Open Access

CircRNA-associated ceRNA network reveals ErbB and Hippo signaling pathways in hypopharyngeal cancer

  • Authors:
    • Chun Feng
    • Yuxiao Li
    • Yan Lin
    • Xianbao Cao
    • Dongdong Li
    • Honglei Zhang
    • Xiaoguang He
  • View Affiliations

  • Published online on: October 19, 2018
  • Pages: 127-142
  • Copyright: © Feng et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Accumulating evidence has suggested that circular RNAs (circRNAs), a novel class of non‑coding RNAs, have crucial roles in tumor progression. However, the significance of circRNAs in hypopharyngeal cancer (HCa) remains to be investigated. The present study has identified aberrantly expressed circRNAs by performing circRNA sequencing analyses of three pairs of tumor and adjacent normal samples from patients with HCa. The results demonstrated that 173 circRNAs were differentially expressed (DE), including 71 upregulated and 102 downregulated circRNAs (FDR<0.05 and fold changes of ≥2 or ≤0.5 by Mann‑Whitney U test followed by Benjamini‑Hochberg correction for multiple testing). Pathway analyses of the genes producing DE circRNAs revealed that many of them were involved in cancer‑related pathways. To further illustrate the roles of circRNAs in HCa progression, a competing endogenous RNA (ceRNAs) network was constructed, consisting of circRNAs, miRNA, and miRNA targeted genes. The results demonstrated that multiple cancer‑related pathways were affected by performing enrichment analyses of the targeted genes. Of note, a ceRNA subnetwork was isolated, consisting of two circRNAs (hsa_circ_0008287 and hsa_circ_0005027) and one miRNA (hsa‑miR‑548c‑3p), which significantly affect both ErbB and Hippo signaling pathways. In conclusion, the present study identified a set of circRNAs that are potentially implicated in the tumorigenesis of HCa and may serve as potential biomarkers for the diagnosis of HCa.


Hypopharyngeal carcinoma is a primary malignant tumor of the hypopharynx, accounting for 3-5% of the malignancies in the upper aerodigestive tract. Early diagnosis of hypopharyngeal cancer is hard because the early stages of hypopharyngeal carcinoma have no specific symptoms. Studies have reported that 60-80% of these patients had ipsilateral lymph node metastases and ≤40% of these patients have contralateral occult lymph node tumor deposits (1-3). Thus, the majority of patients with hypopharyngeal cancer have a poor prognosis and low survival rate (4). Therefore, identifying early stage indicators or biomarkers to improve patient survival is urgent.

Unlike normal linear RNA, the 3′ and 5′ ends of circular RNAs (circRNAs) are linked by covalent bonds and lack polarities or polyadenylated tails, thereby rendering them stable in tissues, serum and urine (5). Owing to this characteristic, the potential of circRNAs as biomarkers for human cancer has attracted significant focus. In addition, circRNAs are widely involved in cancer; ciRS-7 in HeLa cells (6), Hsa_ circ_001569 in colorectal cancer (7), circHIPK3 in several types of cancer (8), f-circM9, f-circPR in hematological malignancy (9), and circTCF25 in urinary bladder carcinoma (10). Previous studies have demonstrated that the main function of circRNAs is that they can function as a microRNA (miRNA) sponge, binding to miRNAs and regulating them and their downstream gene targets, through a competing endogenous (ce) RNA mechanism (11).

The present study comprehensively investigated the expression profile of circRNAs in HCa patients. The results identified a circRNA signature in HCa and suggested that a core miRNA-ceRNA network, regulating both the ErbB and Hippo signaling pathways, may have important roles in HCa progression.

Materials and methods

Patients and specimens

The study included three patients with HCa who underwent partial or radical cystectomies at the First Affiliated Hospital of Kunming Medical University (Kunming, China); samples were collected from March 2017 to October 2017. All three patients were male and their ages were 44, 54 and 56. Following surgery, the matched specimens were immediately preserved in liquid nitrogen until use. All patient samples were confirmed by pathological examination and none of the patients received neoadjuvant therapy. The study was approved by the Second Department of Otolaryngology Head and Neck Surgery of the First Affiliated Hospital of Kunming Medical University (Kunming, China). Written informed consent was obtained from all the participants in the study.

Total RNA isolation and quality control

Total RNA was isolated from samples using TRIzol reagent (Thermo Fisher Scientific, Inc., Waltham, MA, USA) following the manufacturer’s protocol. The quantity and quality of total RNA samples were measured using NanoDrop ND-1000 (Thermo Fisher Scientific, Inc.). RNA integrity was assessed and confirmed via electrophoresis using denaturing agarose gels. Isolated RNA samples were stored at −80°C prior to use.

