used as the threshold for filtering the DE-circRNAs. The R package DESeq (1.10.1) was used to analyze the differential circRNA expression. Functional annotation and pathway enrichment of the parent genes of the DE-circRNAs were performed using KEGG Orthology-based annotation system (KOBAS 3.0) (35). GO and KEGG annotation and enrichment were also performed for the parent genes and corresponding target genes of circRNAs using the KOBAS software (corrected P-value <0.05) (36-38).

To investigate the correlation between circRNAs and mRNAs in patients with HCC, interactions among the DE-circRNAs were studied and the location of the related parent genes on exons and their corresponding target miRNAs were identified. TargetScan (version no 7.1; and miRanda (v3.3a; were used to predict the target miRNAs of these circRNAs. Cytoscape (v3.8.0; was used to illustrate the co-expression network (39,40).


Total RNA extracted from PBMCs and cell lines was subjected to RT-qPCR validation. Specific divergent primers spanning the back-splice junction sites of the circRNAs were designed. To detect the expression levels of the selected circRNAs, cDNA was synthesized using the PrimeScript™ RT reagent kit (Takara Biotechnology Co., Ltd.) by incubating at 37˚C for 15 min and then 85˚C for 5 sec to terminate the reaction, with a final volume of 20 µl. TB Green Premix Ex Taq™ II (Takara Biotechnology Co., Ltd.) was used for qPCR analyses on ABI 7500 thermal cycler according to the manual instruction (Thermo Fisher Scientific, Inc.). After predenaturing at 95˚C for 2 min, the reaction system were submitted to 94˚C for 15 sec and 60˚C, 45 sec, for 40 cycles. The internal control gene GAPDH was measured at the same time. The expression of different circRNAs was compared using the 2-ΔΔCq method (41). The primers (synthesized by Sangon Biotech) used in the present study are listed in Table I.

Table I

Primers for qPCR of the selected circRNAs.

Table I

Primers for qPCR of the selected circRNAs.

NameDirectionSequence (5'-3')

[i] F, forward; R, reverse; hsa, Homo sapiens; circ/circRNA, circular RNA; si, small interfering RNA.

Cell culture and transfection

To further explore the potential diagnostic roles of the DE-circRNAs in liver cancer, the human liver cancer cell line HepG2 was obtained from the Cell Bank of the Chinese Academy of Sciences. The cell line was authenticated via short tandem repeat testing by the Chinese Academy of Sciences.

Cells were cultured in DMEM (Corning, Inc.) supplemented with 10% (v/v) fetal bovine serum (Biological Industries) at 37˚C in a humidified atmosphere with 5% CO2.

Following confirmation by RT-qPCR, Homo sapiens (hsa)_circ_0001181 was selected for further study. To knockdown the expression of endogenous hsa_circ_0001181 in HepG2 cells, the hsa_circ_0001181 siRNA (corresponding double stranded oligo sequences are as follows, sense, 5'-CACCGAAGCTGGTTGATGATAATTCGAAAATTATCATCAACCAGC-3'; antisense, 5'-AAAAGCTGGTTGATGATAATTTTCGAATTATCATCAACCAGCTTC-3') was packaged with a pGLV2-U6 lentivector packaging plasmid mix (Sangon Biotech Co., Ltd.) in a 293T packaged cell line (cat. no. CRL-11268; ATCC). The routine culture conditions are as follows: DMEM + 10% FBS at 37˚C and 5% CO2. When packaging, one tube contained 20 µg lentiviral plasmid, 15 µg packaging vector (pCMV-dR8.9; Addgene, Inc.) and 10 µg envelope (pCMV-VSV-G; Addgene, Inc.) were prepared, then 500 µl Trans-EZ solutions (Sangon Biotech Co., Ltd.) was diluted with 4.5 ml Opti-MEM medium (cat. no. 31985; Gibco; Thermo Fisher Scientific, Inc.). The Trans-EZ solution was then added into the plasmid mix to obtain the transfection complex, it was incubated 20 min at room temperature; the mixture was added to 293T cell line and cultured for about 48 h at 37˚C and 5% CO2. The lentiviral particles were isolated by ultracentrifugation (4˚C, 82700 x g and 2 h). An empty vector was transfected as the negative controls. HepG2 cells were then transfected with the lentiviral vector particles (MOI=8). After incubation at 37˚C for 24-36 h, RT-qPCR was also performed to confirm the knockdown efficiency.

