Open Access

Profiling and bioinformatics analyses reveal differential circular RNA expression in radioresistant esophageal cancer cells

  • Huafang Su1,
  • Fuqiang Lin2,
  • Xia Deng1,
  • Lanxiao Shen1,
  • Ya Fang1,
  • Zhenghua Fei1,
  • Lihao Zhao1,
  • Xuebang Zhang1,
  • Huanle Pan1,
  • Deyao Xie2,
  • Xiance Jin1Email author and
  • Congying Xie1Email author
Contributed equally
Journal of Translational Medicine201614:225

DOI: 10.1186/s12967-016-0977-7

Received: 31 March 2016

Accepted: 13 July 2016

Published: 28 July 2016

Abstract

Background

Acquired radioresistance during radiotherapy is considered as the most important reason for local tumor recurrence or treatment failure. Circular RNAs (circRNAs) have recently been identified as microRNA sponges and involve in various biological processes. The purpose of this study is to investigate the role of circRNAs in the radioresistance of esophageal cancer.

Methods

Total RNA was isolated from human parental cell line KYSE-150 and self-established radioresistant esophageal cancer cell line KYSE-150R, and hybridized to Arraystar Human circRNA Array. Quantitative real-time PCR was used to confirm the circRNA expression profiles obtained from the microarray data. Bioinformatic tools including gene ontology (GO) analysis, KEGG pathway analysis and network analysis were done for further assessment.

Results

Among the detected candidate 3752 circRNA genes, significant upregulation of 57 circRNAs and downregulation of 17 circRNAs in human radioresistant esophageal cancer cell line KYSE-150R were observed compared with the parental cell line KYSE-150 (fold change ≥2.0 and P < 0.05). There were 9 out of these candidate circRNAs were validated by real-time PCR. GO analysis revealed that numerous target genes, including most microRNAs were involved in the biological processes. There were more than 400 target genes enrichment on Wnt signaling pathway. CircRNA_001059 and circRNA_000167 were the two largest nodes in circRNA/microRNA co-expression network.

Conclusions

Our study revealed a comprehensive expression and functional profile of differentially expressed circRNAs in radioresistant esophageal cancer cells, indicating possible involvement of these dysregulated circRNAs in the development of radiation resistance.

Keywords

Radioresistance Esophageal cancer Circular RNA Microarray

Background

Esophageal cancer is the eighth most frequently diagnosed and the sixth highest mortality rate cancer in the world [1]. The 5-year survival rate of esophageal cancer patients with localized disease is less than 20 % [2]. Radiation therapy (RT) plays a crucial role in the treatment of esophageal cancer [3]. Although complex multidisciplinary methods incorporating surgery, chemotherapy, and radiotherapy had been applied in the treatment of esophageal cancer, the rate of local recurrence and distant metastasis remains high [4, 5]. Radiotherapy resistance has been considered as one of the most important reasons for local tumor recurrence or treatment failure [6].

Circular RNAs (circRNAs), unlike the well known linear RNA, forms a covalently closed continuous loop [7, 8]. CircRNAs are integral, conserved, and demonstrated to be resistant to RNase R treatment [911]. With the advent of novel biochemical and computational approaches, circRNAs have been represented as a research hotspot in the RNA field [12]. Accumulating evidence demonstrated that circRNAs involve in the development of several types of diseases, such as Alzheimer’s disease [13], atherosclerotic vascular disease [14] and cancer [15, 16]. Recent studies found that circRNAs can function as microRNAs (miRNAs) sponges [17, 18], RNA-binding protein sequestering agents, and nuclear transcriptional regulators. The recently identified circRNA, ciRS-7, which acts as a designated miR-7 sponge, involves in competing in endogenous RNA networks [19].

