Cancer Translational Medicine

Original Research | Open Access

Vol.9 (2023) | Issue-1 | Page No: 1-18


Potential role of CircMET as A Novel Diagnostic Biomarker of Papillary Thyroid Cancer

Yan Liu1,2,3,4#, Chen Cui1,2,3,4#, Jidong Liu1,2,3,4, Peng Lin1,2,3,4,Kai Liang1,2,3,4, Peng Su5, Xinguo Hou1,2,3,4, Chuan Wang1,2,3,4, Jinbo Liu1,2,3,4, Bo Chen6, Hong Lai1,2,3,4, Yujing Sun1,2,3,4* and Li Chen 1,2,3,4*


1. Department of Endocrinology, Qilu Hospital of Shandong University, Jinan, Shandong, China

2. Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, Shandong, China

3. Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province medicine & health, Jinan, Shandong, China

4. Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, Shandong, China

5. Department of Pathology, Qilu Hospital of Shandong University, Jinan, Shandong, China

6. Department of Thyroid Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China

#These authors contributed equally to this work and should be considered as equal first coauthors.

*Corresponding Author


Address for correspondence: Prof. Li Chen, Department of Endocrinology, Qilu Hospital of Shandong University, No. 107 Wenhua West Road, Lixia District, Jinan 250012, Shandong, China. E-mail:

Dr. Yujing Sun, Department of Endocrinology, Qilu Hospital of Shandong University, No. 107 Wenhua West Road, Lixia District, Jinan 250012, Shandong, China. E-mail:

Important Dates  

Date of Submission:   12-Dec-2022

Date of Acceptance:   30-Jan-2023

Date of Publication:   31-Mar-2023


Aim: To study the circular RNAs (circRNAs) expression profiles and their potential role in the diagnosis and treatment of papillary thyroid cancer (PTC).

Methods: From June 2019 to July 2021, 68 patients diagnosed with PTC at Qilu Hospital were selected for the study. High-throughput sequencing determined the PTC and paired tissue samples' circRNAs expression profiles. Bioinformatics analyzed the differentially expressed circRNAs, miRNAs, and mRNAs. Upregulated circRNA expression levels were verified using qRT-PCR. GEPIA analyzed host gene expression and survival curves. circMET (hsa_circ_0082002) expression was confirmed by fluorescence in situ hybridization and qRT-PCR in the thyroid tissues and cell lines of PTC.

Results: Compared with benign thyroid tissues, 89 circRNAs were significantly upregulated and 14 were downregulated in PTC tissues (P<0.05). The network map identified 7 top upregulated circRNAs and their expressions were further verified using qRT-PCR. In addition, they were also highly expressed in other PTC patients. GEPIA showed that the host gene MET of circMET was significantly expressed in the PTC group compared with the control group (P<0.05). Silencing of circMET inhibited cell proliferation and disrupted the cell cycle in TPC-1 cells.

Conclusions: circMET may serve as a diagnostic or predictive biomarker of PTC and targeting circMET may be a promising therapeutic strategy.

Keywords: Papillary thyroid carcinoma, circular RNAs, high-throughput sequencing


Thyroid cancer is the most common malignant tumor of the endocrine system. Among malignant tumors in women, thyroid cancer's incidence has increased at the quickest rate over the last 20 years.[1] Thyroid cancers account for approximately 1.5% to 2.1% of all cancers diagnosed annually worldwide.[2] According to the National Cancer Center, there were 6,800 thyroid cancer fatalities in China in 2015.[3] Papillary thyroid cancer (PTC) is the most common subtype compared to follicular and medullary types and has a high recurrence rate. PTC is generally associated with regional or distant lymph node metastasis, trachea invasion, bone metastasis, and brain metastasis,[4] and the incidence of distant cervical lymph node metastasis is as high as 90%.[5] Lymph node metastasis after surgical treatment in patients with PTC is independently and significantly negatively correlated with their survival. Early diagnosis and appropriate treatment can improve the outcomes and reduce the mortality of PTC patients.[6] Therefore, early diagnosis of PTC is a critical issue to be solved.

Circular RNAs (CircRNAs) are a kind of non-coding RNA (ncRNA) and are expressed in many biological cells. It has attracted the interest of researchers in recent years, primarily owing to its molecular and biological properties.[7] They are tissue-specific molecules produced by a special variable splicing mechanism. It has the characteristics of stable structure, conservation of sequence, and high specificity of cells or tissue. With the continuous development of sequencing technology, circRNAs have been found to play a key role in various biological processes through spongy microRNAs (miRNAs), regulating transcription or binding to proteins. Cancer has always been a kind of disease that seriously affects and disturbs human beings. It is becoming increasingly recognized that circRNAs are differentially expressed in cancer tissues and participate in the occurrence and development of tumors, which is expected to become a new molecular marker.[8] The expression profiles of circRNAs associated with PTC were identified, and the related bioregulatory networks were constructed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.[9] One study suggested that circ_0058124 is a potential molecular marker playing a role in the diagnosis and treatment of PTC.[10] However, the specific mechanism of action of circRNAs in PTC is still unknown, and the expression profiles of circRNAs and their roles in the development of PTC must be investigated further.

In this study, we evaluated the expression profiles of circRNAs in PTC and adjacent normal tissues using high-throughput RNA sequencing technology and validated the differentially expressed circRNAs by quantitative real-time polymerase chain reaction (qRT-PCR). Using gene enrichment and pathway analyses, we then looked at the relationship between circRNAs and messenger RNAs(mRNAs) as part of a potential circRNA-miRNA-mRNA regulatory axis related to PTC carcinogenesis and progression. The findings of the study shed light on the molecular events that happened during the development and prognosis of PTC.


Patients’ information

From June 2019 to July 2021, we recruited 68 patients who underwent fine-needle aspiration biopsy (FNAB) at Qilu Hospital's Department of Endocrinology; This research was approved by the Ethics Committee of Qilu Hospital, Shandong University, with the approval number KYLL-2018(KS)-226. Written informed consent was obtained from all the patients participating in this study before they underwent FNAB.