Proliferation assay

Proliferation of the transfected cells was measured using a Cell Counting Kit-8 (CCK-8; Dojindo Molecular Technologies, Inc.) according to the manufacturer's protocol. In total, ~5,000 cells were seeded into each well and after incubation at 37˚C for 24, 48 or 72 h in a 96-well microplate (Corning, Inc.), CCK-8 stain (5 µl) and 100 µl of fresh medium were added to each well, followed by incubation at 37˚C for 2 h. The absorbance was measured at 450 nm using a Multiskan FC microplate reader (Thermo Fisher Scientific, Inc.). HepG2 cells without any transfection served as the blank control and cells transfected with the empty vector were used as the negative control (NC) (42).

Statistical analysis

Data were analyzed using SPSS software (version 18.0; SPSS, Inc.) and graphs were generated using GraphPad Prism 8.0 (GraphPad Software, Inc.). Comparisons between the HCC and control groups were performed using Student's t-test. The results of the cell proliferation assay were compared between different groups by one-way ANOVA using Tukey's test. RT-qPCR data are presented as the mean ± standard deviation of at least three independent tests. Statistical significance was set at P<0.05. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic accuracy of the different genes analyzed.


CircRNA profiling of PBMCs from patients with HCC and normal controls

A total of 1,807.83 M raw reads with a mean length of 300 bp was obtained. Quality control analysis indicated that 93.64-95.19% of the sequencing data had a Q score of >30. The mean GC content of the clean reads for PBMCs from HCCs and controls ranged from 45.02 to 47.99%. Due to the high quality of the sequencing data obtained, the present results are reliable.

A total of 36,316 unique circRNAs were sequenced from the PBMC samples by counting the junction reads (count number >2) and a total of 2,239-9,897 circRNAs were identified from these 38 PBMC samples. Of these, 22,325 circRNAs were already listed in the circBase that were identified in previous studies and 13,991 novel circRNAs were identified in the present study. The host genes of the identified circRNAs were distributed across all chromosomes. Most of them contained two or more unique junction reads (Fig. 1A and B).

Multiple circRNAs may be generated from one gene as a result of the alternative back-splicing mechanism (43). In the present study, it was analyzed whether alternative back-splicing is related to circRNA diversity in PBMCs from patients with HCC. As presented in Fig. 1C, two or more circRNAs were produced from 68.3% of the host genes. Of note, >10 circRNAs were generated from certain genes. Most of the circRNAs were identified from the exonic regions and consisted of 2-5 exons. Among them, the circRNAs consisted of 2, 3, 4, 5 and 6 exons accounted for 55.38, 19.62, 8.46, 4.62 and 2.93% respectively (Fig. 1D and E). In addition, the lengths of most 71.8% circRNAs ranged between 200 and 6,000 nucleotides (Fig. 1F).

Detection of dysregulated circRNAs in PBMCs from patients with HCC

Among the circRNAs identified, only 608 and 474 circRNAs were co-expressed in all control and HCC samples, respectively. Of the co-expressed circRNAs, 27 upregulated and 13 downregulated circRNAs were determined to be differentially expressed between the HCC and healthy controls with a fold-change of ≥2.0 and P<0.05 by scatter plot filtering (Fig. 2A and Table II). Hierarchical clustering analysis of the abnormally expressed circRNAs suggested that the circRNA expression pattern was significantly different between patients with HCC and healthy controls (Fig. 2B).

Table II

Differentially expressed of circRNAs in peripheral blood mononuclear cells from patients with hepatocellular carcinoma.

Table II

Differentially expressed of circRNAs in peripheral blood mononuclear cells from patients with hepatocellular carcinoma.