Library preparation and sequencing

Total RNA from three matched HCa samples and adjacent normal tissues were treated with Epicenter Ribo-Zero rRNA Removal kit (Illumina, Inc., San Diego, CA, USA) and RNase R (Epicenter; Illumina, Inc.) to remove ribosomal and linear RNA. Then, the RNA-seq libraries were constructed using TruSeq Stranded Total RNA HT/LT Sample Prep kit (Illumina, Inc.). Sequencing was determined on Illumina Hiseq 2500 instrument with 2×150 bp paired reads.

Computational analysis of circRNAs

The clean reads were obtained after the raw reads were preprocessed with the FastQC quality control tool (12). CircRNAs were identified using CIRI (v.1.2) pipeline with default parameters (13). Genomic circRNAs were mapped to the human reference genome (GRCh37) by BWA (14). All circRNAs were annotated for circRNA-hosting genes with the application of GENCODE v24 (15). The identified circRNAs were converted to circRNA ID with web server circBase (16).

Principal component analysis (PCA)

PCA was performed as previously described (17). A total of 4,634 distinct circRNAs with non-zero raw counts across the six samples were isolated and expressions of circRNAs were normalized with the reads per Million mapped reads (RPM) method and the expression matrix (each row represented a gene, each column represented a sample) were used for PCA. The prcomp package from R was used to perform PCA and the default parameters were used (18). The ggplot2 package from R was used to draw the scatter plot (19).

Normalization and differential expression analysis of circRNAs

Two steps were performed to normalize circRNA expression for depth. Firstly, the total back-spliced reads in a sample were counted and that number was divided by 1,000,000. This resulted in the ‘per million’ scaling factor. Secondly, the read counts were divided by the ‘per million’ scaling factor. This method normalized for sequencing depth, giving RPM. CircRNAs were isolated with RPM>0 across 6 samples and Mann-Whitney U test (20) (paired=T) followed by Benjamini-Hochberg multiple testing correction (21) were applied to identify the differentially expressed (DE) circRNAs. FDR<0.05 and a fold change of >2.0 or <0.5 were the selection criteria for significant DE circRNAs.

Functional enrichment analysis

Gene ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted with web server DAVID 6.8 (22). P<0.05 was considered as statistically significant.

CeRNA network

The top 20 upregulated circRNAs and the top 20 downregulated circRNAs were used to survey miRNA targets with the web tool CircInteractome (23). Specifically, CircInteractome downloads the mature sequences of circRNAs from the UCSC browser mirror ( (24) and predicts miRNAs that target circRNA by surveying for 7-mer or 8-mer complementarity to the seed region, as well as the 3′end of each miRNA using the TargetScan algorithm (25). The complete miRNA list and sequences were taken from the miRBase ( (16). miRNA downstream targets were isolated with mirPath 3.0 (26) which was also used for miRNA KEGG pathway analysis. The ceRNA network was displayed by Cytoscape (v3.5.1) (27).

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR)

Total RNA was extracted from pooled normal and tumor tissue samples using TRIzol (Thermo Fisher Scientific, Inc.), and 1 µg of total RNA was reverse transcribed into first-strand cDNA using a PrimeScript RT Reagent kit (Takara Bio, Inc., Otsu, Japan), according to the manufacturer’s protocols. qPCR was performed with a SYBR-Green real-time PCR kit (Thermo Fisher Scientific, Inc.) using the ABI StepOnePlus Real-Time PCR system (Applied Biosystems; Thermo Fisher Scientific, Inc.). CircRNAs were analyzed with 18s rRNA as the internal standard and miRNA was analyzed with U6 as the internal standard. The reactions were prepared as follows: 7.5 µl SYBR Premixm Ex Taq II, 0.25 µl ROX Reference Dye II, 0.125 µl forward primer, 0.125 µl reverse primer, 5 µl RNase-free water, and 2 µl cDNA. The thermocycling conditions were: one step at 95°C for 30 sec, followed by 40 cycles of 95°C for 5 sec and 60°C for 30 sec, and a final step of 95°C for 15 sec, 60°C for 15 sec and 95°C for 15 sec. Primer sequences are listed in Table I; expression levels were quantified via the 2−ΔΔCq method (28).

Table I

Primer sequences used for reverse transcription-quantitative polymerase chain reaction analysis.

Table I

Primer sequences used for reverse transcription-quantitative polymerase chain reaction analysis.

GeneForward primer (5′-3′)Reverse primer (5′-3′)
Expression analysis of miR-548c-3p

Two methods were used to investigate the expression of miR-548c-3p among normal and tumor samples. The first was RT-qPCR, as detailed above. The second was in-silico analysis. The miRNA dataset of the esophageal carcinoma cohort from The Cancer Genome Atlas (TCGA) project (29) was exploited. There were 13 normal samples and 184 tumor samples in this dataset. Normalized miRNA expressions of miR-548c-3p were compared between normal and tumor samples. Mann-Whitney U test was applied to test the significance.

