Literature DB >> 32218863

Integrated analysis of transcriptome profiling predicts potential lncRNA and circRNA targets in human nasopharyngeal carcinoma.

Dong-Ni Zhou1, Chun-Sheng Ye2, Qing-Qing Yang2, Yan-Fei Deng2,3.   

Abstract

Non-coding RNAs (ncRNAs) regulate numerous genes and influence the progression of various human diseases, including cancer. The role of regulatory ncRNAs implicated in nasopharyngeal carcinoma (NPC), as well as their target genes, remains unclear. The present study aimed to investigate specific long non-coding (lnc)RNAs, circular RNAs (circRNAs) and mRNAs associated with the molecular pathogenesis of NPC, and to predict the underlying target genes of specific lncRNAs and circRNAs. The expression levels of lncRNAs, circRNAs and mRNAs in NPC and chronic nasopharyngitis tissues were detected and analyzed using microarray and bioinformatics techniques. A total of 2.80% lncRNAs (425 upregulated and 431 downregulated) were significantly differentially expressed (DE) between the two tissue types. Additionally, 0.96% circRNAs (18 upregulated and 13 downregulated) were significantly DE, while 2.94% mRNAs (426 upregulated and 341 downregulated) were significantly DE between the two tissue types. In total, 420 NPC-associated nearby encoding genes (196 up- and 224 downregulated) of the DE lncRNAs were identified. Overlap analysis identified 23 DE circRNAs and their corresponding target genes, with 37 microRNAs and 50 mRNAs, from which 14 interaction networks were constructed. Subsequent pathway analysis revealed 221 DE target genes corresponding to 31 key signaling pathways associated with NPC, 14 of which may represent hub genes associated with NPC pathophysiology. Thus, certain lncRNAs, circRNAs and mRNAs are aberrantly expressed in NPC tissues, and partially specific lncRNAs, circRNAs and their target genes may influence the tumorigenesis and progression of NPC. Target prediction and regulatory network identification may help to determine the pathogenic mechanisms of NPC. Copyright: © Zhou et al.

Entities:  

Keywords:  bioinformatics analysis; circular RNA; long non-coding RNA; mRNA; microRNA; microarray; nasopharyngeal carcinoma

Year:  2020        PMID： 32218863      PMCID： PMC7068695          DOI： 10.3892/ol.2020.11412

Source DB:  PubMed          Journal:  Oncol Lett        ISSN： 1792-1074            Impact factor:   2.967


Introduction

Nasopharyngeal carcinoma (NPC) is the most prevalent malignancy in southern China and Southeast Asia. In total, ~129,000 new cases are reported annually worldwide, with >70% reported in South China and Southeast Asia (1). Its pathogenesis is associated with three primary etiological factors; Epstein-Barr virus infection, genetic susceptibility and environmental condition (2). However, the pathophysiological mechanism underlying NPC progression is yet to be elucidated. Previous studies have predominantly focused on the function of specific genes expressed in NPC instead of the molecular pathogenesis of the disease (1,2). Notably, >98% of human genes are non-protein coding, and the expression of these genes generates non-coding RNAs (ncRNAs) (3). There are multiple families of ncRNA, including ribosomal (r), short interfering (si), micro (mi), circular (circ) and long non-coding (lnc)RNA (4–6). The latter refers to ncRNAs >200 nucleotides in length (4,5). Previous studies have reported that lncRNAs are important regulators of numerous biological processes, including tumor progression (7,8). miRNAs are a class of small ncRNAs of ~22 nucleotides, which serve as key regulators of multiple disease-associated processes (9). Unlike linear RNA, circRNAs exhibit a covalently closed continuous loop, and serve as an miRNA sponge to regulate transcription (10). A number of studies have indicated that ncRNAs play important roles in transcriptional regulation by forming regulatory networks and subsequently interacting with their respective target genes (4–7). lncRNAs and miRNAs have become increasingly associated with the progression of NPC (11); however, the majority of studies have solely focused on the role of single or small groups of these molecules. Ma et al (12) reported that lncRNA HOX transcript antisense RNA contributes towards the tumorigenesis of NPC via the upregulation of fatty acid synthase. Moreover, Zhang et al (13) discovered that miRNA-200c acts as an oncogene in NPC, by regulating the phosphatase and tensin homolog genes. To the best of our knowledge, a limited number of studies to date have investigated the association between circRNA expression and NPC, and no data are currently available concerning circRNAs and their target genes in NPC. Shuai et al (14) indicated that circRNA_0000285 may be used as a novel biomarker for NPC radiosensitivity. As the initiation and progression of different types of tumor is a multi-gene, multi-step process, research is typically focused on gene regulatory networks at a genome-wide level. The purpose of the present study was to comprehensively analyze the expression profiles of lncRNAs, circRNAs and mRNAs in NPC, and their inter-regulatory molecular mechanisms. It was also aimed to identify the target genes of differentially expressed (DE) lncRNAs and circRNAs, and the DE genes (DEGs) within key signaling pathways influencing NPC progression, in order to elucidate the regulatory network in NPC. First, the present study compared the transcriptome profiling of lncRNAs, circRNAs and mRNAs between NPC and chronic nasopharyngitis (CNP) tissues, using microarray technology at the whole genome level. Subsequently, integrated bioinformatics analysis was performed on the three microarray datasets. The results of the present study may help to clarify the associations between lncRNAs, circRNAs and miRNAs (and their target genes), and elucidate the notable regulatory networks involved in the molecular pathogenesis of NPC.