CircRNACircbase IDHost gene symbolDirection of regulationAdj P-valueLocation
CHR6:31238850|31323369NovelHLA-CUp 3.91617x10-12n/a
CHR13:50571210|50618904 hsa_circ_0100694n/aUp 8.68089x10-5Intergenic region
CHR7:128218007|128278951 hsa_circ_0133256n/aUp 8.68089x10-5Intergenic region
CHR10:5815805|5842668 hsa_circ_0017586GDI2Up0.000268902Exon
CHR3:141811903|141820683 hsa_circ_0008304TFDP2Down0.000327428Exon
CHR21:30693542|30702014 hsa_circ_0001181BACH1Up0.000359788Exon
CHR5:82832826|82850857 hsa_circ_0073239VCANUp0.000409673Exon
CHR4:79747191|79772210 hsa_circ_0070190BMP2KUp0.000745549Exon
CHR11:128628010|128642880 hsa_circ_0000369FLI1Up0.000950703Exon
CHR10:28408580|28420620 hsa_circ_0093467MPP7Up0.000987788Exon
CHR8:22332467|22333137 hsa_circ_0083619PPP3CCDown0.00124762Exon
CHR9:35546427|35548532 hsa_circ_0002702RUSC2Up0.002681018Exon
CHR16:19627436|19659204 hsa_circ_0005699VPS35LUp0.002681018Exon
CHR2:228356263|228389631 hsa_circ_0058514AGFG1Up0.002889102Exon
CHR5:82815168|82818128 hsa_circ_0073239VCANUp0.003273089Exon
CHR4:17963526|17974508 hsa_circ_0069285LCORLDown0.003518927Exon
CHR13:21732061|21746820 hsa_circ_0007547SKA3Up0.004447874Exon
CHR12:66597491|66611015 hsa_circ_0005505IRAK3Up0.005338426Exon
CHR5:82832826|82838087 hsa_circ_0073239VCANUp0.005492673Exon
CHR9:99271955|99327765 hsa_circ_0087636CDC14BUp0.005492673Exon
CHR5:150838349|150859050 hsa_circ_0074623SLC36A1Up0.005492673Exon
CHR1:211526581|211527809 hsa_circ_0007234TRAF5Down0.005492673Exon
CHR4:146767108|146770713 hsa_circ_0003187ZNF827Down0.005492673Exon
CHR6:145992375|146007432 hsa_circ_0130925EPM2ADown0.005492673Exon
CHR13:95686859|95715113 hsa_circ_0008463ABCC4Up0.005715187Exon
CHR11:108200941|108202764 hsa_circ_0024235ATMDown0.005715187Exon
CHR18:2890559|2892484 hsa_circ_0004658EMILIN2Up0.006202165Exon
CHR2:174819601|174820960 hsa_circ_0002642SP3Up0.006497297Exon
CHR1:46032241|46032692 hsa_circ_0000067AKR1A1Down0.006946213Exon
CHR11:129979324|129992408 hsa_circ_0000372APLP2Up0.007668903Exon
CHR7:27668990|27672064 hsa_circ_0003958HIBADHDown0.007894677Exon
CHR8:67484717|67485741 hsa_circ_0136924MYBL1Down0.007894677Exon
CHR10:75830428|75834661 hsa_circ_0018881VCLUp0.008421014Exon
CHR14:91947920|91957146 hsa_circ_0003045PPP4R3AUp0.00909621Exon
CHR3:150834125|150845771 hsa_circ_0067735MED12LUp0.009420242Exon
CHR15:50923614|50941082 hsa_circ_0035249TRPM7Up0.009420242Exon

[i] hsa, Homo sapiens; circ/circRNA, circular RNA; n/a, not available; CHR, chromosome; Adj, adjusted.

The significantly dysregulated mRNAs are presented in the heatmap of the inter-sample correlation (Pearson's correlation coefficient). As indicated in Fig. 2C, the circRNA expression levels were significantly different between the HCC and control groups, whereas a slight variation was observed within each group.