Survival analysis

A Kaplan-Meier curve was used to examine the clinical relevance of miR-548c-3p levels in the patients’ outcomes (30). Patients were separated into two groups according to the median expression of hsa-miR-548c-3p using TCGA clinical and expression dataset. Differences between groups were analyzed using log-rank test (31) and two-tailed P-values <0.05 were considered statistically significant. Statistical analyses were performed using the survival package (version 2.39-5) in R (version 3.4.3).

Expression correlation of hsa-miR-548c-3p and its targeted genes

The miRNA and mRNA datasets of the esophageal carcinoma cohort from TCGA (29) were used for the correlation analysis. Common samples were isolated according to the sample barcodes. The Pearson correlation method was used to assess the expression association between hsa-miR-548c-3p and the targeted genes. Significance of association was determined by the R package cor.test (alternative=‘two.sided’, method=‘pearson’). Then, P-values were corrected with Benjamini-Hochberg procedure for multiple testing.

Statistical analysis

All statistical analyses were generated using R (32). The Pearson correlation method was used to assess the expression association. Significances of associations were determined by the R package cor.test. Mann-Whitney U test was used for comparisons between two groups. Benjamini-Hochberg procedure was applied for multiple testing. Log-rank test was used for Kaplan-Meier survival curves. P<0.05 was considered to indicate a statistically significant difference.


Identification of DE circRNAs in HCa

To identify DE circRNAs in HCa, circRNA sequencing (Seq) was performed using three matched normal and HCa tissue samples, and an average of 90 million reads was achieved for each sample. A total of 4,634 distinct circRNAs with at least two unique back-spliced reads across six samples using CIRI pipeline (13) were identified and the expressions of circRNAs were normalized and represented by reads per million mapped reads (RPM) values. Genetic distances across 6 samples were evaluated using PCA (Fig. 1A), and the normalized expression level (RPM) of circRNAs across the six samples is illustrated in Fig. 1B. Following statistical analysis, 71 and 102 circRNAs were determined to be significantly upregulated and downregulated, respectively (Table II). The DE circRNAs between tumor and adjacent normal samples were presented in a heatmap (Fig. 1C). To confirm the circRNA-Seq results, RT-qPCR was performed to assess the expression of 19 of the above DE circRNAs in both normal and tumor samples. The results confirmed that 12 of them were consistently upregulated or downregulated with the circRNA-Seq results (Fig. 2).

Table II

Differentially expressed circRNAs.

Table II

Differentially expressed circRNAs.