We previously developed a radioresistant esophageal squamous cancer cell line (KYSE-150R) by irradiating esophageal cancer cells KYSE-150 with gradient dose [2022]. To explore the underlying molecular regulation mechansim of circRNAs in the radioresistance, circular RNA microarray was used to detect the differential expressed circRNAs between radioresistant esophageal cell line KYSE-150R and the parental cell line KYSE-150. Our results suggested that the aberrant expression of circRNAs may play a role in transformation of radiation resistance of esophageal cancer cells.

Methods

Cell culture and reagents

Human esophageal squamous cancer cell lines KYSE-150 were purchased from the American Type Culture Collection (Manassas, VA, USA). Radioresistant cell line KYSE-150R has been previously established in our department by gradient dose irradiation treatment. Both KYSE-150 and KYSE-150R were cultured in RPMI-1640 (Gibco, Life Technologies Inc., Grand Island, NY, USA) with 100 unit/ml of penicillin, 100 mg/ml of streptomycin, and 10 % fetal bovine serum at 37 °C in a humidified incubator containing 5 % CO2. The cell lines were sub-cultured every 2–3 days following digestion at room temperature with 0.5 ml trypsin/EDTA per well (Sigma-Aldrich Ltd, UK).The viability was reported as the percentage of the viable cells number to the total cells number. There was an average viability over 95 %, determined by Trypan Blue staining.

RNA isolation, purification and hybridization

Three samples were collected from each of the two cell cultural groups and used for the following RNA extraction. Total RNA from each sample was treated with Rnase R (Epicentre, Inc.) to remove linear RNAs and to enrich circRNAs. Then, the enriched circRNAs were amplified and transcribed into fluorescent cRNA utilizing a random priming method (Arraystar Super RNA Labeling Kit; Arraystar). The labeled cRNAs were purified by RNeasy Mini Kit (Qiagen). The concentration and specific activity of the labeled cRNAs (pmol Cy3/μg cRNA) were measured by NanoDrop ND-1000. One μL of each labeled cRNA was fragmented by adding 5μL 10× Blocking Agent and 1 μL of 25× Fragmentation Buffer, and then the mixture was heated at 60 °C for 30 min. Finally 25 μL 2× Hybridization buffer was added to dilute the labeled cRNA. Hybridization solution of 50 μL was dispensed into the gasket slide and assembled to the circRNA expression microarray slide. The slides were incubated for 17 h at 65 °C in an Agilent Hybridization Oven. The hybridized arrays were washed, fixed and scanned using the Agilent Scanner G2505C.

Microarray data analysis

Scanned images were imported into Agilent Feature Extraction software (version 11.0.1.1) for raw data extraction. Quantile normalization of raw data and subsequent data processing were performed using the R software package. After quantile normalization of the raw data, low intensity filtering was performed. The circRNAs with at least 3 out of 6 samples flagged in “P” or “M” (“all targets value”) were retained for further analysis. When comparing the profile differences between two groups (such as disease versus control), the “fold change” (i.e. the ratio of the group averages) between the groups for each circRNA was computed. The statistical significance of the difference may be conveniently estimated by t test. CircRNAs having fold changes ≥2 and P values <0.05 were selected as of significantly differential expression.

Real-time PCR validation

Quantitative Real-time PCR was used to confirm the circRNA expression profiles obtained from the microarray data. Total RNA was extracted from cells using Trizol Reagent (Invitrogen) and reversely transcribed into cDNA using Super Script TM III Reverse Transcriptase (Invitrogen) according to a standard protocol. The relative gene expression was determined using ViiA 7 Real-time PCR System (Applied Biosystems). All samples were normalized to the signal generated from GAPDH (Sangon Biotech, Shanghai, China). Data was shown as fold change (2−ΔΔCt) and analyzed initially using Opticon Monitor Analysis Software V2.02 (MJ Research, Waltham, MA, USA). Triplicates were performed for each sample in three independent experiments. Primer sequences were presented in Additional file 1: Table S1.