Materials and methods

Specimens

A total of 42 human nasopharyngeal tissue samples were collected from 42 patients (30 men and 12 women) during nasopharyngeal biopsy between August 2013 and October 2014, at Zhongshan Hospital (Ximen, China). The tissue samples included 21 cases of primary NPC and 21 cases of CNP from patients suspected of having cancer. All specimens were confirmed by histopathological examination. Patients did not receive chemoradiotherapy and biotherapy prior to biopsy. The 21 patients with primary NPC comprised 15 men and six women (age range, 20–69 years; median, 45.5 years), and the 21 patients with CNP comprised 16 men and five women (age range, 21–60 years; median, 43.2 years). All tissues were immediately stored at −80°C following biopsy, prior to subsequent RNA extraction. Tumor tissues were isolated via micro-dissection and specimens containing >70% tumor cells were further analyzed. In total, six pairs of NPC and CNP specimens among the 42 collected tissues were selected for lncRNA, mRNA and circRNA expression microarray, while all 42 specimens were used for reverse transcription-quantitative (RT-q)PCR. These specimens were used to evaluate differences in the expression levels between NPC and CNP tissues. The present study was approved by the Medical Ethics Committee of Zhongshan Hospital, Xiamen University (Fujian, China) and written informed consent was obtained from all patients prior to the study start.

RNA extraction and quality control

Total RNA was extracted from each tissue sample using TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc.) and purified using the RNeasy Mini kit (Qiagen GmbH), according to the manufacturers' protocol. The quantity and quality of the RNA were determined using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies; Thermo Fisher Scientific, Inc.) and the RNA integrity was assessed via electrophoresis on a denaturing agarose gel.

RNA labeling, lncRNA and mRNA microarray

Sample labeling and microarray hybridization were performed using a modified version of the Agilent One-Color Microarray-Based Gene Expression Analysis protocol (Agilent Technologies, Inc.). The rRNA was removed from the total RNA sample using the Ribo-Zero™ rRNA Removal kit (Epicentre; Illumina, Inc.) and mRNA was purified using the mRNA-ONLY™ Eukaryotic mRNA Isolation kit (Epicentre; Illumina, Inc.). Subsequently, each sample was amplified and transcribed into fluorescent cRNA along the entire length of the transcripts, using a random priming method. The labeled cRNAs were purified using the RNeasy Mini kit (Qiagen GmbH), and the concentration and specific activity of the labeled cRNAs [pmol cyanine (Cy)3/µg cRNA] were measured using NanoDrop ND-1000 (NanoDrop Technologies; Thermo Fisher Scientific, Inc.). A total of 0.6 µg of each labeled cRNA was fragmented via the addition of 5 µl 10X blocking agent and 1 µl 25X fragmentation buffer. The mixture was heated at 60°C for 30 min, and 25 µl 2X GEx hybridization buffer (Agilent Technologies, Inc.) was added to dilute the labeled cRNA. A total of 40 µl hybridization solution was dispensed into a gasket slide and placed into the Human LncRNA Array (version 3.0; 8×60 K; Arraystar, Inc.), which contained 30,586 lncRNAs and 26,109 mRNAs. The slides were incubated for 17 h at 65°C in an Agilent Microarray hybridization oven. The hybridized arrays were washed with Gene Expression Wash Buffer (Agilent Technologies, Inc.) and subsequently fixed with 3.7% paraformaldehyde for 15 min at room temperature prior to being scanned using the Agilent DNA Microarray Scanner System (G2505C; Agilent Technologies, Inc.). Agilent Feature Extraction software (version 11.0.1.1; Agilent Technologies, Inc.) was used to analyze the acquired array images. Quantile normalization and subsequent data processing were performed using the GeneSpring GX software package (version 11.5.1; Agilent Technologies, Inc.). Following quantile normalization of the raw data, lncRNAs and mRNAs that were flagged as Present or Marginal (‘All Targets Value’), in ≥6 out of 12 samples, were selected for further analyses. Significantly DE lncRNAs and mRNAs (fold-change ≥2.0; P≤0.05) between the two groups were identified via volcano plot filtering. Hierarchical clustering was performed using the Agilent GeneSpring GX software (version 11.5.1; Agilent Technologies, Inc.). Gene Ontology (GO; http://geneontology.org) and Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg) pathway analyses were performed using the standard enrichment computation method for DE mRNAs. GO analysis was performed in order to characterize genes and gene products in terms of cellular component, molecular function and biological process. KEGG pathway analysis was performed to identify the signaling pathways in which DE mRNAs underwent significant enrichment, and thus predict the underlying biological functions of the DEGs. P<0.05 and the false discovery rate denoted the significance of the GO term enrichment and the biological pathways. The computational data analysis was performed by Kangchen BioTech Co., Ltd.