circRNA ID (CIRI)circRNA ID (circBase)Adjusted P-valueFCGene
chr16:21973780-21987564 hsa_circ_00056900.0220029299.346453412UQCRC2
chr2:242343242-242357524 hsa_circ_00049240.0421060034.237176918FARP2
chr7:72873865-72884813 hsa_circ_00046700.0035794754.136199328BAZ1B
chr5:133871547-133887899 hsa_circ_00056080.0361323293.805820016PHF15
chr22:41979962-41980607 hsa_circ_00057030.0304066063.76275756PMM1
chr3:48019354-48040369 hsa_circ_00052550.0391839193.736266721MAP4
chr12:27521194-27523163 hsa_circ_00090090.011650163.553331882ARNTL2
chr9:117399269-117401006 hsa_circ_00023180.0411591983.381349856 C9orf91
chr1:165859440-165860559 hsa_circ_00067580.0417589583.367866028UCK2
chr16:50321822-50322261 hsa_circ_00006990.0439120283.314878392ADCY7
chr16:89484691-89497734 hsa_circ_00007270.0354119873.287797218ANKRD11
chr8:98817580-98837381 hsa_circ_00032140.0184835583.272900498LAPTM4B
chr19:48229068-48229481 hsa_circ_00031460.0060190623.268823068EHD2
chr1:118003110-118045592 hsa_circ_00020590.0105723163.169435071MAN1A2
chr14:92264128-92268765 hsa_circ_00329690.0401490423.130356466TC2N
chr2:210968827-211019335 hsa_circ_00026170.0209689373.121028088 C2orf67
chr15:94899365-94945248 hsa_circ_00006600.0322921493.092238966MCTP2
chr2:43655238-43657441 hsa_circ_00543090.0027829533.091209609THADA
chr2:110321942-110323436 hsa_circ_00090200.039173143.05906561SEPT10
chr19:2137009-2138713 hsa_circ_00483440.0408055973.048246172AP3D1
chr16:4311779-4312702 hsa_circ_00024390.0462405252.987845154TFAP4
chr11:128993340-128997200 hsa_circ_00050270.0017640732.945490868ARHGAP32
chr8:62460629-62479877 hsa_circ_00846040.0187940622.926235997ASPH
chr3:155628480-155643155 hsa_circ_00081840.0378548752.918397576GMPS
chr16:47531309-47581459 hsa_circ_00047910.0483878752.9019822PHKB
chr20:35457456-35467844 hsa_circ_00602190.0156228272.89815993KIAA0889
chr3:195101737-195112876 hsa_circ_00073310.0487153752.872843159ACAP2
chr22:29517344-29521404 hsa_circ_00045470.0447717572.802768657KREMEN1
chr2:122260742-122287901 hsa_circ_00023740.0372278072.731455995CLASP1
chr3:128514202-128526514 hsa_circ_00063460.0460032372.690296373RAB7A
chr4:83793096-83796975 hsa_circ_00035490.0446209732.679827252SEC31A
chr12:1399017-1481143 hsa_circ_00249970.0118820222.668639886ERC1
chr16:30715384-30715636 hsa_circ_00390760.0306650312.628113834SRCAP
chr7:2400344-2404164 hsa_circ_00048690.0337966972.611610945EIF3B
chr2:55209650-55214834 hsa_circ_00010060.0458999112.579018715RTN4
chr12:42768664-42792796 hsa_circ_00039610.0108664892.568055885PPHLN1
chr7:2404006-2406083 hsa_circ_00016710.0229077292.551488457EIF3B
chr4:87685745-87689129 hsa_circ_00079480.0229457042.547028768PTPN13
chr1:176085759-176105683 hsa_circ_00153730.0299309442.536801963RFWD2
chr22:36737414-36745300 hsa_circ_00044705.2192E-052.529021894MYH9
chr2:32396355-32409407 hsa_circ_00534230.0310869822.514457353SLC30A6
chr7:138951078-138957186 hsa_circ_00055940.0277339612.504492053UBN2
chr9:95030455-95032265 hsa_circ_00083670.0165131492.486273648IARS
chr7:65705311-65751696 hsa_circ_00060410.0055145782.410252357TPST1
chr4:75040222-75067087 hsa_circ_00699810.0326579592.370951734MTHFD2L
chr1:246021797-246093239 hsa_circ_00172890.0108428852.366575966SMYD3
chr22:29090019-29091861 hsa_circ_00048110.0018316252.363098777 CHEK2
chr16:3900297-3901010 hsa_circ_00076370.0091545772.351311388 CREBBP
chr2:168920009-168931741 hsa_circ_00032790.0003935972.342009076STK39
chr10:101728871-101731891 hsa_circ_00083930.0495644342.328443205 DNMBP
chr9:140646782-140652463 hsa_circ_00019040.0317454592.316860866EHMT1
chr22:46125304-46136418 hsa_circ_00012470.0135269422.31185877ATXN10
chr14:23419522-23421892 hsa_circ_00056630.045382122.257072167HAUS4
chr3:172363412-172365904 hsa_circ_00070420.0269897582.238835674NCEH1
chr2:10799297-10808849 hsa_circ_00085110.0427582862.221921485NOL10
chr10:70696691-70703013 hsa_circ_00070970.0408199242.219586601 DDX50
chrX:14868626-14877456 hsa_circ_00069710.0092112452.215797457FANCB
chr1:23356961-23385660 hsa_circ_00078220.0272401292.206406433KDM1A
chr20:13539654-13561628 hsa_circ_00020010.0178084982.188222168TASP1
chr7:139741443-139757834 hsa_circ_00046840.0268132062.168759032PARP12
chr7:72883846-72884813 hsa_circ_00038660.0129042682.108744306BAZ1B
chr18:21644103-21663045 hsa_circ_00472700.0206491372.07972359TTC39C
chr1:31532050-31532424 hsa_circ_00000450.0092598982.078191163PUM1
chr2:63660878-63667005 hsa_circ_00034970.0335263192.072706608WDPCP
chr10:128859931-128908618 hsa_circ_00204620.0140212982.068064026 DOCK1
chr1:44773981-44804994 hsa_circ_00076930.0224064092.066977275ERI3
chr7:99621041-99621930 hsa_circ_00017270.0439872212.025308683ZKSCAN1
chr16:11873021-11876244 hsa_circ_00054200.0459747170.499738449ZC3H7A
chr20:13539654-13561628 hsa_circ_00020010.039372260.497281358TASP1
chr3:43341245-43345284 hsa_circ_00040890.0079853020.491228173SNRK
chr2:11905658-11907984 hsa_circ_00022290.0139045140.48799699LPIN1
chr17:60111147-60112969 hsa_circ_00042730.0107275190.487251947MED13
chr2:168920009-168986268 hsa_circ_00058820.0182535590.481253188STK39