Construction of the circRNA-miRNA-mRNA interaction network

circRNAs inhibit miRNA-mediated gene regulation by serving as miRNA sponges (44). The potential target miRNAs of HCC-specific circRNAs were identified using miRanda. Cytoscape was used to generate a circRNA-miRNA interaction network through the predicted miRNAs and circRNAs (39,40). All abnormally expressed circRNAs were predicted based on complementary sequences of miRNAs. A total of 514 miRNAs that are able to bind to circRNAs were identified. The complete network of 11 DE-circRNAs (circular nodes) and target miRNA (triangular nodes) interactions are delineated in Fig. 3A, where different colors of the miRNAs presented in the figure indicate differential expression in HCC compared with normal controls by miRNA sequencing. Increasing expression levels of the miRNAs is represented as changes in color increased from green to blue to red, as indicated in the legend bar on the top right corner of the figure.

To study the potential functions of the circRNAs, a regulatory network of circRNA-miRNA-mRNA was generated based on these circRNAs. Fig. 3B indicates the top 150 circRNA-miRNA-mRNA interactions with significant P-values (P<0.05). The results suggested that a single circRNA is able to interact with several miRNAs by acting as an antagonist. For instance, >50 miRNAs were predicted to bind to hsa_circ_0070190 and hsa_circ_0001181. Furthermore, both these circRNAs contained binding sites for hsa-miR-4659b-3p, hsa-miR-3614-3p and hsa-miR-4659a-3p. Different circRNAs with the same miRNA binding site may interact with one miRNA and regulate the expression level of a target gene in HCC. As presented in Fig. 3C, miRNA/mRNA interactions were predicted using the miRNA database (Fig. 3C).

As shown in Fig. 3D, functional annotation indicated that these abnormally expressed circRNAs have important roles in ‘nucleoplasm’, ‘cytoplasm’ and ‘nucleus’ of cellular components; ‘positive regulation of transcription from RNA polymerase II promoter’, ‘negative regulation of transcription from RNA polymerase II promoter’ and ‘positive regulation of cell proliferation’ of biological processes and molecular functions, including ‘protein binding’, ‘transcription factor binding’ and ‘protein kinase binding’. It was indicated that the target mRNAs were involved in cancer-related pathways, including the TNF, MAPK and the PI3K-AKT signaling pathways, which were implicated in carcinogenesis and the cell cycle (Fig. 3E). In addition, several pathways associated with HCC were found, including pathways in cancer, TNF signaling pathway, hepatitis B, hepatitis C and hepatocyte differentiation (Fig. 3E).

Validation of the DE-circRNAs

To verify the sequencing and prediction results, the expression levels of the top six circRNAs annotated in circBase were analyzed, including five upregulated and one downregulated circRNA from the 28 clinical cases with HCC and 22 healthy controls by RT-qPCR. GAPDH was used as an internal control. The results indicated that the relative expression levels of hsa_circ_0017586, hsa_circ_0001181, hsa_circ_0073239, hsa_circ_0070190 and hsa_circ_0000369 in PBMCs from patients with HCC were significantly higher compared with those in healthy controls, whereas hsa_circ_0008304 was downregulated in patients with HCC compared with that in the controls (Fig. 4A). These results are consistent with the results of the RNA-seq analysis (Fig. 4B).

ROC curve analysis was performed to evaluate the potential of the circRNAs as diagnostic biomarkers in all 50 samples (28 HCC and 22 controls) collected for the present study. As presented in Fig. 4C, the area under the ROC curve to differentiate patients with HCC from healthy controls was 0.690 (95% CI: 0.622-0.758) for hsa_circ_0073239, 0.888 (95% CI: 0.847-0.928) for hsa_circ_0070190, 0.918 (95% CI: 0.881-0.956) for hsa_circ_0000369, 0.811 (95% CI: 0.757-0.865) for hsa_circ_0008304, 0.829 (95% CI: 0.777-0.881) for hsa_circ_0017586 and 0.940 (95% CI: 0.913-0.967) for hsa_circ_0001181, indicating the potential of these circRNAs as diagnostic markers for HCC. Among them, hsa_circ_0001181 and hsa_circ_0000369 had the highest potential as diagnostic markers and may be used for diagnosing HCC.