RNA labeling and circRNA microarray

Sample labeling and Arraystar Human circRNA Array hybridization (Arraystar, Inc.) were performed according to the manufacturer's protocol. Briefly, circRNAs were treated with RNase R (Epicentre; Illumina, Inc.) to remove the linear RNA. Each sample was subsequently amplified and transcribed into fluorescent cRNA using the Arraystar Super RNA Labeling kit (Arraystar, Inc.) and a random priming method. The labeled cRNAs were purified using the RNeasy Mini kit (Qiagen GmbH) and the concentration and specific activity of the labeled cRNAs (pmol Cy3/µg cRNA) was determined using NanoDrop ND-1000 (NanoDrop Technologies; Thermo Fisher Scientific, Inc.). A total of 1 µg of each labeled cRNA was fragmented using 5 µl 10X blocking agent and 1 µl 25X fragmentation buffer. The sample was heated at 60°C for 30 min, and 25 µl 2X hybridization buffer (Agilent Technologies, Inc.) was added to dilute the labeled cRNAs. A total of 50 µl of hybridization solution was dispensed into a gasket slide and assembled into the Arraystar Human CircRNA Microarray slide (Arraystar, Inc.). The slides were incubated for 17 h at 65°C in an Agilent hybridization oven. The hybridized arrays were washed with Gene Expression Wash Buffer (Agilent Technologies, Inc.) and subsequently fixed in 3.7% paraformaldehyde for 15 min at room temperature, prior to being scanned using the Agilent Microarray Scanner System (Agilent Technologies, Inc.). The scanned images were imported into Agilent Feature Extraction software (version 11.0.1.1; Agilent Technologies, Inc.) for raw data extraction. Quantile normalization of the raw data and subsequent data processing were performed using the R software package (version 3.28.0; http://bioconductor.org/packages/edgeR). Low-intensity filtering was performed and circRNAs that were flagged as Present or Marginal (‘All Targets Value’) in ≥6 out of 12 samples were retained for further analyses. The fold-change between the groups for each circRNA was computed to allow for comparisons between two groups of profile differences (such as cancer vs. inflammation). circRNAs with a fold-change ≥1.5 and P≤0.05 were selected as significantly DE. The analysis outputs were filtered and the DE circRNAs were ranked according to their fold-change and P-value using Microsoft Excel's Data/Sort & Filter functionalities (Microsoft Corporation). The computational data analysis was performed by Kangchen BioTech Co., Ltd.

RT-qPCR validation

Randomly selected DE lncRNAs, mRNAs and circRNAs were evaluated using RT-qPCR. The specific primer sequences for 12 lncRNAs, eight mRNAs and four circRNAs were designed using Primer (version 5.0; Premier Biosoft, Inc.) and are presented in Table SI. The total RNA (1.5 µg) was reverse transcribed into cDNA using the PrimeScript™ RT Reagent kit (Takara Bio, Inc.), according to the manufacturer's protocol. qPCR was performed on a total reaction volume of 10 µl, comprised of 5 µl 2X Master Mix (Arraystar, Inc.), 0.5 µl each of the PCR forward and reverse primers (10 µM), 2 µl DNA and 2 µl double-distilled water. The following thermocycling conditions were used for RT-qPCR: An initial denaturation step of 10 min at 95°C, followed by 40 cycles of 95°C for 10 sec and 60°C for 1 min. All experiments were performed in triplicate. For RT-qPCR validation analysis, all 42 samples were normalized to GAPDH. The fold-change in expression was calculated using the 2−ΔΔCq method (15).

Identification of the nearby coding genes of DE lncRNAs

A nearby coding gene is defined as a coding transcript <300 kb between the DE lncRNA and the neighboring coding mRNA. In the present study, genomic coordinate analysis of DE lncRNAs was performed alongside computational analysis of lncRNA and mRNA microarray data. The NPC-associated DE lncRNAs and their neighboring coding genes were annotated, and genomic coordinates of the lncRNAs, and the association between an lncRNA and its nearby coding gene were also detailed. Additionally, nearby coding genes of DE lncRNAs in NPC were obtained following lncRNA classification, subgroup analysis and genomic coordinate determination.

Predictive analysis of potential targets of DE circRNAs

Overlap analysis was performed via three steps. First, the circRNA-miRNA interaction was predicted with Arraystar's proprietary miRNA target prediction software (version 1.0) using datasets retrieved from the TargetScan and miRanda databases, and the DE circRNAs within all the comparisons were annotated in detail using the circRNA-miRNA interaction information. A total of five target miRNAs for each DE circRNA were subsequently identified according to the number of conservative miRNA binding sites. Subsequently, the candidate target mRNAs for the selected target miRNAs of DE circRNAs were analyzed using Overlap software (version 1.0; Kangchen BioTech Co., Ltd.), based on three miRNA databases (miRanda, miRDB and TargetScan). The intersection of the mRNAs between the aforementioned candidate mRNAs, and the mRNAs in the lncRNA and mRNA microarray data, was determined using Venny software (version 2.1; http://bioinfogp.cnb.csic.es/tools/venny/index.html). Following the determination of DE circRNAs and their corresponding target genes, the circRNA-miRNA-mRNA regulatory network was constructed and visualized using Cytoscape software (version 3.7.1; http://www.cytoscape.org).