A total of 33 patients were excluded because they did not have surgery, they withdrew, or their samples were unsuitable. After excluding an additional set of four failed samples, we obtained 31 pairs of PTC and adjacent normal tissues. High-throughput sequencing was performed on four PTCs and their adjacent normal tissues. Further validation was performed on the remaining 27 pairs of PTC and adjacent normal tissues. All samples were observed by two pathologists independently and identified as PTC. All FNAB samples were stored immediately with Total RNA Extraction Reagent (TRIzol) (Thermo Fisher Scientific, Waltham, MA, USA) and kept at 80°C until use. The experimental workflow analysis of FNAB samples is shown in Figure 1.

Figure 1.
Figure 1. Experimental workflow of high-throughput sequencing analysis of FNAB samples. The screening process for FNAB samples is depicted as a flowchart. The original sample was unfit for analysis due to a lack of tissues. The sample cancellation (n = 4) was caused by an unsuccessful RNA extraction.

Inclusion and exclusion criteria

The inclusion criteria were as follows: 1) All samples were confirmed as PTC by surgery and pathology; 2) No infection when FNAB in the biopsy location; 3) There were no other clotting abnormalities. The exclusion criteria were as follows: 1) Infection at the site of the puncture; 2) Severe bleeding disorders; 3) non-PTC organization type.

Hematoxylin and eosin staining

Tumor tissues were dissected and preserved in 4% PFA at 4°C for 24 h before being dehydrated with xylenes and alcohols and embedded in paraffin. For histological examination, sections were cut at 5 μm and stained with hematoxylin and eosin (HE). Images were captured using a Leica DM3000 microscope (Leica, Wetzlar, Germany).[11]

Immunofluorescence assay

Tissues were first fixed in 4% PFA for 20 min and then permeabilized with 0.2% Triton X-100 for 30 min. After being blocked with 5% goat serum for 2 h, cells were incubated with primary antibody rabbit polyclonal anti-Ki67 antibody (dilution 1:100; cat. no. TA314198, OriGene Technologies, Inc.) overnight at 4°C in a humidified chamber. Then, slices were washed using 1× PBS three times (5 min each) and incubated with secondary antibody goat anti-rabbit IgG H&L (Alexa Fluor® 594; dilution 1:300, cat. no ab150080, Abcam, Cambridge, MA, USA) for 2 h at room temperature. At last, DAPI (dilution:1:10,000; cat. no. D9542, Sigma-Aldrich, St. Louis, MO, USA) was used for nuclei staining. The images were taken with an Olympus IX81 microscope (Olympus Corporation, Tokyo, Japan).

Isolation of total RNA and quality control

RNA isolation was performed in an RNA-specific workspace at 4°C temperatures. Total RNA was purified from all samples using TRIzol (Thermo Fisher Scientific, Pudong, Shanghai, China). The RNA quality was measured by OD260/280 ratios and OD260/230 ratios using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). The total RNA concentration and quality were assessed for each sample. OD260/280 ratios between 1.8 and 2.1 were considered acceptable, and OD260/230 ratios greater than 1.8 were considered acceptable. RNA integrity and DNA contamination were then assessed using denaturing agar gel electrophoresis.

High-throughput sequencing analysis

Four pairs of PTC and paired tissues were used for high-throughput sequencing to investigate the differentially expressed circRNAs between cancer tissues and paracancerous tissues. Sequencing was performed by Shanghai Jing Zhou Gene Technology Co.; Ltd. RNA was sequenced on an Illumina Novaseq 6000 sequencer. The sequencing process was completely controlled by the data acquisition software provided by Illumina (San Diego, CA, USA), and the sequencing results were analyzed in real time. Differential expression analysis of circRNAs was performed using edgeR software. To obtain differentially expressed circRNAs, the screening conditions were set as the fold change of cancer tissues relative to the expression level of normal tissues ≥ 2 and P < 0.05.

CeRNA network construction and functional enrichment analysis

The regulatory network of miRNA sponge adsorption was established using regression model analysis and sequence matching based on gene expression values, and the core endogenous competitive RNAs (ceRNAs) were identified. We used Cytoscape 3.4.0 to construct a biological interaction network. We used this network to predict which microRNAs are bound by the differential circRNAs by analyzing the gene regulatory interactions and identifying the core regulatory genes.According to the location information of circRNA on the genome, the corresponding gene information can be obtained. The genes coding for the differential circRNAs were annotated using the Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes, databases.

Reverse transcription and quantitaive real-time PCR (qRT-PCR) validation

RNA was reverse transcribed to cDNA using EvoM-ML Reverse Transcription Kit II [AG11711, Accurate Biotechnology (Hunan) Co., Ltd], at 4°C according to the manufacturer’s instructions. A reaction solution volume of 20 µL was obtained. The reaction conditions were as follows: 37°C for 15 min followed by 85°C for 5 s, and then storage at 4°C. qRT-PCR was performed using the SYBR Green Pro Taq HS premixed qPCR kit (Takara, Japan) in a Light Cycler 480 System (Roche Diagnostics, Germany), with a 10 µL PCR reaction mixture including cDNA (1 µL). The reaction mixture was incubated in a Micro Amp TM Fast Optical 96-well reaction plate at 95°C for 5 min, followed by 35 cycles of 95°C (10 s), 60°C (30 s), and 72°C (30 s). Each experiment was repeated three times. The 2−∆∆Ct method was used to calculate the relative RNA expression levels. The expression levels of circRNAs were normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The GAPDH and circRNAs primers sequences are listed in Table 1.

Table 1.
Table 1. Sequences of GAPDH and circRNA primers designed for RT-PCR amplification

Analysis of host genes expression and survival

Through the analysis of the relevant PTC data in the GEPIA platform for the Cancer Genome Atlas (TCGA) database (, the significant differential expression and survival lines of host genes between the tumor and the control group were analyzed.