MicroRNA prediction

The circRNA/microRNA interaction was predicted with Arraystar’s home-made miRNA target prediction software (Rockville, USA), whose principles are based on the TargetScan and miRanda prediction algorithm. The differentially expressed circRNAs within all the comparisons were annotated in detail with the circRNA/miRNA interaction information.

MicroRNA target genes prediction and GO analysis

To further investigate the functional roles of microRNA, putative targets of miRNAs were predicted by TargetScan software. GO analysis was performed to explore the functional roles of target genes in terms of biological processes, cellular components and molecular functions. Biological pathways defined by Kyoto Encyclopedia of Genes and Genomes (KEGG), Biocarta and Reactome (http://www.genome.jp/kegg/) were identified by Database for Annotation, Visualization and Integrated Discovery (DAVID; http://www.david.abcc.ncifcrf.gov/).

CircRNA-microRNA co-expression network

To further elucidate correlations between circRNAs and microRNA, potential microRNA-circRNA-mRNA interaction analysis was conducted by Cytoscape. The size of each node represents the number of putative microRNA functionally connected to each circRNA.

Statistical analysis

The results were reported as mean ± SD for triplicate measurements. Statistically significant differences between groups were estimated by the Student’s t test using SPSS (13.0). P < 0.05 was considered as being statistically significant.

Result

Overview of circRNAs profiles

The expression of 3752 human circRNAs was quantitated for esophageal cancer KYSE-150R and KYSE-150 cell samples using microarray platform (Additional file 2: Table S2). Hierarchical clustering and scatter plot visualization showed that the circRNAs expression levels were distinguishable and variations (Fig. 1). The expression profiles of 74 circRNAs were differentially expressed (fold change ≥2.0 and P < 0.05) between the KYSE-150R and KYSE-150 cells, in which 57 circRNAs were found to be upregulated and 17 circRNAs were downregulated more than two-fold in KYSE-150R cells (Tables 1, 2). Among these, The expression levels of circRNA_100385, circRNA_104983 and circRNA_001059 were upregulated in KYSE-150R by 41.06, 11.68 and 6.16-folds, respectively. Meanwhile, circRNA_101877, circRNA_102913, and circRNA_000695 were downregulated by 3.53, 2.69 and 2.51-folds, respectively.
Fig. 1

CircRNA microarray expression data between the KYSE-150 and KYSE-150R cells. a Hierarchical clustering shows a distinguishable circRNA expression profiling among KYSE-150 and KYSE-150R cell. b Box plots show the distribution of circRNAs for the two compared samples. The distributions were nearly the same after normalization. c Scatter plots assess the circRNA expression variation between the two compared groups. The circRNAs above the top green line and below the bottom green line indicated more than twofold changes of circRNAs between the two compared samples

Table 1

Upregulated circRNAs in the KYSE-150R compared with KYSE-150

ProbeID

CircRNA

Genesymbol

Chrom

FC (abs)