circRNAs may be involved in the regulation of HCC cell growth

The results of the CCK-8 proliferation assay (Fig. 5A) were analyzed by ANOVA. It was indicated that significant differences were present between different groups and different times (P<0.001), suggesting that the knockdown of hsa_circ_0001181 expression in HepG2 cells significantly reduced cell growth compared to the blank (no transfection) and NC (transfected with empty pGLV2-U6 lentivector) groups, particularly at 48 and 72 h. There was no significant difference between the blank and NC groups (P=0.056), suggesting that cell proliferation was specifically affected by hsa_circ_0001181 and not by the vector. RT-qPCR was performed to confirm the knockdown efficiency and the results indicated that the expression level of hsa_circ_0001181 was significantly downregulated following hsa_circ_0001181 silencing (Fig. 5B).


CircRNAs were once thought of as by-products of aberrant RNA splicing, but with the development of RNA-seq and bioinformatics technologies in the past few decades, a number of circRNAs have been functionally characterized, revealing their ability to adsorb to miRNA (45) and regulate the expression of the target gene(s) (46). Studies have also indicated that circRNAs may be translated into functional proteins (47). In addition, studies have revealed that circRNAs are highly abundant, stable and resistant to exonuclease (8,48). Furthermore, differences in circRNAs exist among different tissues and diseases (49,50). Thus, circRNAs have demonstrated great potential for application in early diagnosis and as therapeutic targets, and it is worthwhile investigating their mechanistic involvement in disease occurrence and development.

Several studies on cancers have focused on PBMCs (51-53). Certain studies have also reported an association with immunological diseases. PBMCs consist of monocytes and lymphocytes, which originate from the innate and adaptive immune systems. Changes in gene expression in PBMCs are generally associated with the status of tumors and tumor-related immune diseases (54), suggesting that the DE-circRNAs in PBMCs may be important potential biomarkers for the diagnosis and prognosis of liver diseases.

In the present study, circRNAs were identified in PBMCs from 19 patients with HCC and 19 controls using RNA-seq. Profiling of the DE-circRNAs led to the identification of 40 DE-circRNAs between HCC and healthy controls. Among them, 27 were upregulated and 13 were downregulated and of these, certain selected DE-circRNAs were confirmed by RT-qPCR. For the ROC analysis, 50 PBMCs samples were involved, which includes 38 samples were used for sequencing and another 12 PBMCs samples collected. It is a limitation of the present study that there was no separate cohort for RT-qPCR validation and further patients with HCC will be collected for future study. Among the DE-circRNAs, hsa_circ_0001181 and hsa_circ_000369 demonstrated a high diagnostic potential in patients with HCC and represent potential diagnostic biomarkers for HCC prediction. A CCK-8 assay was performed and the results indicated that silencing of hsa_circ_0001181 reduced the proliferation of HepG2 cells. This suggests that circRNA 0001181 may be involved in the genesis and development of HCC and may thus serve as a potential therapeutic target for HCC.

To investigate the potential function of these circRNAs profiled, a circRNA-miRNA-mRNA interaction network was constructed. Most of the circRNAs were newly identified to be related to HCC. As one of the types of competing endogenous RNAs, circRNAs have important roles in regulating gene expression through sequestering miRNAs. Numerous miRNAs in the network were studied previously, such as hsa-miR-30a-3p, which had been reported to be related to numerous cancer types, including lung (55), breast (56), prostatic (57) and pancreatic cancer (58). It has also been reported to be related to hepatic astrocyte fibrosis and autophagy (59). It is the target of hsa-circ-0001181, hsa-circ-0073239 and hsa-circ-0000284, which were determined to be significantly dysregulated in HCC in the present study. It is inferred that these circRNAs may have important roles in the genesis and development of tumors in numerous cancer types.

In the present study, >100,000 circRNAs were profiled from PBMCs of patients with HCC and only those with counts of >2 were considered to be reliable circRNAs for the follow-up experiments. Differential expression analysis was performed only for these co-expressed circRNAs and this may be the main reason why only 40 DE-circRNAs were identified in the present study.