Fluorescence in situ hybridization (FISH)

CircMET probe was synthesized by the Suzhou Gemma Gene Co., Ltd. and used for the qualitative detection of circMET in the PTC tissues and cell lines. The circMET probe sequence was 5’ to 3’ (GGT + TTATCCTAT + TAAAGCAGTGCTCAGAGAGGT + TTA + TCC + TAT + TAAAGCAGTCAT + TATGAGAGGT + TTA + TCCTAT + TAA).

Cell transfection

Three PTC cancer cell lines—TPC-1, SW-579, and B-CPAP—and the benign human thyroid follicular cell line Nthy-ori 3-1 were purchased from the American Type Culture Collection (ATCC) and maintained in Roswell Park Memorial Institute (RPMI) modified medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/ streptomycin. Cell culture was performed at 37°C in a humidified CO2 incubator (5%). Cell identities were authenticated by short tandem repeat profiling and potential mycoplasma contamination was excluded by regular PCR methods. Exponential cells were used for all analyses. SiRNAs targeting the junction site of circMET were purchased from Suzhou Gemma Gene Co., Ltd [Table 2]. All oligonucleotides and vectors were transfected into the TPC-1 cell line using Lipofectamine 3000 (Invitrogen, Waltham, MA, USA). After 24 h, their respective transfection efficiencies were tested by qRT-PCR.

Table 2.
Table 2. Sequences of SiRNAs targeting to the junction site of circMET

Cell proliferation and cell cycle analysis

Proliferation curves of PTC cell lines were determined by Cell Counting Kit‐8 (CCK‐8; Beyotime, Haimen, China) followed by the official directions. Specifically, the cells were cultured at 2 × 103 cells per well in triplicate in 96-well plates. After 0, 24, 48, and 72 h culturing, the OD value was detected at 450 nm after incubating the cells with CCK-8 reagent at 37°C for 2 h. Flow cytometric assay was carried out using Cell Cycle and Apoptosis Detection Kit (Meilune, Cat: MA0334, China) following the manufacturer's protocol (BD Biosciences, San Jose, CA).

Statistical analysis

GraphPad Prism 7.0 (GraphPad Software, San Diego, CA, USA) and SPSS version 21.0 software (SPSS Inc., Chicago, IL, USA) were used for statistical analysis. Continuous variables were expressed as mean ± standard deviation, and t-tests were performed to evaluate the differential expression of circRNAs between PTC and normal tissues. The average expression of circRNAs was used to define the low or high expression. P < 0.05 was considered to indicate statistical significance.


General clinicopathological characteristics of the patients

PTC tissue samples were collected by FNAB. The postoperative pathological analysis confirmed the results. The histomorphological characteristics of the PTC are shown in Supplementary Figures 1A and 1B. Table 3 demonstrates the clinicopathological characteristics of the patients. Of the enrolled patients, 25 were female. A pathologist classified all the samples as typical PTC with varying stages of tumor node metastasis. Six cases were multifocal, and 10 cases had a capsular invasion. The immunofluorescence staining of the PTC with Ki-67 and DAPI showed the proliferation characters of the PTC tissues [Supplementary Figure 1C].

Table 3.
Table 3. Participants’ clinicopathological characteristics (n = 31)

Analysis of circRNAs profiles

Based on pathology, we chose four pairs of samples for high-throughput sequencing. The heatmap [Figure 2A] and volcanic plot [Figure 2B] show the expression profiles of mRNAs in PTC and adjacent normal tissues. A total of 2,339 differentially expressed mRNAs (248 downregulated and 2,091 upregulated) were identified in PTC compared with normal tissues. The heatmap [Figure 2C] and scatter plot [Figure 2D] show the expression profiles of circRNAs in PTC and adjacent normal tissues. In PTC and adjacent thyroid tissues, a total of 5,835 circRNAs were identified. Among them, 103 circRNAs are differentially expressed, 14 of which were significantly downregulated and 89 of which were significantly upregulated in PTC.

CircRNA genes were distributed in nearly every human chromosome, including 22 autosomes and the sex chromosomes [Figure 2E]. The differentially expressed circRNAs were found primarily in exons, which accounted for approximately 94% of the total. We further screened out 14 significantly upregulated circRNAs. [Table 4].

Figure 2.
Figure 2. Identification of differentially expressed circRNAs. (A) Heatmap and (B) Volcano plot showing the differential expression of mRNAs in PTC and adjacent normal tissues. P < 0.05 and fold change (FC) ≥ 2. The red and blue colors indicate high and low expression, respectively. (C) Heatmap and (D) Scatter plot showing the differential expression of circRNAs in PTC and adjacent normal tissues. P < 0.05 and fold change (FC) ≥ 2. The red and blue colors indicate high and low expression, respectively. (E) Genomic coverage distribution of sample sequences and circRNA classification. The outermost circle represents the chromosome distribution, and each color inside the circle represents the coverage distribution of a sample sequence on the chromosome. The differentially expressed circRNAs are divided into three categories: exons, introns, and intergenic. The horizontal axis represents the different regions of the genetic element, and the ordinate represents the number of reads.

Table 4.
Table 4. Expression of the top 14 circRNAs in the PTC and paired adjacent tissues

The ceRNA network

We predicted potential miRNAs with differentially expressed circRNAs through sequence alignment [Supplementary Figure 2]. Then we constructed a circRNA and mRNA network to further study the interaction between circRNA and mRNA in PTC [Supplementary Digure 3]. Then, we built a circRNA-miRNA-mRNA network with the top 14 most significantly upregulated circRNAs [Figure 3A]. According to the putative binding site location in the circRNA sequence, the miRNA predicted by circMET are hsa-miR-6796-5p and hsa-miR-1197. We also found several potential axes in these ceRNA networks, namely circMET/hsa-miR-1197/B3GNT3, hsa-miR-6796-5p/CDH3, and circMCTP2/hsa-miR-1200 /MMP7.

Figure 3.
Figure 3. circRNA–miRNA–mRNA network map and biological analysis. (A) Fourteen upregulated circRNAs were used to construct the circRNA–miRNA–mRNA network. The network map contains (represented by red rectangles) downstream miRNAs (represented by blue triangles) and target mRNA (represented by a red oval). (B) Differential circRNA GO enrichment analysis of the first 30 biological processes. (C) Differential circRNA parental gene enrichment results of the first 30 pathways.