chr16:53532302-53534241 hsa_circ_00040720.0218674180.480875236AKTIP
chr9:95030455-95032265 hsa_circ_00083670.0246082070.480576493IARS
chr9:14146687-14179779 hsa_circ_00863760.0250171350.480402794NFIB
chr20:35457456-35467844 hsa_circ_00602190.019655150.472752875KIAA0889
chr15:80412669-80415142 hsa_circ_00006430.0196565940.47081423ZFAND6
chr15:62299506-62306191 hsa_circ_00006070.018622740.47022119VPS13C
chr10:128859931-128908618 hsa_circ_00204620.0485263130.46671725 DOCK1
chr1:32381495-32385259 hsa_circ_00073640.0129049620.464916349PTP4A2
chr8:37734626-37735069 hsa_circ_00017890.0198467690.462223466RAB11FIP1
chr13:28748408-28752072 hsa_circ_00043720.0237657650.461563778PAN3
chr18:9931806-9937063 hsa_circ_00069900.0396231120.461171429VAPA
chr17:34910660-34923615 hsa_circ_00039300.0217483310.458221861GGNBP2
chr22:46125304-46136418 hsa_circ_00012470.0080454290.453521935ATXN10
chr2:206992520-206994966 hsa_circ_00024310.0364370060.452588631NDUFS1
chr2:62100136-62103369 hsa_circ_00010180.0297995010.451702188 CCT4
chr11:119144577-119145663 hsa_circ_00003620.0256217240.450849769 CBL
chr22:29682911-29683123 hsa_circ_00080440.0100963950.44308523EWSR1
chr14:21971315-21972024 hsa_circ_00005230.0475125630.44023344METTL3
chr7:72883846-72884813 hsa_circ_00038660.0043903680.424421462BAZ1B
chr18:46858233-46906128 hsa_circ_00025010.035735140.420126159 DYM
chr1:246784730-246797889 hsa_circ_00173110.0284622490.41970008CNST
chr12:1399017-1481143 hsa_circ_00249970.0401741710.417295868ERC1
chr12:27521194-27523163 hsa_circ_00090090.0247111140.414722924ARNTL2
chr1:52959282-52975384 hsa_circ_00036320.0457084350.414593811ZCCHC11
chr5:50055476-50059076 hsa_circ_00067870.0184884740.412395338PARP8
chr3:179096128-179104417 hsa_circ_00022190.0292242820.403006229MFN1
chr17:26490568-26499644 hsa_circ_00036380.0133352580.397922038NLK
chr16:8952206-8953192 hsa_circ_00006690.0007151690.397901388CARHSP1
chr11:85685750-85695016 hsa_circ_00066290.019910690.396956177PICALM
chr14:35519989-35522657 hsa_circ_00064240.043226340.387507944FAM177A1
chr12:42768664-42792796 hsa_circ_00039610.0144654560.386034041PPHLN1
chr1:246021797-246093239 hsa_circ_00172890.0111814560.38452595SMYD3
chr10:27431315-27434519 hsa_circ_00056330.0091313140.384171219YME1L1
chr12:129299319-129299615 hsa_circ_00004620.0069410250.380095649SLC15A4
chr1:118003110-118045592 hsa_circ_00020590.0022204170.379882753MAN1A2
chr8:17123415-17126465 hsa_circ_00085920.0400122630.373149729VPS37A
chr10:99915849-99923154 hsa_circ_00044190.0321036170.372519687 C10orf28
chr20:17933230-17934761 hsa_circ_00067040.0205607150.371748201SNX5
chr1:31532050-31532424 hsa_circ_00000450.0409017720.368955101PUM1
chrX:14868626-14877456 hsa_circ_00069710.0323860450.36887067FANCB
chr16:3900297-3901010 hsa_circ_00076370.0203401320.36603697 CREBBP
chr4:103644027-103647840 hsa_circ_00060070.025705680.359726565MANBA
chr18:18619432-18624147 hsa_circ_00067330.0375955230.353388689ROCK1
chr14:52977957-53011089 hsa_circ_00319390.019667470.351937505TXNDC16
chr21:37711076-37717005 hsa_circ_00011890.0100821960.351308477MORC3
chr1:94685813-94697199 hsa_circ_00033100.02596110.351119971ARHGAP29
chr3:47103652-47108608 hsa_circ_00651590.0205219330.350064342SETD2
chr16:71779046-71779517 hsa_circ_00025050.0461851360.343484211AP1G1
chr5:31421378-31424578 hsa_circ_00055240.0449663530.34303411DROSHA
chr11:1307231-1317024 hsa_circ_00083010.0189421310.342187543TOLLIP
chr19:48229068-48229481 hsa_circ_00031460.0205621640.340720207EHD2
chr3:37170553-37190529 hsa_circ_00032640.0131549320.336803278LRRFIP2
chr7:65705311-65751696 hsa_circ_00060410.0392829610.334902033TPST1
chr7:27668989-27689252 hsa_circ_00067730.0039359980.328463848HIBADH
chr10:32308785-32310215 hsa_circ_00064080.0436409240.325878207KIF5B
chr3:56600621-56601081 hsa_circ_00013120.0297558560.321652942 CCDC66
chr7:73100965-73101425 hsa_circ_00055880.0420735870.318524548WBSCR22
chr7:72873865-72884813 hsa_circ_00046700.0433429210.316315928BAZ1B
chr2:242282406-242283312 hsa_circ_00590600.0078109990.315723287SEPT2
chr3:47139444-47144913 hsa_circ_00012890.0392747320.313502465SETD2
chr2:43655238-43657441 hsa_circ_00543090.0385408750.309703944THADA
chr21:46275124-46281186 hsa_circ_00012000.04748820.302025085PTTG1IP
chr5:179976930-179980471 hsa_circ_00088360.0287909050.292442462 CNOT6
chr1:87185189-87190088 hsa_circ_00130840.015670650.280991627SH3GLB1
chr19:53577392-53578436 hsa_circ_00074800.0317845330.275005495ZNF160
chr22:29090019-29091861 hsa_circ_00048110.0084388510.252361922 CHEK2
chrX:117718697-117724265 hsa_circ_00913820.0327650140.247214419 DOCK11
chr11:128993340-128997200 hsa_circ_00050270.0413412750.246885645ARHGAP32
chr15:34542498-34543258 hsa_circ_00343460.0397076430.246690583SLC12A6
chr10:70152894-70154208 hsa_circ_00002390.0085123720.236281903RUFY2
chr19:33604672-33605325 hsa_circ_00082870.0421620870.207707068GPATCH1
chr2:168920009-168931741 hsa_circ_00032790.0017673520.196582978STK39
chr18:9524591-9525849 hsa_circ_00051580.0433927310.166193525RALBP1
chr22:36737414-36745300 hsa_circ_00044700.0424948360.147382721MYH9