ASCRP000770

hsa_circRNA_100385

PRRX1

chr1(+):170688866170695542

41.06

ASCRP005230

hsa_circRNA_104983

NHS

chrX(+):1770586117710588

11.69

ASCRP000189

hsa_circRNA_001059

LIN52

chr14(+):7455167774551959

6.16

ASCRP001355

hsa_circRNA_100984

FOXRED1

chr11(+):126142863126143349

5.96

ASCRP000752

hsa_circRNA_100367

DCAF8

chr1(−):160206924160231148

5.62

ASCRP004073

hsa_circRNA_103783

NSUN2

chr5(−):66233266625782

5.19

ASCRP000699

hsa_circRNA_100312

MAN1A2

chr1(+):117944807117984947

5.04

ASCRP005194

hsa_circRNA_104947

PRRC2B

chr9(+):134330462134334743

3.67

ASCRP000700

hsa_circRNA_100313

MAN1A2

chr1(+):117957334117963271

3.46

ASCRP003691

hsa_circRNA_103393

CCDC66

chr3(+):5662699756628056

3.40

ASCRP002905

hsa_circRNA_102592

MYH14

chr19(+):5072087150721028

3.34

ASCRP004116

hsa_circRNA_103826

ZNF131

chr5(+):4316135043162033

3.18

ASCRP004597

hsa_circRNA_104334

MPP6

chr7(+):2466328424708279

3.10

ASCRP004358

hsa_circRNA_104084

LINC00340

chr6(+):2202056722056919

2.96

ASCRP003426

hsa_circRNA_103122

DONSON

chr21(−):3495360734958487

2.91

ASCRP000085

hsa_circRNA_000629

KIF18B

chr17(−):4301230343012398

2.88

ASCRP000698

hsa_circRNA_100311

MAN1A2

chr1(+):117944807117948267

2.88

ASCRP003235

hsa_circRNA_102928

RHBDD1

chr2(+):227729319227779067

2.86

ASCRP001219

hsa_circRNA_100845

MARK2

chr11(+):6366263063663105

2.83

ASCRP002541

hsa_circRNA_102213

USP36

chr17(−):7682332676825089

2.69

ASCRP002519

hsa_circRNA_102191

HN1

chr17(−):7314276073144766

2.67

ASCRP000062

hsa_circRNA_000543

MALAT1

chr11(+):6527249065272586

2.59

ASCRP000292

hsa_circRNA_001543

RSBN1L

chr7(+):7737874077387395

2.58

ASCRP004442

hsa_circRNA_104169

SOBP

chr6(+):107824860107827631

2.56

ASCRP003692

hsa_circRNA_103394

APPL1

chr3(+):5727688357301820

2.54

ASCRP004877

hsa_circRNA_104624

PCMTD1

chr8(−):5277340452773806

2.51

ASCRP000286

hsa_circRNA_001506

LAPTM4A

chr2(−):2024080920240905

2.49

ASCRP001133

hsa_circRNA_100759

DENND5A

chr11(−):92252069229179

2.38

ASCRP001576

hsa_circRNA_101213

RAN

chr12(+):131357380131357465

2.35

ASCRP001668

hsa_circRNA_101309

TMCO3

chr13(+):114188359114193822

2.35

ASCRP003134

hsa_circRNA_102825

RAB3GAP1

chr2(+):135883750135884226

2.33

ASCRP002340

hsa_circRNA_102003

USP22

chr17(−):2091020820914622

2.30

ASCRP004596

hsa_circRNA_104333

MPP6

chr7(+):2466328424690331

2.30

ASCRP000391

hsa_circRNA_100001

SAMD11

chr1(+):871151874792

2.28

ASCRP002927

hsa_circRNA_102614

ASAP2

chr2(+):94909369499018

2.26

ASCRP001679

hsa_circRNA_101320

PRMT5

chr14(−):2339534123396023

2.26

ASCRP002384

hsa_circRNA_102049

TADA2A

chr17(+):3579783835800763

2.25

ASCRP005237

hsa_circRNA_104990

POLA1

chrX(+):2482801424844718

2.25

ASCRP005013

hsa_circRNA_104763

UBAP2

chr9(−):3395328233996331

2.23

ASCRP002333

hsa_circRNA_101996

SPECC1

chr17(+):2010764520109225

2.23

ASCRP003820

hsa_circRNA_103522

FXR1

chr3(+):180685838180688146

2.21

ASCRP001777

hsa_circRNA_101418

CEP128

chr14(−):8129748681307112