Statistical analysis

The differences in lncRNA, circRNA and mRNA expression levels between NPC and CNP tissues (from the microarray and RT-q-PCR data) were analyzed using the paired Student's t-test, according to their fold-change. Fisher's exact test was used for GO and pathway analyses. P<0.05 was considered to indicate a statistically significant difference. For the microarray analysis, the false discovery rate was calculated to correct the P-value.

Results

Profiles of DE lncRNAs and mRNAs

Among all the lncRNA and mRNA probes in the microarray, 2.80% (856/30,586) lncRNAs (425 upregulated and 431 downregulated) and 2.94% (767/26,109) mRNAs (426 upregulated and 341 downregulated) were significantly DE between the two groups (Tables SII and SIII). The top 20 most significantly DE lncRNAs consisted of more upregulated lncRNAs compared with downregulated lncRNAs (ratio, 16:4). Furthermore, uc004ebm.1 (fold-change, 38.478134) was the most significantly upregulated lncRNA, while ENST00000572818 was the most significantly downregulated lncRNA (fold-change, 12.04174). Of the 20 most significantly DE mRNAs; the number of upregulated and downregulated mRNAs were equal (ratio, 10:10). Furthermore, SPRR2E (fold-change, 63.81733) was the most significantly downregulated mRNA, while PCDH10 (fold-change, 18.465176) was the most significantly upregulated mRNA. Differences in mRNA expression between NPC and CNP tissues are presented using a volcano plot, scatter plot and clustering heat-map (Fig. 1A-C, respectively). In addition, pathway analysis revealed that 31 significantly enriched signaling pathways (18 upregulated and 13 downregulated) corresponded to DE mRNAs in this microarray (Tables I, II, SIV and SV).
Figure 1.

Bioinformatics analysis of differentially expressed mRNAs in NPC and CNP tissues. (A) Volcano plot demonstrating the distribution of mRNAs. (B) Scatter plot exhibiting variation in mRNA expression. (C) Heatmap clustering indicating a distinguishable association between the mRNA expression patterns of certain samples. mRNA expression levels are indicated as follows; Red, high expression and green, low expression. NPC, nasopharyngeal carcinoma; CNP, chronic nasopharyngitis.

Table I.

Key signaling pathways associated with upregulated DEGs.

Pathway IDDefinitionDEGs
hsa04060Cytokine-cytokine receptor interaction-Homo sapiens (human)CCL2, CCL4, CCR8, CXCL10, CXCL2, CXCL3, CXCL6, CXCR6, EGFR, FAS, GHR, IFNG, IL12A, IL15, IL22RA2, IL23A, LIFR, TNFRSF11B, TNFSF10, TNFSF18
hsa05164Influenza A-Homo sapiens (human)CCL2, CXCL10, EIF2AK2, FAS, IFIH1, IFNG, IL12A, MX1, OAS1, OAS2, RSAD2, STAT1, TMPRSS13, TNFSF10
hsa05162Measles-Homo sapiens (human)EIF2AK2, FAS, IFIH1, IFNG, IL12A, MX1, OAS1, OAS2, STAT1, TNFSF10
hsa00260Glycine, serine and threonine metabolism-Homo sapiens (human)CHDH, GATM, PIPOX, PSAT1, SDS
hsa04630Jak-STAT signaling pathway-Homo sapiens (human)GHR, IFNG, IL12A, IL13RA2, IL15, IL22RA2, IL23A, LIFR, SPRY2, STAT1
hsa05160Hepatitis C-Homo sapiens (human)CLDN1, EGFR, EIF2AK2, IFIT1, OAS1, OAS2, PPP2R2B, PPP2R2C, STAT1
hsa05168Herpes simplex infection-Homo sapiens (human)CCL2, EIF2AK2, FAS, IFIH1, IFIT1, IFNG, IL12A, IL15, OAS1, OAS2, STAT1
hsa05142Chagas disease (American trypanosomiasis)-Homo sapiens (human)C1QB, CCL2, FAS, IFNG, IL12A, PPP2R2B, PPP2R2C
hsa04940Type I diabetes mellitus-Homo sapiens (human)FAS, GAD1, IFNG, IL12A
hsa05144Malaria-Homo sapiens (human)CCL2, IFNG, IL12A, KLRK1
hsa05412Arrhythmogenic right ventricular cardiomyopathy (ARVC)-Homo sapiens (human)CACNA2D1, DSG2, DSP, ITGAV, ITGB8
hsa04062Chemokine signaling pathway-Homo sapiens (human)CCL2, CCL4, CCR8, CXCL10, CXCL2, CXCL3, CXCL6, CXCR6, STAT1
hsa04080Neuroactive ligand-receptor interaction-Homo sapiens (human)C3AR1, CHRNA7, CHRNB4, EDN1, GABRE, GAL, GHR, GRIN2A, GZMA, LGR5, LHCGR, PPYR1, SSTR2
hsa04066HIF-1 signaling pathway-Homo sapiens (human)ANGPT2, EDN1, EGFR, IFNG, NOX1, TF
hsa05143African trypanosomiasis-Homo sapiens (human)FAS, IFNG, IL12A
hsa05410Hypertrophic cardiomyopathy (HCM)-Homo sapiens (human)CACNA2D1, EDN1, ITGAV, ITGB8, TPM1
hsa04512ECM-receptor interaction-Homo sapiens (human)COL4A5, ITGAV, ITGB8, LAMA3, LAMB3
hsa05132Salmonella infection-Homo sapiens (human)CCL4, CXCL2, CXCL3, DYNC1I1, IFNG

DEGs, differentially expressed genes.