[i] The criteria for the differential expression were: Adjusted P<0.05 and FC>2 or FC<0.5. The top 20 upregulated and downregulated genes are presented in bold. circRNA, circular RNA; FC, fold change.

Next, the distribution of circRNAs in different DNA elements and chromosomes was examined. The bar diagram of Fig. 3A demonstrates the % of back-spliced junction reads on intron, intergenic, and exon areas. The majority of circRNAs belonged to exonic, followed by intronic and intergenic elements (Fig. 3B). These dysregulated circRNAs are widely distributed in all chromosomes, including sex chromosomes X (Fig. 3C).

Functional enrichment analysis of genes producing DE circRNAs

To reveal the dysregulated pathways underlying HCa, first KEGG pathway enrichment analyses were performed for genes that matched DE circRNAs. The results demonstrated that genes containing downregulated circRNAs were enriched in endocytosis, ubiquitin-mediated proteolysis, and Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathways (Fig. 4A), whereas there were no KEGG pathways enriched with genes producing upregulated circRNAs.

Next, GO term enrichment analyies was performed for genes that produced aberrantly expressed circRNAs. Biological processes, such as the establishment of spindle orientation, response to fungicide, positive regulation of transcription, cell division were significantly enriched (Fig. 4B), whereas genes producing downregulated circRNAs were related to autophagy, mitochondrion organization actin cytoskeleton organization, membrane fission, and cell-cell adhesion pathways (Fig. 4C). These results suggested that multiple pathways may contribute to HCa pathogenesis and progression.

CircRNAs regulate the ErbB and Hippo pathways through a miRNA-CeRNA network

The role of circRNAs as a miRNA sponge is the main mechanism of circRNA function in tumor cells (11,33). Therefore, we further investigated the roles of circRNAs in HCa progression through establishing a ceRNA network. Firstly, the top 20 upregulated and top 20 downregulated circRNAs were isolated and were converted to circRNA ID using circBase database (34). Secondly, miRNAs targeting DE-circRNAs were isolated with the web server CircInteractome (23). Specifically, CircInteractome downloaded the mature sequences of all of the reported circRNAs from the UCSC browser, then to characterize miRNA-circRNA interactions, CircInteractome incorporated the ability to search using the TargetScan algorithm, which predicts miRNAs that target circRNA by surveying for 7-mer or 8-mer complementarity to the seed region as well as the 3′end of each miRNA (23). A total of 191 and 182 miRNAs were putatively identified as the targets of upregulated and downregulated circRNAs, respectively. Networks consisted of circRNAs and miRNAs were displayed using Cytoscape software (27). The results demonstrated extensive interactions between miRNAs and upregulated (Fig. 5A), and downregulated circRNAs (Fig. 5B). Then, KEGG pathway enrichment analysis was performed for the miRNAs targeted by the top 40 DE circRNAs, in order to explore the altered biological processes using mirPath 3.0 (26). Genes targeted by miRNAs were significantly enriched in multiple signaling pathways, including the ErbB, the Hippo, the Ras, the transforming growth factor (TGF)-β, the phosphoinositide 3-kinase/AKT serine/threonine kinase and the Wnt signaling pathways (Fig. 5C).