Enrichment analysis

We performed GO and KEGG pathway analyses on the circRNA genes. These were annotated by GO based on their biological process, cellular component, and molecular function. Genes were annotated to the cell (GO:0005623, P = 5.523E-01), protein binding (GO:0005515, P = 3.005E-03), response to stimulus (GO:0050896, P = 5.881E-05), and regulation of cellular process (GO:0050794, P = 6.073E-02) [Figure 3B]. We also enriched the host gene MET associated with the regulation of gene expression, metabolic process, and cellular process.

We used KEGG to analyze the genomic pathways of 30 closely related circRNA genes. Some critical pathways, such as cell adhesion molecules, focal adhesion, and ECM-receptor interactions, were enriched. These pathways are associated with tumor initiation, development, invasion, and metastasis [Figure 3C].

Q-RT-PCR validation of differentially expressed circRNAs

To validate these findings, we chose 7 top upregulated circRNAs in the PTC and adjacent tissues based on fold change values: circARHGAP5 (P < 0.0001), circMET (P < 0.0001), circMCTP2 (P < 0.0001), circSKIL (P < 0.0001), circPTK2 (P < 0.0001), circFNDC3B (P < 0.0001), and circMYO9A (P < 0.0001). The P value was <0.01, which was consistent with the trend in the sequencing results [Figure 4].

Figure 4.
Figure 4. Validation of differential circRNA expression in PTC and adjacent normal tissues. According to the high-throughput sequencing results, 7 circRNAs were identified, and 15 pairs of PTC and normal control tissues were used for qRT-PCR verification. The expression level of circRNAs in PTC was compared with that in normal tissues. Real-time quantitative reverse transcription PCR results were evaluated using the 2−∆ΔCT method, and the results were expressed as mean ± standard deviation (**P < 0.01).

Host gene expression analysis

Using the GEPIA platform analysis of the TCGA database, we examined the most critical genes based on the 7 circRNAs results, including fibronectin type III domain containing 3B (FNDC3B), SKI like proto‑oncogene (SKIL), multiple C2 and transmembrane domain containing 2 (MCTP2), MET proto-oncogene, receptor tyrosine kinase (MET), protein tyrosine kinase 2 (PTK2), myosin IXA (MYO9A) and Rho GTPase activating protein 5 (ARHGAP5). Only the differential expression of MET was found to be significant among the host genes of differentially expressed circRNA [Figure 5]. Then, we calculated overall survival (OS) rates based on the GEPIA software. In the TCGA database, PTC patients with higher FNDC3B and MYO9A expression had relatively low OS rates (hazards ratio HR =1.5 and 2.2, respectively) [Supplementary Figure 4A, 4F]. PTC patients with higher expression of MCTP2 and MET had relatively increased OS rates (HR = 0.54 and 0.52, respectively) [Supplementary 4C, 4D]. And the other genes expressed no change in the OS rates [Supplementary Figure 4B, 4E, 4G].

Figure 5.
Figure 5. Host gene expression level in PTC and adjacent normal tissues. (A-G) Overall analysis of host gene (ARHGAP5, FNDC3B, MCTP2, MET, MYO9A, PTK2, SKIL) expression level of patients with PTC by the Cancer Genome Atlas (TCGA) cohort.

Function analysis of the circMET in PTC patient tissues and cell lines

According to TCGA database analysis and the above results, we chose circMET as a promising circRNA. We confirmed that circMET expression was augmented in PTC tissues compared to non-cancer tissues through FISH [Figure 6A]. We used qRT-PCR to detect the circMET mRNA expression, and the results showed that in SW-579 (P = 0.0006), TPC-1 (P < 0.0001), and B-CPAP (P = 0.0028) cells line the circMET mRNA level was significantly higher than that in Nthy-ori3-1 cells line [Figure 6B] and selected the TPC-1 cell line for further study. Three siRNAs targeting the circMET junction site were designed, of which si-circRNA2 (P < 0.0001) and si-circRNA3 (P < 0.0001) were selected for further investigation because they have a higher silencing efficiency in TPC-1 cells compared with si-circRNA1 (P = 0.0023). The qRT-PCR and FISH results showed that the expression of circMET was significantly downregulated in the TPC1 cell line transfected with siRNA fragments and was localized in the cytoplasm of the protocell [Figure 6C, 6D].

In the proliferation experiment, we used the CCK-8 assay to conduct the growth curve. The CCK-8 results demonstrated that silencing of circMET significantly inhibited the proliferation of TPC-1 cells [Figure 6E] (24 h: P = 0.0028; 48 h: P < 0.0001; 72 h: P < 0.0001). The cell cycle analysis revealed that after silencing of circMET, TPC-1 cells distributed more in the G1 (P = 0.4922) and less in the S (P = 0.2800) and G2/M (P = 0.0006) phase, which indicated that TPC-1 cells were arrested in the S phase by silencing circMET [Figure 6F, 6G].

Figure 6.
Figure 6. Silencing of circMET affects cell proliferation and cell cycle. (A) Differential expression of circMET in PTC and normal tissues was analyzed by FISH. (B) The relative expression of circMET in each cell line was detected by qRT-PCR. (C) The expression of circMET in TPC1 cells transfected with si-circRNA and si-NC was analyzed by qRT-PCR. (D) circMET expression of si-NC and si-RNA2 add si-RNA3 in TPC1 cell line after transfection. (E) The growth curves of si-circ and si-NC cells were detected by the experimental method of CCK-8. (F-G) After F-G transfection of si-circRNA and si-NC, flow cytometry was used to analyze cell cycle progression. *P < 0.05, **P < 0.01, ***P < 0.001.