Although only a limited number of DE-circRNAs were identified in the present study, most of them were detected for the first time in patients with HCC and they were confirmed by RT-qPCR. Furthermore, GO functional annotation results indicated that these DE-circRNAs were mainly enriched in protein binding, regulation of cell proliferation, signal transduction, protein phosphorylation, hepatocyte differentiation, among others, and were mainly involved in cancer-associated pathways related to HCC. These included pathways in cancer, the TNF signaling pathway, ErbB signaling pathway, MAPK signaling pathway, hepatitis B, hepatitis C and hepatocyte differentiation, which indicates that the host genes of these circRNAs may participate in the regulation of HCC development through related molecular functions or signaling pathways. Ni et al (60) reported that lncRNA inhibits HCC progression through CUL4A-mediated LATS1 ubiquitination and increased YAPS127 phosphorylation. Wang et al (61) indicated that SIRT4 inhibits glutamine metabolism and increases the level of adenosine diphosphate/adenosine monophosphate (AMP) and blocks the mTOR signaling pathway through phosphorylation of AMP kinase α that has an antitumor effect in HCC.

In the present study, circRNA expression patterns in PBMCs from HCC were determined by RNA-seq, indicating that patients with HCC had distinct circRNA expression signatures when compared to healthy controls. ROC analysis indicated that hsa_circ_0073239, hsa_circ_007090, hsa_circ_0008304, hsa_circ_0017586, hsa_circ_0001181 and hsa_circ_0000369 may potentially be used as biomarkers for HCC diagnosis. In addition, silencing of hsa_circ_0001181 reduced the growth of HepG2 cells, suggestive of its potential therapeutic target of HCC.

Supplementary Material

Clinicopathological data of patients with HCC and healthy controls.


Not applicable.


This research was funded by the Shenzhen Science and Technology Project and Sanming Project of Medicine in Shenzhen (grant nos. JCYJ20170817094901026, JCYJ20180302173542393 and SZSM201612074).

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the NCBI project repository (accession no. PRJNA754685;

Authors' contributions

XZ and ZH contributed to the conception, design, performing of experiments and analysis of the data. XS, RH and WM contributed to sample collection and cell culture. WZ, SX, BZ, LZ and QL contributed to sample collection and CCK-8 assay. QG, LZ and ZH contributed to the bioinformatics analysis. WF and RH contributed to the acquisition of data and cell culture. ZH and WF confirm the authenticity of all the raw data. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of Shenzhen Traditional Chinese Medicine Hospital (Shenzhen, China). Written informed consent was obtained from individual or guardian participants.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.



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Han Z, Feng W, Hu R, Ge Q, Sun X, Ma W, Zhang W, Xu S, Zhan B, Zhang L, Zhang L, et al: CircRNA expression profiling of PBMCs from patients with hepatocellular carcinoma by RNA‑sequencing. Exp Ther Med 22: 1467, 2021
Han, Z., Feng, W., Hu, R., Ge, Q., Sun, X., Ma, W. ... Zhou, X. (2021). CircRNA expression profiling of PBMCs from patients with hepatocellular carcinoma by RNA‑sequencing. Experimental and Therapeutic Medicine, 22, 1467.
Han, Z., Feng, W., Hu, R., Ge, Q., Sun, X., Ma, W., Zhang, W., Xu, S., Zhan, B., Zhang, L., Li, Q., Zhou, X."CircRNA expression profiling of PBMCs from patients with hepatocellular carcinoma by RNA‑sequencing". Experimental and Therapeutic Medicine 22.6 (2021): 1467.
Han, Z., Feng, W., Hu, R., Ge, Q., Sun, X., Ma, W., Zhang, W., Xu, S., Zhan, B., Zhang, L., Li, Q., Zhou, X."CircRNA expression profiling of PBMCs from patients with hepatocellular carcinoma by RNA‑sequencing". Experimental and Therapeutic Medicine 22, no. 6 (2021): 1467.