Table II.

Key signaling pathways associated with downregulated DEGs.

Pathway IDDefinitionDEGs
hsa04640Hematopoietic cell lineage-Homo sapiens (human)CD19, CD1C, CD22, CD37, CR1, CR2, FCER2, MME, MS4A1
hsa04080Neuroactive ligand-receptor interaction-Homo sapiens (human)ADRA2A, CNR2, CTSG, EDN3, GALR2, GPR77, GRM5, HTR2A, LEP, MC1R, P2RX5, PRSS1, PRSS3, S1PR4, SCT, TAC4
hsa04662B cell receptor signaling pathway-Homo sapiens (human)CD19, CD22, CD72, CD79A, CD79B, CR2, VAV3
hsa05217Basal cell carcinoma - Homo sapiens (human)GLI1, PTCH1, TCF7, WNT16, WNT9A
hsa05340Primary immunodeficiency-Homo sapiens (human)CD19, CD40LG, CD79A, TNFRSF13C
hsa04340Hedgehog signaling pathway-Homo sapiens (human)GLI1, PTCH1, WNT16, WNT9A
hsa04064NF-kappa B signaling pathway-Homo sapiens (human)CCL19, CCL21, CD40LG, CXCL12, TNFRSF13C
hsa00920Sulfur metabolism-Homo sapiens (human)SULT2B1, SUOX
hsa04614Renin-angiotensin system-Homo sapiens (human)CTSG, MME
hsa04060Cytokine-cytokine receptor interaction-Homo sapiens (human)AMHR2, CCL17, CCL19, CCL21, CD40LG, CXCL12, LEP, TNFRSF10D, TNFRSF13C
hsa04062Chemokine signaling pathway-Homo sapiens (human)CCL17, CCL19, CCL21, CXCL12, GNG7, RASGRP2, VAV3
hsa04672Intestinal immune network for IgA production-Homo sapiens (human)CD40LG, CXCL12, TNFRSF13C
hsa05030Cocaine addiction-Homo sapiens (human)CDK5R1, DLG4, FOSB

DEGs, differentially expressed genes.

Profiles of DE circRNAs

In the circRNA microarray, 0.96% of all probes (31 circRNAs, 18 upregulated and 13 downregulated) were significantly DE between the NPC and CNP samples (Tables III and SVI). These comprised two circRNAs (upregulated); hsa_circRNA_100160 (circRNA identifier) and hsa_circRNA_100989, with >6-fold-change differences in expression. The variation of circRNAs between NPC and CNP tissues is presented in a volcano plot, scatter plot and clustering heat map (Fig. 2A-C, respectively).
Table III.

Differentially expressed circRNAs and their miRNA binding sites.