2.18

ASCRP002683

hsa_circRNA_102364

DYM

chr18(−):4678337946808545

2.18

ASCRP003659

hsa_circRNA_103361

SMARCC1

chr3(−):4771968747727660

2.18

ASCRP000064

hsa_circRNA_000554

PRB4

chr12(−):1119961811248400

2.17

ASCRP001819

hsa_circRNA_101468

TJP1

chr15(−):3005334130092905

2.16

ASCRP005238

hsa_circRNA_104991

POLA1

chrX(+):2482801424861794

2.15

ASCRP001818

hsa_circRNA_101467

TJP1

chr15(−):3005334130065560

2.13

ASCRP003032

hsa_circRNA_102720

RTN4

chr2(−):5521462655214834

2.12

ASCRP000314

hsa_circRNA_001676

PPP1R12A

chr12(−):8018015380183460

2.11

ASCRP003844

hsa_circRNA_103546

LPP

chr3(+):188202379188242575

2.09

ASCRP001842

hsa_circRNA_101491

MAPKBP1

chr15(+):4210308042105299

2.09

ASCRP002653

hsa_circRNA_102334

SS18

chr18(−):2361236223637706

2.07

ASCRP003119

hsa_circRNA_102810

RALB

chr2(+):121036193121047333

2.06

ASCRP002883

hsa_circRNA_102570

ERCC2

chr19(−):4586052745860957

2.05

ASCRP004893

hsa_circRNA_104640

CSPP1

chr8(+):6801813968028357

2.04

ASCRP003216

hsa_circRNA_102908

BARD1

chr2(−):215632205215646233

2.01

Table 2

Downregulated circRNAs in the KYSE-150R compared with KYSE-150

probeID

circRNA

Genesymbol

Chrom

FC (abs)

ASCRP002219

hsa_circRNA_101877

RFWD3

chr16(−):74670243 74671868

3.53

ASCRP003221

hsa_circRNA_102913

ATIC

chr2(+):216177220 216190861

2.69

ASCRP000100

hsa_circRNA_000695

EEFSEC

chr3(+):128102470 128102926

2.51

ASCRP000018

hsa_circRNA_000167

RPPH1

chr14(−):20811404 20811554

2.47

ASCRP001778

hsa_circRNA_101419

STON2

chr14(−):81837331 81837529

2.42

ASCRP001605

hsa_circRNA_101242

PAN3

chr13(+):28830428 28855516

2.39

ASCRP001111

hsa_circRNA_100737

TOLLIP

chr11(−):1307231 1317024

2.3

ASCRP003406

hsa_circRNA_103102

DIDO1

chr20(−):61537238 61545758

2.3

ASCRP001876

hsa_circRNA_101525

PIGB

chr15(+):55621921 55634000

2.25

ASCRP004888

hsa_circRNA_104635

MTFR1

chr8(+):66582107 66582253

2.21

ASCRP003466

hsa_circRNA_103165

PI4KA

chr22(−):21161649 21167794

2.2

ASCRP000390

hsa_circRNA_002178

RPPH1

chr14(−):20811436 20811534

2.16

ASCRP000535

hsa_circRNA_100146

EIF3I

chr1(+):32691771 32692131

2.11

ASCRP000950

hsa_circRNA_100571

PDSS1

chr10(+):27024168 27024508

2.09

ASCRP003989

hsa_circRNA_103695

KLHL8

chr4(−):88116475 88116842

2.07

ASCRP004979

hsa_circRNA_104729

GLIS3

chr9(−):4117767 4118881

2.07

ASCRP001678

hsa_circRNA_101319

RBM23

chr14(−):23378691 23380612

2.01

Real-time quantitative PCR validation

To validate the microarray profiling expression data, real-time quantitative RT-PCR revealed 4 upregulated and 5 downregulated expressed circRNAs as shown in Fig. 2. Expression levels detected by the two methods were consistent with each other, demonstrating the high reliability of the microarray expression results.
Fig. 2

CircRNA expression changes validated by qRT-PCR. Validation of microarray analysis data by qRT-PCR. The expression levels of 9 circRNAs were determined by qRT-PCR. Each qRT-PCR assay was performed at least three times. *P < 0.05