In recent years, an increasing number of studies have focused on circRNAs owing to their particular biological properties.[12] CircRNAs are highly stable and conservative ncRNAs with the classic ring structure and play an important role in the pathogenesis and development of various types of human cancer.[13],[14] Former studies have reported dysregulations in circRNA expression levels in several cancers, such as breast cancer,[15] cervical cancer,[16] and esophageal cancer.[17] Analysis of circRNA expression profiles in PTC based on microarray and RNA deep sequencing have been reported previously.[18],[19],[20] However, how circRNAs participate in PTC occurrence and development remains unknown and deserves further investigation.

Compared with the previous sequencing, we measured a greater number of differentially expressed circRNAs in PTC with strict threshold quality control, which allows us to explore the potential key circRNA that participates in PTC. Compared to normal tissues, 103 differentially expressed circRNAs and 2,339 differentially expressed mRNAs (FC > 2.0, P < 0.05) were determined in PTC, of which 89 circRNAs and 2,091 mRNAs were significantly upregulated, while 14 circRNAs and 248 mRNAs were significantly downregulated. The expression levels of circPSD3, circPSD3, circPTPRM, and circMET were recently reported to be upregulated in PTC, which is in line with our data.[20],[21] According to the report, the circPTK2/miR-758-3p/MYC axis promotes the proliferation of acute myeloid leukemia.[22] circPTK2 upregulates the CCL4-CCR5 axis through miR-125a-3p to promote the growth and invasion of lung adenocarcinoma.[23] The expression levels of circFN1, circFAM120B, circCCT4, and circRASSF2 were also reported to be upregulated in PTC and involved in promoting PTC progression but showed no significant difference in our results.[24],[25],[26],[27]

As circRNAs are transcribed from their host genes, we analyzed the host genes of the 7 top upregulated circRNAs. Then, we performed a functional cluster analysis of these host genes and circRNAs to predict the circRNAs’ function. To investigate the underlying mechanisms of circRNAs in PTC, we constructed a ceRNA network map of circRNA-miRNA-mRNA interactions for the 14 circRNAs that were most significantly upregulated and identified some pivotal miRNAs in the network. From the 14 upregulated circRNAs, we selected seven circRNAs for verification via qRT-PCR, and the results were in accordance with the sequencing results, which confirmed the reliability of the high-throughput sequencing results. Furthermore, we identified several circRNAs that had not been reported in previous studies. Therefore, our study plays an indispensable role in comprehensively studying and describing the differentially expressed circRNA and host genes in PTC. More importantly, MET, the host gene of circMET, showed statistical significance in the analysis among the circRNAs that we verified to be significantly differentially expressed. Previous studies have reported the upregulation of MET expression and the activation of the MET/PI3K/Akt signaling pathway which ultimately promotes the development of pancreatic ductal cancer cells.[15] And circMET drives immunosuppression and anti-PD1 therapy resistance in hepatocellular carcinoma via the miR-30-5p/Snail/DPP4 axis.[28] These previous studies found that circMET may influence the occurrence and progression of PTC via a series of circRNA-miRNA-mRNA regulation patterns.

To determine the role of circMET in the development and progression of PTC, further molecular biology experiments are required. According to the underlying mechanisms in thyroid carcinogenesis, DNA replication, and the cell cycle play important roles in PTC. Therefore, we conducted in vitro experiments and found that downregulated circMET by siRNAs could inhibit the proliferation of TPC-1 cells and regulate the cell cycle.

There are some limitations to this study. First, we only sequenced four pairs of control and PTC tissues, a dataset that is at high risk of bias due to the small sample size. Second, most of the samples were obtained from female patients with thyroid cancer. Therefore, unequal distribution of gender may cause a bias. Third, we did not fully investigate the role of circRNAs and miRNAs in the pathological process of PTC. The detailed mechanism of the molecular-molecular interaction remains unclear and requires further exploration in the future. Given the clinical utility of circRNAs, more research is needed to assess the diagnostic value of circRNAs in serum or FNAB samples. The regulatory mechanism of circRNAs in PTC occurrence and development is complex and should be further studied, and specific circRNAs should be identified.


This study discovered that 14 circRNAs are significantly upregulated in PTC compared with adjacent normal thyroid tissues. Among them, circMET was found to be potentially involved in the pathogenesis and progression of PTC by affecting cell proliferation and cell cycle. CircRNA-miRNA-mRNA interactions were predicted, and potential regulatory pathways were discussed in this analysis. Further functional validation could improve our understanding of the molecular mechanisms underlying PTC pathogenesis and progression and provide new ideas for PTC prevention and control.



We thank LetPub for its linguistic assistance during the preparation of this manuscript.



This research was funded to Hong Lai by the Key Research and Development Plan of Shandong Province (No. 2019GSF108099), and Yujing Sun by Youth Foundation of Shandong Natural Science Foundation (No. ZR2021QC111).



The authors declare no conflicts of interest.



The following information was supplied relating to ethical approvals: The Ethics Committee of Qilu Hospital, Shandong University, China (approval number: KYLL-2018(KS)-226). A written informed consent was obtained from all the patients participating in this study.



LC, YS designed the research study. CC and PS performed the research. XH, HL and BC collected clinical patient information. JL(Jidong Liu), CW and JL(Jinbo Liu) oversaw the patient sample collection and design. PS performed the histological examination of the thyroid. LC conceived the study and edited the manuscript. YL, PL and KL analyzed the data. YL wrote the manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript.



The following information was supplied regarding data availability: The sequences are available at GEO: GSE197443.




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Supplementary Figures

Supplemental figure 1.
Supplemental figure 1. Thyroid immunohistochemical and fluorescent dye staining Images of representative H&E staining: (A) FNAB PTC sample, (B) PTC tissue sample and adjacent normal tissue sample, and (C) PTC fluorescent staining. Amplification factor: 40×, 200×.

Supplemental figure 2.
Supplemental figure 2. Construction of the circRNA–miRNA interaction network We constructed of the circRNA–miRNA interaction network and predicted the relationship of 500 top circRNAs (represented by blue) to miRNAs (represented by red).

Supplemental figure 3.
Supplemental figure 3. circRNA–mRNA co-expression network map analysis We do the circRNA–mRNA co-expression network map analysis and there were 500 top circRNAs (green dots) and target mRNAs (red dots).