Differentially expressed circRNAsmiRNA binding sites
circRNA IDRegulationChromosomeMRE1MRE2MRE3MRE4MRE5
hsa_circRNA_100160Upchr 1hsa-miR-193b-5phsa-miR-518a-5phsa-miR-527hsa-miR-1264hsa-miR-584-3p
hsa_circRNA_100181Upchr1hsa-miR-223-5phsa-miR-148a-3phsa-miR-148b-3phsa-miR-152-3phsa-miR-146b-5p
hsa_circRNA_100386Upchr1hsa-miR-193a-5phsa-miR-149-3phsa-miR-1301-3phsa-miR-139-3phsa-miR-494-5p
hsa_circRNA_100491Upchr1hsa-miR-223-3phsa-miR-26a-5phsa-miR-26b-5phsa-miR-212-5phsa-miR-663a
hsa_circRNA_100989Upchr11hsa-miR-30b-3phsa-miR-889-5phsa-miR-766-3phsa-miR-509-3phsa-miR-608
hsa_circRNA_101252Upchr13hsa-miR-762hsa-miR-654-3phsa-miR-1301-3phsa-miR-15a-3phsa-miR-145-5p
hsa_circRNA_101728Upchr16hsa-miR-518c-5phsa-miR-514a-5phsa-miR-762hsa-miR-105-5phsa-miR-585-5p
hsa_circRNA_101965Upchr17hsa-miR-519d-5phsa-miR-489-3phsa-miR-376c-5phsa-miR-376b-5phsa-miR-298
hsa_circRNA_102539Upchr19hsa-miR-224-3phsa-miR-370-3phsa-miR-452-5phsa-miR-506-3phsa-miR-522-3p
hsa_circRNA_102682Upchr2hsa-miR-383-3phsa-miR-486-3phsa-miR-541-3phsa-miR-557hsa-miR-18a-5p
hsa_circRNA_102817Upchr2hsa-miR-331-5phsa-miR-298hsa-miR-296-3phsa-miR-30c-1-3phsa-miR-588
hsa_circRNA_103578Upchr4hsa-miR-149-5phsa-miR-650hsa-miR-548a-3phsa-miR-148b-5phsa-miR-589-3p
hsa_circRNA_103964Upchr5hsa-miR-512-3phsa-miR-215-3phsa-miR-492hsa-miR-105-5phsa-miR-200a-3p
hsa_circRNA_103965Upchr5hsa-miR-512-3phsa-miR-215-3phsa-miR-492hsa-miR-105-5phsa-miR-377-3p
hsa_circRNA_104204Upchr6hsa-miR-619-5phsa-miR-370-3phsa-miR-448hsa-miR-18b-5phsa-miR-18a-5p
hsa_circRNA_104244Upchr6hsa-miR-892bhsa-miR-149-5phsa-miR-130b-5phsa-miR-1271-3phsa-miR-432-3p
hsa_circRNA_104405Upchr7hsa-miR-122-5phsa-miR-205-5phsa-miR-136-5phsa-miR-214-3phsa-miR-138-5p
hsa_circRNA_104589Upchr8hsa-miR-511-5phsa-miR-548d-5phsa-miR-548b-5phsa-miR-548c-5phsa-miR-658
hsa_circRNA_000250Downchr18hsa-miR-181c-5phsa-miR-181b-5phsa-miR-181d-5phsa-miR-224-3phsa-miR-181a-5p
hsa_circRNA_000526Downchr10hsa-miR-92a-2-5phsa-miR-488-5phsa-miR-542-3phsa-miR-525-5phsa-miR-193b-5p
hsa_circRNA_001430Downchr5hsa-miR-92a-2-5phsa-miR-491-5phsa-miR-16-5phsa-let-7g-5phsa-miR-193a-5p
hsa_circRNA_100044Downchr1hsa-miR-629-3phsa-miR-486-3phsa-miR-134-3phsa-miR-877-3phsa-miR-377-5p
hsa_circRNA_100499Downchr1hsa-miR-504-3phsa-miR-21-5phsa-miR-337-3phsa-miR-642a-5phsa-miR-16-2-3p
hsa_circRNA_101969Downchr17hsa-miR-18a-3phsa-miR-519a-5phsa-miR-519b-5phsa-miR-519c-5phsa-miR-518e-5p
hsa_circRNA_102062Downchr17hsa-miR-18a-5phsa-miR-485-5phsa-miR-150-3phsa-miR-433-5phsa-miR-18b-5p
hsa_circRNA_102113Downchr17hsa-miR-106b-3phsa-miR-545-5phsa-miR-766-5phsa-miR-660-3phsa-miR-625-5p
hsa_circRNA_102224Downchr17hsa-miR-17-3phsa-miR-520g-3phsa-miR-422ahsa-miR-520hhsa-miR-545-3p
hsa_circRNA_102226Downchr17hsa-miR-17-3phsa-miR-802hsa-miR-495-3phsa-miR-143-5phsa-miR-516b-5p
hsa_circRNA_102535Downchr19hsa-miR-221-5phsa-miR-431-5phsa-miR-602hsa-miR-662hsa-miR-661
hsa_circRNA_103992Downchr5hsa-miR-1301-3phsa-miR-130b-5phsa-miR-204-3phsa-miR-29b-1-5phsa-miR-877-3p
hsa_circRNA_104652Downchr8hsa-miR-25-3phsa-miR-512-3phsa-miR-134-5phsa-miR-134-3phsa-let-7i-5p

circRNA, circular RNA; miRNA, micro RNA; MRE, miRNA recognition elements.

Figure 2.

Bioinformatics analysis of differentially expressed circRNAs in NPC and CNP tissues. (A) Volcano plot demonstrating the distribution of circRNAs. (B) Scatter plot exhibiting variation in circRNA expression. (C) Heatmap clustering indicating a distinguishable association between the circRNA expression patterns of certain samples. circRNAs expression levels are indicated as follows; Red, high expression and green, low expression. circRNA, circular RNA; NPC, nasopharyngeal carcinoma; CNP, chronic nasopharyngitis.

Validation of selected DE lncRNAs, mRNAs and circRNAs using RT-qPCR

Of the 12 selected DE lncRNAs (NR_027358, ENST00000566575, ENST00000560280, ENST00000411815, ENST00000565929, ENST00000457799, ENST00000514571, ENST00000440518, uc010zye.1, ENST00000443373, ENST00000513638 and ENST00000429469), only ENST00000560280 exhibited significantly opposing microarray expression patterns, which was compared with those resulting from RT-q-PCR analysis. Changes in the expression levels of the other 11 lncRNAs were also verified by RT-q-PCR (Fig. 3A). Of the 8 selected DE mRNAs (SHISA8, XAF1, RORA, MEF2C, TXLNB, MCM2, STAT1 and caspase 3), the expression levels of all except MCM2 exhibited the same trends of up- and downregulation as the microarray data, which indicated a significant difference between NPC and CNP tissues, following RT-qPCR (Fig. 3B). The changes in expression of the four selected DE circRNAs (hsa_circRNA_104204, hsa_circRNA_101252, hsa_circRNA_100160 and hsa_circRNA_001430) were consistent with the results of microarray analysis (Fig. 3C).
Figure 3.