MicroRNA prediction and bioinformatics analyses

MicroRNA prediction was done with Arraystar’s home-made miRNA target prediction software based on miRanda (Table 3; Additional file 3: Table S3, Additional file 4: Table S4). There were 12 out of 17 downregulated circRNAs had target microRNAs. The results of Gene Ontology enrichment analysis on the up and down regulated circRNAs with identified target genes were shown in Fig. 3 and Additional file 5: Table S5. Gene Ontology analysis revealed that numerous target genes were involved in the biological processes, such as cellular process, regulation of biological process, metabolic process, etc. These processes were associated with human tumorigenesis and metastasis. KEGG analysis showed that there were 10 pathways related to upregulated circRNAs, including Olfactory transduction, Spliceosome, Glutamatergic synapse, and Phosphatidylinositol signaling system, and 17 pathways related to downregulated circRNAs, including Neurotrophin signaling pathway, Wnt signaling pathway, Microbial metabolism in diverse environments and Insulin signaling pathway (Fig. 4; Additional file 6: Table S6). Especially, there were more than 400 target genes enrichment in Wnt signaling pathway.
Table 3

Target gene numbers of circRNA-miRNA

 

CircRNA

MiRNA

MiRNA::circRNA

MiRNA::circRNA target_location

MiRNA::target

Up

57

120

120::53

120::149

120::40335

Down

12

36

36::12

36::12

36::38979

Fig. 3

Gene ontology enrichment analysis. a Gene ontology enrichment corresponds to the upregulated circRNAs. b Gene ontology enrichment corresponds to the downregulated circRNAs

Fig. 4

KEGG pathway analysis. a Pathways correspond to the upregulated circRNAs. b Pathways correspond to the downregulated circRNAs. c Wnt signaling pathway. Red marked nodes are associated with target genes enrichment on Wnt signaling pathway

CircRNA-microRNA co-expression network

Potential connections between circRNA and microRNA were explored by using Cytoscape. As shown in Fig. 5, CircRNA_001059 and circRNA_000167 were the two largest nodes in the network.
Fig. 5

CircRNA-microRNA co-expression network. The circRNA-microRNA co-expression network as drawn with the cytoscape software. The size of each node represent functional connectivity of each circRNA. CircRNA_001059 and circRNA_000167 were the two largest nodes in the network

Discussion

Acquired radioresistance has been considered as one of the most important reasons causing treatment failure for esophageal cancer patients. In this study, we explored the expression patterns of circRNAs between radioresistant esophageal cancer cell line KYSE-150R and its parental cell line KYSE-150 with Arraystar Circular RNA Microarray to investigate the mechanisms of acquired radioresistance of esophageal cancer. Differentially expressed profiles of circRNAs in radioresistant esophageal cancer cells were observed and validated compared with the parental esophageal cancer cells, indicating possible involvement of these dysregulated circRNAs in the development of radiation resistance of esophageal cancer cells.

CircRNAs are discovered as new special kind of ubiquitous endogenous noncoding RNAs [23]. Recent evidences revealed that circRNAs can function as miRNA sponges and regulate parent gene expression to affect disease. Despite the potential importance of circRNAs reported in several types of cancer [24, 25], there is no reported studies on the functional roles of circRNAs in the radiation resistance of cancer. In this study, there were 57 circRNAs significantly upregulated and 17 circRNAs significantly downregulated in the KYSE-150R cell lines compared with KYSE-150, respectively. In which, circRNA_100385, circRNA_104983 and circRNA_001059 were upregulated with top magnitudes. CircRNA_101877, circRNA_102913, and circRNA_000695 were downregulated with top magnitudes. The expression pattern of these circRNAs were validated by qRT-PCR, and consistent results were observed. Our results indicated that the altered expression levels of circRNAs may be related to their involvement in the transcription level regulation on the radiation resistance of esophageal cancer cells. Aberrant expression of circRNAs has been linked to carcinogenesis and the malignant behavior of many different cancer. The abundance of a circRNA called hsa_circ_002059 has been reported to be significantly downregulated and suggested as a potential diagnostic marker for gastric cancer [26]. Qin M et al. [27] found that hsa_circ_0001649 may play a role in tumorigenesis and metastasis of hepatocellular carcinoma. Li et al. [28] reported that cir-ITCH might influence the expression level of ITCH and may be involved in the development of esophageal squamous cell carcinoma. However, no obvious changes of cir-ITCH was observed in this study, suggesting it may have no contribution to the radioresistance of esophageal cancer cells.