To get further insight into the function of circRNAs in the ErbB and Hippo signaling pathways, miRNA-ceRNA networks were constructed corresponding to the two pathways using Cytoscape. For the miRNA-ceRNA network regulating the ErbB pathway, there were 33 circRNAs, 43 miRNAs and 74 ErbB pathway genes (Fig. 6A). In the ErbB miRNA-ceRNA network, we isolated a subnetwork consisting of circRNAs (hsa_circ_0008287 and hsa_circ_0005027), miRNAs (hsa-miR-548c-3p) and 38 ErbB pathway genes which had the most interaction between miRNAs and targeted genes (Fig. 6B). Hsa_circ_0008287 and hsa_circ_0005027 were significantly downregulated in tumor samples compared with normal (Figs. 2 and 6C). In a similar manner, the miRNA-ceRNA network regulating the Hippo pathway was constructed, consisting of 33 circRNAs, 43 miRNAs and 110 Hippo pathway genes (Fig. 7A). In the Hippo miRNA-ceRNA network, we also isolated a subnetwork consisting of circRNAs (hsa_circ_0008287 and hsa_circ_0005027), miRNAs (hsa-miR-548c-3p) and 61 Hippo pathway genes, which had the most interaction between miRNAs and targeted genes (Fig. 7B).

To further investigate the important role of this subnet-work in tumor progression, the miRNA and mRNA datasets of the esophageal carcinoma cohort from TCGA (29) were exploited. The esophageal carcinoma cohort contains 13 normal samples and 184 tumor samples. In this cohort, the miRNA hsa-miR-548c-3p expression between normal and tumor samples was detected, and its clinical relevance to patient survival was analyzed. The results suggested that hsa-miR-548c-3p was highly expressed in tumor samples compared with normal samples (Fig. 8A and B), and its high expression was significantly associated with lower survival in patients with esophageal carcinoma (Fig. 8C). These findings suggested that hsa-miR-548c-3p is an oncogenic miRNA, which is consistent with the hypothesis that in tumor samples circRNAs were downregulated resulting in more oncogenic hsa-miR-548c-3p being released, and highly expressed hsa-miR-548c-3p may promote HCa progression through downstream target genes. To confirm the negative regulation of hsa-miR-548c-3p on the ErbB and Hippo pathway genes, the expression correlation of hsa-miR-548c-3p and its targeted genes were also analyzed. Many of the targeted genes were negatively correlated with hsa-miR-548c-3p levels, which supported a negative regulatory role of hsa-miR-548c-3p on the ErbB and Hippo pathways (Table III). The present results demonstrated that circRNAs regulate HCa progression through multiple pathways and identifying a miRNA-ceRNA network that regulated the ErbB and Hippo signaling pathways.

Table III

Expression correlation of hsa-miR-548c-3p and its targeted genes in The Cancer Genome Atlas esophageal carcinoma cohort.

Table III

Expression correlation of hsa-miR-548c-3p and its targeted genes in The Cancer Genome Atlas esophageal carcinoma cohort.

miRNAGeneCorrelation coefficientFDR
hsa-miR-548c-3p CTGF−0.1394349910.050682
hsa-miR-548c-3p DLG2−0.1360019420.056703
hsa-miR-548c-3p CCND2−0.1138802940.11107
hsa-miR-548c-3p CBLB−0.0838384260.241477
hsa-miR-548c-3p DLG4−0.0832174710.244993
hsa-miR-548c-3p DLG1−0.0724179360.311876
hsa-miR-548c-3p CTNNA3−0.066692160.351776
hsa-miR-548c-3p CBL−0.0063748310.929156
hsa-miR-548c-3p CRKL−0.0055914220.937844
hsa-miR-548c-3p CRK0.0313855850.661515
hsa-miR-548c-3p CRB10.0328042740.647223
hsa-miR-548c-3p CTNNB10.0579675630.418448

[i] miRNA, microRNA; FDR, false discovery rate.


HCa is clinically difficult to diagnose and has a poor prognosis, therefore, identifying early stage molecular biomarkers has become urgent. CircRNAs, which are stable and easier to extract and detect, are considered ideal candidates for early-stage biomarkers. This is the first report on the expression profile of circRNAs in HCa. In the present study, a number of aberrantly expressed circRNAs in HCa samples were identified. Pathway enrichment results revealed that circRNAs may regulate HCa progression through multiple signaling pathways, especially the ErbB and Hippo signaling pathways. These results provided several potential biomarkers and therapeutic targets for HCa.

The ceRNA hypothesis was described as a way that RNAs communicate with each other, via competing for binding to miRNAs and regulating the expression of each other to construct a complex post-transcriptional regulatory network (35,36). mRNAs and long non-coding (lnc) RNAs may all serve as ceRNAs (37). It has been demonstrated that circRNAs can also function as miRNA sponges (6,11). The present study demonstrated that aberrantly expressed circRNAs have extensive interactions with miRNAs, and those miRNAs exerted their effect on multiple cancer-related pathways. These data indicated that the circRNA-associated ceRNA network may have crucial roles in HCa progression.