Validation of selected (A) lncRNAs, (B) mRNAs and (C) circRNAs based on microarray and RT-qPCR data. Column heights represent the fold changes (log2 transformed) between NPC and CNP tissues, determined using RT-qPCR and microarray data. The validation results indicated a positive association between the RT-qPCR and microarray data. **P<0.01 vs. microarray. lncRNA, long non-coding RNA; circRNA, circular RNA; RT-qPCR, reverse transcription-quantitative PCR; NPC, nasopharyngeal carcinoma; CNP, chronic nasopharyngitis.

Prediction of potential targets of DE lncRNAs

Following bioinformatics and genomic coordinate analyses, the NPC-associated nearby coding genes of DE lncRNAs were listed in the relevant tables (Tables IV, V, SII, SVII and SVIII), including their association and corresponding gene accession number, gene name, protein name, gene strand, gene start location and gene end location. In summary, 420 NPC-associated nearby coding genes, corresponding with DE lncRNAs, were accurately defined and comprised 196 upregulated and 224 downregulated genes.
Table IV.

Nearby coding genes for long intergenic non-coding RNAs.

Sequence nameGene symbolGenomic locationGRNearby gene
AW833912Chr 3: 172556888-172557496DNM_018098
BF108976Chr 2: 192068946-192069452DNM_007315
BG953017RChr 4: 184736396-184736595UENST00000296741
ENST00000400353AP000569.8Chr 21: 35303517-35343487UNM_001001132
ENST00000411844KIAA0664L3Chr 16: 3171560031717339UENST00000389202
ENST00000412797RP11-70P17.1Chr 1: 25907968-25916847DNM_024037
ENST00000413991AC073257.2Chr 2: 121300484-121301902UNM_005270
ENST00000420672AC009948.5Chr 2: 179278665-179295551UNM_145739
ENST00000425214CCDC144BChr 17: 18494172-18507053UNM_016078
ENST00000431729RP11-191N8.2Chr 1: 222001007-222014008DNM_144729
ENST00000434893GUSBP11Chr 22: 23995356-24029101UNM_013378
ENST00000438082RP11-57C13.6Chr 10: 89367741-89419036DNM_004670
ENST00000439051RP11-57C13.6Chr 10: 89367768-89419036DNM_004670
ENST00000439472TTTY10Chr Y: 22669139-22680293UNM_001039567
ENST00000440357RP4-738P15.1Chr 20: 25124000-25129876DENST00000480798
ENST00000441287AC011193.1Chr 17: 32806352-32806976UNM_002982
ENST00000442583CCDC144BChr 17: 18491592-18509704UNM_016078
ENST00000449023SRGAP3-AS4Chr 3: 9298442-9299191UNM_014850
ENST00000457217RP11-222A5.1Chr 1: 175846478-175849604UNM_003285
ENST00000483245RP11-202A13.1Chr 3: 133774099-133776492DNM_001063
ENST00000486295EGFEM1PChr 3: 168538977-168547319DENST00000264674
ENST00000514571CTC-454M9.1Chr 5: 88261691-88464485UNM_001193347
ENST00000520323CTB-11I22.2Chr 5: 158654722-158672135UNM_024007
ENST00000520840RP11-875O11.3Chr 8: 22928889-22932001UENST00000312584
ENST00000536112RP11-81H14.2Chr 12: 68825634-68826434DNM_000619
ENST00000537192RP11-1038A11.3Chr 12: 5399645-5487520DNM_002527
ENST00000538430RP11-1038A11.1Chr 12: 5497754-5515817DNM_002527
ENST00000539404RP11-81H14.2Chr 12: 68726727-68797580DNM_000619
ENST00000541707RP11-81H14.2Chr 12: 68726667-68729561DNM_000619
ENST00000544591RP11-291B21.2Chr 12: 10705961-10710648UNM_007333
ENST00000544842RP11-319E16.2Chr 12: 5425126-5428513DNM_002527
ENST00000546086RP11-81H14.2Chr 12: 68727032-68835996DNM_000619
ENST00000546968RP11-44N21.1Chr 14: 105561527-105565341UNM_138790
ENST00000548846RP3-473L9.4Chr 12: 111834638-111841111DNM_001136538
ENST00000549710RP11-498M15.1Chr 12: 72102950-72104154DNM_003667
ENST00000552154RP11-554D14.7Chr 12: 108226634-108228807DENST00000342331
ENST00000556624RP11-219E7.1Chr 14: 21252046-21252452DENST00000298687
ENST00000558147LINC00277Chr 15; 69373189-69383734UENST00000310673
ENST00000558419CTD-2008A1.1Chr 15: 45118737-45119292DNM_003104