Supplemental figure 4.
Supplemental figure 4. Survival analysis of the host genes of the main differential circRNAs expressed in PTC patients. (A-G) Overall survival analysis of patients with PTC by FNDC3B, SKIL, MCTP2, MET, PTK2, MYO9A and ARHGAP5 in The Cancer Genome Atlas (TCGA) cohort.

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The Producing Capabilities of Interferon‑g and Interleukin‑10 of Spleen Cells in Primary and Metastasized Oral Squamous Cell Carcinoma Cells-implanted Mice

Yasuka Azuma1,2, Masako Mizuno‑Kamiya3, Eiji Takayama1, Harumi Kawaki1, Toshihiro Inagaki4, Eiichi Chihara2, Yasunori Muramatsu5, Nobuo Kondoh1

“Eating” Cancer Cells by Blocking CD47 Signaling: Cancer Therapy by Targeting the Innate Immune Checkpoint

Yi‑Rong Xiang, Li Liu

Glycosylation is Involved in Malignant Properties of Cancer Cells

Kazunori Hamamura1, Koichi Furukawa2

Biomarkers in Molecular Epidemiology Study of Oral Squamous Cell Carcinoma in the Era of Precision Medicine

Qing‑Hao Zhu1*, Qing‑Chao Shang1*, Zhi‑Hao Hu1*, Yuan Liu2, Bo Li1, Bo Wang1, An‑Hui Wang1

I‑Kappa‑B Kinase‑epsilon Activates Nuclear Factor‑kappa B and STAT5B and Supports Glioblastoma Growth but Amlexanox Shows Little Therapeutic Potential in These Tumors

Nadège Dubois1, Sharon Berendsen2, Aurélie Henry1,2, Minh Nguyen1, Vincent Bours1,
Pierre Alain Robe1,2

Suppressive Effect of Mesenchymal Stromal Cells on Interferon‑g‑Producing Capability of Spleen Cells was Specifically Enhanced through Humoral Mediator(s) from Mouse Oral Squamous Cell Carcinoma Sq‑1979 Cells In Vitro

Toshihiro Inagaki1,2, Masako Mizuno‑Kamiya3, Eiji Takayama1, Harumi Kawaki1, Eiichi Chihara4, Yasunori Muramatsu5, Shinichiro Sumitomo5, Nobuo Kondoh1

An Interplay Between MicroRNA and SOX4 in the Regulation of Epithelial–Mesenchymal Transition and Cancer Progression

Anjali Geethadevi1, Ansul Sharma2, Manish Kumar Sharma3, Deepak Parashar1

MicroRNAs Differentially Expressed in Prostate Cancer of African‑American and European‑American Men

Ernest K. Amankwah

The Role of Reactive Oxygen Species in Screening Anticancer Agents

Xiaohui Xu1, Zilong Dang2, Taoli Sun3, Shengping Zhang1, Hongyan Zhang1

Panobinostat and Its Combination with 3‑Deazaneplanocin‑A Induce Apoptosis and Inhibit In vitro Tumorigenesis and Metastasis in GOS‑3 Glioblastoma Cell Lines

Javier de la Rosa*, Alejandro Urdiciain*, Juan Jesús Aznar‑Morales, Bárbara Meléndez1,
Juan A. Rey2, Miguel A. Idoate3, Javier S. Castresana

Cancer Stem‑Like Cells Have Cisplatin Resistance and miR‑93 Regulate p21 Expression in Breast Cancer

Akiko Sasaki1, Yuko Tsunoda2, Kanji Furuya3, Hideto Oyamada1, Mayumi Tsuji1, Yuko Udaka1, Masahiro Hosonuma1, Haruna Shirako1, Nana Ichimura1, Yuji Kiuchi1

The Contribution of Hexokinase 2 in Glioma

Hui Liu1, Hongwei Yang2, Xin Wang3, Yanyang Tu1

The Mechanism of BMI1 in Regulating Cancer Stemness Maintenance, Metastasis, Chemo‑ and Radiation Resistance

Xiaoshan Xu, Zhen Wang, Nan Liu, Pengxing Zhang, Hui Liu, Jing Qi, Yanyang Tu

A Multisource Adaptive Magnetic Resonance Image Fusion Technique for Versatile Contrast Magnetic Resonance Imaging

Lei Zhang1,2, Fang‑Fang Yin1,2,3, Brittany Moore1,2, Silu Han1,2, Jing Cai1,2,4

Senescence and Cancer

Sulin Zeng1,2, Wen H. Shen2, Li Liu1

The “Wild”‑type Gastrointestinal Stromal Tumors: Heterogeneity on Molecule Characteristics and Clinical Features

Yanhua Mou1, Quan Wang1, Bin Li1,2

Retreatment with Cabazitaxel in a Long‑Surviving Patient with Castration‑Resistant Prostate Cancer and Visceral Metastasis

Raquel Luque Caro, Carmen Sánchez Toro, Lucia Ochoa Vallejo

Therapy‑Induced Histopathological Changes in Breast Cancers: The Changing Role of Pathology in Breast Cancer Diagnosis and Treatment

Shazima Sheereen1, Flora D. Lobo1, Waseemoddin Patel2, Shamama Sheereen3,
Abhishek Singh Nayyar4, Mubeen Khan5

Glioma Research in the Era of Medical Big Data

Feiyifan Wang1, Christopher J. Pirozzi2, Xuejun Li1

Transarterial Embolization for Hepatocellular Adenomas: Case Report and Literature Review

Jian‑Hong Zhong1,2, Kang Chen1, Bhavesh K. Ahir3, Qi Huang4, Ye Wu4, Cheng‑Cheng Liao1, Rong‑Rong Jia1, Bang‑De Xiang1,2, Le‑Qun Li1,2

Nicotinamide Phosphoribosyltransferase: Biology, Role in Cancer, and Novel Drug Target

Antonio Lucena‑Cacace1,2,3, Amancio Carnero1,2

Enhanced Anticancer Effect by Combination of Proteoglucan and Vitamin K3 on Bladder Cancer Cells