Despite the lack of knowledge of the exact functions of most circRNAs, we investigated the potential targets of these altered miRNAs. In a total, 57 upregulated circRNAs were identified to regulate the expression level of 120 microRNAs using the miRanda software, and 12 downregulated circRNAs were identified to regulate 36 microRNAs. According to the findings of Denzler et al. [29] low levels of circRNAs may not be sufficient to affect the target miRNAs. The circRNA-microRNA co-expression network analysis were conducted for our Top-5 circRNAs in this study. Two potential crucial circRNAs, circRNA_001059 and circRNA_000167 were identified to be influential on target miRNAs. CircRNAs were believed to negatively regulate miRNAs, and contribute substantially to the competing endogenous RNA (ceRNA) network. It has been reported that ciRS-7, as a circular miR-7 inhibitor, harbors more than 60 conventional miR-7 binding sites, which is far more than any known linear sponges [30]. Sex-determining region Y (SRY) was another identified miRNA sponge and functioned as a miR-138 sponge [31]. According to our results, we hypothesized that circRNA_001059 may act as an inhibitor of miRNA by binding several specific miRNAs, including miR-30c-1*, miR-30c-2*, miR-122*, miR-139-3p, miR-339-5p and miR-1912. Our results implied that it is worthwhile to further investigate these novel dysregulated circRNAs as microRNA sponges and their potential biological functions in the development of radiation resistance.

In this study, GO analysis and KEGG pathway annotation were conducted to investigate the functions of related microRNAs [32]. GO enrichment analysis revealed that target genes were involved in the regulation of crucial biological processes, indicating that regulating these genes in the cellular response is of great importance during the development of radioresistance. Among the upregulated pathways found in this study, phosphatidylinositol signaling pathway had been reported to be a key mediator of tumor cell responsiveness to radiation [33]. Phosphatidylinositol 3-kinase (PI3K)/Akt pathway accelerates the repair of DNA-DSB (DNA double-strand breaks), and consequently, its activation leads to therapy resistance [33]. Wnt signaling pathway which corresponds to downregulated circRNAs had also been reported to play predominant roles in radioresistance in Glioblastoma [34] and prostate cancer [35]. These results were also in line with the observations reported in our previous study [21].

Conclusions

In a conclusion, a unique set of circRNAs and their expression profiles were found in radioresistant esophageal cancer cells. Furthermore, their potential roles were investigated by bioinformatics analysis. Pathway analysis suggested that Wnt signaling pathway may be involved in the radioresistance. Network analysis uncovered two potential key circRNAs, i.e. circRNA_001059 and circRNA_000167. Our results would be helpful for future studies on investigating the molecular functions of circRNAs in radiotherapy resistance.

Notes

Declarations

Authors’ contributions

CX, XJ contributed experimental design; HS, FL,LS, XZ and YF performed the biological experiments; CX and XJ provided administrative support and funded experiments; HS, ZF, XZ,XD,DX and HP analysis of data; HS, XJ and CX drafted the manuscript. All the authors read and approved the final manuscript.

Acknowledgements

The study was supported by the Natural Science Foundation of Zhejiang Province (Nos. LY13H160036; LQ15H160013) and Wenzhou Municipal Science and Technology Bureau (No. Y20130180).

Competing interests

The authors declare that they have no competing of interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Radiotherapy and Chemotherapy, The First Affiliated Hospital of Wenzhou Medical University
(2)
Department of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University

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