The activation of ErbB oncogenes has been described in various types of human tumors, including hypopharynx carcinomas, and it has been correlated with a poor prognosis. For example, one study describing the molecular alterations in hypopharynx carcinomas demonstrated that ErbB1 was amplified in 29% of patients with hypopharyngeal squamous cell carcinomas (38). In addition, ErbB1 amplification is correlated with a hypopharyngeal primary site (39). Another study reported that v-erbB stained positively in 62.5% of hypopharyngeal squamous cell carcinomas samples but negatively in normal mucosa (40). The present ceRNA network analysis demonstrated that a circRNA (hsa_circ_0008287 and hsa_circ_0005027)/miRNA (hsa-miR-548c-3p) axis may have important roles in ErbB-mediated tumor progression (Fig. 6).

Another pathway that is likely to be associated with hypopharynx carcinomas is the Hippo signaling pathway. The Hippo pathway has generated considerable interest in recent years because of its involvement in several key hallmarks of cancer progression and metastasis (41). Regulation of Hippo signaling can be an attractive alternative strategy for cancer treatment (42-44). Previously, ACTL6A and p63 were demonstrated to cooperatively promote head and neck squamous cell carcinoma, through activation of the Hippo/Yes-associated protein 1 (YAP) pathway and YAP activation can predict poor patient survival (45). The present ceRNA network analysis demonstrated that a circRNA (hsa_circ_0008287 and hsa_circ_0005027)/miRNA (hsa-miR-548c-3p) axis may have important roles in Hippo-mediated tumor progression (Fig. 7).

Extensive evidence has suggested that miRNAs have important roles in breast cancer. The miR-548 family has been demonstrated to be involved in the pathogenesis of several cancers. For example, miR-548-3p was significantly downregulated in breast cancer and overexpression of miR-548-3p inhibited the proliferation and promoted the apoptosis of breast cancer cells (46). Overexpression of miR-548c-3p was also confirmed in prostate epithelial stem cells and in castration-resistant prostate cancer cells (45). Overexpression of miR-548c-3p in differentiated cells induced stem-like properties and radio-resistance (45). Re-analyses of published studies further revealed that miR-548c-3p is significantly overexpressed in castration-resistant prostate cancer cells and is associated with poor recurrence-free survival, suggesting that miR-548c-3p is a functional biomarker for prostate cancer aggressiveness (47). The present results demonstrated that miR-548c-3p may have important roles in HCa progression through modulating the ErbB and Hippo pathways. Due to the crucial roles of miR-548c-3p in multiple types of cancer, development of novel gene therapies based on miR-548c-3p might be encouraged.

Taken together, the present study indicated that hsa_ circ_0008287 and hsa_circ_0005027 were downregulated in HCa and competitively bound miR-548c-3p with ErbB and Hippo signaling pathway genes. Further studies are warranted on the roles of hsa_circ_0008287, hsa_circ_0005027, and miR-548c-3p as potential diagnostic biomarkers and therapeutic targets for HCa.


Not applicable.


This work was funded by Yunnan Applied Basic Research Projects (grant no. 2016FB038).

Availability of data and materials

The sequencing data have been deposited in the Gene Expression Omnibus (GEO) database under the accession number GSE111423.

Authors’ contributions

CF designed experiments and helped analyze the data. YaL, YuL and XC collected the samples. DL analyzed data. HZ interpreted the results and wrote the manuscript. XH designed experiments and interpreted the results. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The study was approved by the Ethics Committee of the First Affiliated Hospital of Kunming Medical University (Kunming, China). Written informed consent was obtained from all the participants in the study.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.



hypopharyngeal cancer


differentially expressed


competing endogenous RNA


reads per Million mapped reads


gene ontology


Kyoto Encyclopedia of Genes and Genomes


principal component analysis



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January 2019
Volume 43 Issue 1

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Feng, C., Li, Y., Lin, Y., Cao, X., Li, D., Zhang, H., & He, X. (2019). CircRNA-associated ceRNA network reveals ErbB and Hippo signaling pathways in hypopharyngeal cancer. International Journal of Molecular Medicine, 43, 127-142.
Feng, C., Li, Y., Lin, Y., Cao, X., Li, D., Zhang, H., He, X."CircRNA-associated ceRNA network reveals ErbB and Hippo signaling pathways in hypopharyngeal cancer". International Journal of Molecular Medicine 43.1 (2019): 127-142.
Feng, C., Li, Y., Lin, Y., Cao, X., Li, D., Zhang, H., He, X."CircRNA-associated ceRNA network reveals ErbB and Hippo signaling pathways in hypopharyngeal cancer". International Journal of Molecular Medicine 43, no. 1 (2019): 127-142.