Michael Zhang, Kelvin Zheng, Muhammad Choudhury, John Phillips, Sensuke Konno

Molecular Insights Turning Game for Management of Ependymoma: A Review of Literature

Ajay Sasidharan, Rahul Krishnatry

IDH Gene Mutation in Glioma

Leping Liu1, Xuejun Li1,2

Challenges and Advances in the Management of Pediatric Intracranial Germ Cell Tumors: A Case Report and Literature Review

Gerard Cathal Millen1, Karen A. Manias1,2, Andrew C. Peet1,2, Jenny K. Adamski1

Assessing the Feasibility of Using Deformable Registration for Onboard Multimodality‑Based Target Localization in Radiation Therapy

Ge Ren1,2,3, Yawei Zhang1,2, Lei Ren1,2

Research Advancement in the Tumor Biomarker of Hepatocellular Carcinoma

Qing Du1, Xiaoying Ji2, Guangjing Yin3, Dengxian Wei3, Pengcheng Lin1, Yongchang Lu1,
Yugui Li3, Qiaohong Yang4, Shizhu Liu5, Jinliang Ku5, Wenbin Guan6, Yuanzhi Lu7

Novel Insights into the Role of Bacterial Gut Microbiota in Hepatocellular Carcinoma

Lei Zhang1, Guoyu Qiu2, Xiaohui Xu2, Yufeng Zhou3, Ruiming Chang4

Central Odontogenic Fibroma with Unusual Presenting Symptoms

Aanchal Tandon, Bharadwaj Bordoloi, Safia Siddiqui, Rohit Jaiswal

The Prognostic Role of Lactate in Patients Who Achieved Return of Spontaneous Circulation after Cardiac Arrest: A Systematic Review and Meta‑analysis

Dongni Ren1, Xin Wang2, Yanyang Tu1,2

Inhibitory Effect of Hyaluronidase‑4 in a Rat Spinal Cord Hemisection Model

Xipeng Wang1,2, Mitsuteru Yokoyama2, Ping Liu3

Research and Development of Anticancer Agents under the Guidance of Biomarkers

Xiaohui Xu1, Guoyu Qiu1, Lupeng Ji2, Ruiping Ma3, Zilong Dang4, Ruling Jia1, Bo Zhao1

Idiopathic Hypereosinophilic Syndrome and Disseminated Intravascular Coagulation

Mansoor C. Abdulla

Phosphorylation of BRCA1‑Associated Protein 1 as an Important Mechanism in the Evasion of Tumorigenesis: A Perspective

Guru Prasad Sharma1, Anjali Geethadevi2, Jyotsna Mishra3, G. Anupa4, Kapilesh Jadhav5,
K. S. Vikramdeo6, Deepak Parashar2

Progress in Diagnosis and Treatment of Mixed Adenoneuroendocrine Carcinoma of Biliary‑Pancreatic System

Ge Zengzheng1, Huang-Sheng Ling2, Ming-Feng Li2, Xu Xiaoyan1, Yao Kai1, Xu Tongzhen3,
Ge Zengyu4, Li Zhou5

Surface-Enhanced Raman Spectroscopy to Study the Biological Activity of Anticancer Agent

Guoyu Qiu1, Xiaohui Xu1, Lupeng Ji2, Ruiping Ma3, Zilong Dang4, Huan Yang5

Alzheimer’s Disease Susceptibility Genes in Malignant Breast Tumors

Steven Lehrer1, Peter H. Rheinstein2

OSMCC: An Online Survival Analysis Tool for Merkel Cell Carcinoma

Umair Ali Khan Saddozai1, Qiang Wang1, Xiaoxiao Sun1, Yifang Dang1, JiaJia Lv1,2, Junfang Xin1, Wan Zhu3, Yongqiang Li1, Xinying Ji1, Xiangqian Guo1

Protective Activity of Selenium against 5‑Fluorouracil‑Induced Nephrotoxicity in Rats

Elias Adikwu, Nelson Clemente Ebinyo, Beauty Tokoni Amgbare

Advances on the Components of Fibrinolytic System in Malignant Tumors

Zengzheng Ge1, Xiaoyan Xu1, Zengyu Ge2, Shaopeng Zhou3, Xiulin Li1, Kai Yao1, Lan Deng4

A Patient with Persistent Foot Swelling after Ankle Sprain: B‑Cell Lymphoblastic Lymphoma Mimicking Soft‑tissue Sarcoma

Crystal R. Montgomery‑Goecker1, Andrew A. Martin2, Charles F. Timmons3, Dinesh Rakheja3, Veena Rajaram3, Hung S. Luu3

Coenzyme Q10 and Resveratrol Abrogate Paclitaxel‑Induced Hepatotoxicity in Rats

Elias Adikwu, Nelson Clemente Ebinyo, Loritta Wasini Harris

Progress in Clinical Follow‑up Study of Dendritic Cells Combined with Cytokine‑Induced Killer for Stomach Cancer

Ling Wang1,2, Run Wan1,2, Cong Chen1,2, Ruiliang Su1,2, Yumin Li1,2

Supraclavicular Lymphadenopathy as the Initial Manifestation in Carcinoma of Cervix

Priyanka Priyaarshini1, Tapan Kumar Sahoo2

ABO Typing Error Resolution and Transfusion Support in a Case of an Acute Leukemia Patient Showing Loss of Antigen Expression

Debasish Mishra1, Gopal Krushna Ray1, Smita Mahapatra2, Pankaj Parida2

Protein Disulfide Isomerase A3: A Potential Regulatory Factor of Colon Epithelial Cells

Yang Li1, Zhenfan Huang2, Haiping Jiang3

Clinicopathological Association of p16 and its Impact on Outcome of Chemoradiation in Head‑and‑Neck Squamous Cell Cancer Patients in North‑East India

Srigopal Mohanty1, Yumkhaibam Sobita Devi2, Nithin Raj Daniel3, Dulasi Raman Ponna4,
Ph. Madhubala Devi5, Laishram Jaichand Singh2

Potential Inhibitor for 2019‑Novel Coronaviruses in Drug Development

Xiaohui Xu1, Zilong Dang2, Lei Zhang3, Lingxue Zhuang4, Wutang Jing5, Lupeng Ji6, Guoyu Qiu1

Best‑Match Blood Transfusion in Pediatric Patients with Mixed Autoantibodies

Debasish Mishra1, Dibyajyoti Sahoo1, Smita Mahapatra2, Ashutosh Panigrahi3

Characteristics and Outcome of Patients with Pheochromocytoma

Nadeema Rafiq1, Tauseef Nabi2, Sajad Ahmad Dar3, Shahnawaz Rasool4

Comparison of Histopathological Grading and Staging of Breast Cancer with p53‑Positive and Transforming Growth Factor‑Beta Receptor 2‑Negative Immunohistochemical Marker Expression Cases

Palash Kumar Mandal1, Anindya Adhikari2, Subir Biswas3, Amita Giri4, Arnab Gupta5,
Arindam Bhattacharya6

Chemical Compositions and Antiproliferative Effect of Essential Oil of Asafoetida on MCF7 Human Breast Cancer Cell Line and Female Wistar Rats

Seyyed Majid Bagheri1,2, Davood Javidmehr3, Mohammad Ghaffari1, Ehsan Ghoderti‑Shatori4

Cyclooxygenase‑2 Contributes to Mutant Epidermal Growth Factor Receptor Lung Tumorigenesis by Promoting an Immunosuppressive Environment

Mun Kyoung Kim1, Aidin Iravani2, Matthew K. Topham2,3

Cuproptosis-related Genes in Glioblastoma as Potential Therapeutic Targets

Zhiyu Xia1,2, Haotian Tian1, Lei Shu1,2, Guozhang Tang3, Zhenyu Han4, Yangchun Hu1*, Xingliang Dai1*

Cancer Diagnosis and Treatments by Porous Inorganic Nanocarriers

Jianfeng Xu1,2, Hanwen Zhang1,2, Xiaohui Song1,2, Yangong Zheng3, Qingning Li1,2,4*

Delayed (20 Years) post-surgical Esophageal Metastasis of Breast Cancer - A Case Report

Bowen Hu1#, Lingyu Du2#, Hongya Xie1, Jun Ma1, Yong Yang1*, Jie Tan2*

Subtyping of Undifferentiated Pleomorphic Sarcoma and Its Clinical Meaning

Umair Ali Khan Saddozai, Zhendong Lu, Fengling Wang, Muhammad Usman Akbar, Saadullah Khattak, Muhammad Badar, Nazeer Hussain Khan, Longxiang Xie, Yongqiang Li, Xinying Ji, Xiangqian Guo

Construction of Glioma Prognosis Model and Exploration of Related Regulatory Mechanism of Model Gene

Suxia Hu, Abdusemer Reyimu, Wubi Zhou, Xiang Wang, Ying Zheng, Xia Chen, Weiqiang Li, Jingjing Dai

ESRP2 as a Non-independent Potential Biomarker-Current Progress in Tumors

Yuting Chen, Yuzhen Rao, Zhiyu Zeng, Jiajie Luo, Chengkuan Zhao, Shuyao Zhang

Resection of Bladder Tumors at the Ureteral Orifice Using a Hook Plasma Electrode: A Case Report

Jun Li, Ziyong Wang, Qilin Wang

Structural Characterization and Bioactivity for Lycium Barbarum Polysaccharides

Jinghua Qi1,2,  Hangping Chen3,Huaqing Lin2,4,Hongyuan Chen1,2,5* and Wen Rui2,3,5,6*

The Role of IL-22 in the Prevention of Inflammatory Bowel Disease and Liver Injury

Xingli Qi1,2, Huaqing Lin2,3, Wen Rui2,3,4,5 and Hongyuan Chen1,2,3

RBM15 and YTHDF3 as Positive Prognostic Predictors in ESCC: A Bioinformatic Analysis Based on The Cancer Genome Atlas (TCGA)

Yulou Luo1, Lan Chen2, Ximing Qu3, Na Yi3, Jihua Ran4, Yan Chen3,5*

Mining and Analysis of Adverse Drug Reaction Signals Induced by Anaplastic Lymphoma Kinase-Tyrosine Kinase Inhibitors Based on the FAERS Database

Xiumin Zhang1,2#, Xinyue Lin1,3#, Siman Su1,3#, Wei He3, Yuying Huang4, Chengkuan Zhao3, Xiaoshan Chen3, Jialin Zhong3, Chong Liu3, Wang Chen3, Chengcheng Xu3, Ping Yang5, Man Zhang5, Yanli Lei5*, Shuyao Zhang1,3*

Advancements in Immunotherapy for Advanced Gastric Cancer

Min Jiang1#, Rui Zheng1#, Ling Shao1, Ning Yao2, Zhengmao Lu1*

Tumor Regression after COVID-19 Infection in Metastatic Adrenocortical Carcinoma Treated with Immune Checkpoint Blockade: A Case Report

Qiaoxin Lin1, Bin Liang1, Yangyang Li2, Ling Tian3*, Dianna Gu1*

Mining and Analysis of Adverse Events of BRAF Inhibitors Based on FDA Reporting System

Silan Peng1,2#, Danling Zheng1,3#, Yanli Lei4#, Wang Chen3, Chengkuan Zhao3, Xinyue Lin1, Xiaoshan Chen3, Wei He3, Li Li3, Qiuzhen Zhang5*, Shuyao Zhang1,3*

Malignant Phyllodes Tumor with Fever, Anemia, Hypoproteinemia: A Rare and Strange Case Report and Literature Review

Zhenghang Li1, Yuxian Wei1*

Construction of Cuproptosis-Related LncRNA Signature as a Prognostic Model Associated with Immune Microenvironment for Clear-Cell Renal Cell Carcinoma

Jiyao Yu1#, Shukai Zhang2#, Qingwen Ran3, Xuemei Li4,5,6*

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