ENST00000559914LINC00277Chr 15: 69365277-69367206UENST00000310673
ENST00000561384CTD-2008A1.2Chr 15: 45119397-45176892UNM_003104
ENST00000562834RP3-523K23.2Chr 6: 54807964-54809897UNM_001010872
ENST00000563852RP11-506G7.1Chr 17: 41020507-41025481DNM_007299
ENST00000564832RP11-531A24.3Chr 8: 73859384-73862680DNM_001243237
ENST00000566575CTA-250D10.23Chr 22: 42318026-42319104DNM_001207020
ENST00000568337RP11-160C18.2Chr 15: 79021382-79026298UNM_000750
ENST00000569215RP11-609N14.1Chr 16: 10445296-10446609DNM_001134407
ENST00000569655RP11-143K11.1Chr 17: 71171621-71172772UNM_001050
ENST00000569892RP11-114H24.3Chr 15: 78246416-78255996UNM_015162
ENST00000575693LA16c-325D7.2Chr 16: 2916348-2917619UNM_024507
ENST00000577807RP11-599B13.3Chr 17: 7959542-7960939DNM_001039131
HMlincRNA791−HMlincRNA791Chr 18: 52298998-52308760UNM_001143829
HMlincRNA963+HMlincRNA963Chr 3: 168554930-168560248DENST00000264674
NR_024475LOC100216001Chr 10: 4692376-4720262UNM_001353
NR_026878FOXD2-AS1Chr 1: 47897806-47900313DENST00000337817
NR_027994NHEG1Chr 6: 137303295-137314368UNM_181310
NR_038293LOC100507173Chr 6: 27661813-27678001DENST00000331442
NR_040109LOC100505495Chr 19: 41960073-42006554UNM_006890
TCONS_00001315XLOC_000595Chr 1: 227976987-227979782DNM_003395
TCONS_00001451XLOC_000781Chr 1: 35081179-35083207UNM_005268
TCONS_00005258XLOC_002368Chr 2: 160780449-160792478UNM_001007267
TCONS_00005268XLOC_002383Chr 2: 169197716-169198115UNM_203463
TCONS_00006514XLOC_003131Chr 3: 54048256-54065456DNM_018397
TCONS_00008529XLOC_004016Chr 4: 90459366-90472707UENST00000420646
TCONS_00009933XLOC_004361Chr 5: 42922835-42924839UNM_000163
TCONS_00010742XLOC_004475Chr 5: 92906525-92909378DNM_005654
TCONS_00011633XLOC_005123Chr 6: 1489677-1490173UNM_033260
TCONS_00011758XLOC_005220Chr 6: 27677988-27680876DENST00000331442
TCONS_00012442XLOC_005214Chr 6: 26674955-26677930UNM_001732
TCONS_00012443XLOC_005214Chr 6: 26675224-26688063UNM_001732
TCONS_00014617XLOC_006712Chr 8: 11500332-11506826UNM_001715
TCONS_00014681XLOC_006779Chr 8: 39891375-39891902UNM_001464
TCONS_00017282XLOC_008100Chr X: 2484083-2488088UNM_001141919
TCONS_00017293XLOC_008116Chr X: 13405670-13437996UNM_001167890
TCONS_00018417XLOC_008704Chr 10: 4790106-4806336UNM_001353
TCONS_00021032XLOC_009637Chr 12: 7491433-7494514UNM_031491
TCONS_00021064XLOC_009662Chr 12: 10725616-10727581UNM_007333
TCONS_00029036XLOC_013955Chr 21: 44232379-44237997UNM_001001568
TCONS_00029753XLOC_014147Chr 22: 18848963-18851914UNM_017414
TCONS_00029855XLOC_014297Chr 22: 19543858-19552723DNM_001178010
uc001yfd.1BX247990Chr 14: 96181819-96223116DNM_001252507
uc002ebp.1TRIM72Chr 16: 31237192-31237830DENST00000389202
uc002iby.2LOC388387Chr 17: 41026690-41050751DNM_001158
uc002nbr.3UCA1Chr 19: 15939756-15946230DENST00000344824
uc002zbk.2BC041455Chr 21: 44019513-44035168UNM_001001568
.uc002zob.1GGT3PChr 22: 18761201-18792992DNM_017414
uc003fif.1AK127557Chr 3: 172308502-172312373DENST00000241261
uc003ihb.3BC042378Chr 4: 134114523-134115760UENST00000264360
uc003qhh.4NHEG1Chr 6: 137303295-137314368UNM_181310
uc010jbc.2FLJ42709Chr 5: 92877577-92916738UNM_005654
uc010vdm.1RRN3P2Chr 16: 29086162-29107582UNM_001178098

Chr, chromosome; GR, genome relationship; U, upstream; D, downstream.

Table V.

Nearby coding genes for enhancer long non-coding RNAs.

Sequence nameGene symbolGenomic locationGRNearby gene
ENST00000400353AP000569.8Chr 21: 35303517-35343487UNM_001001132
ENST00000411844KIAA0664L3Chr 16: 31715600-31717339UENST00000389202
ENST00000412797RP11-70P17.1Chr 1: 25907968-25916847DNM_024037
ENST00000431729RP11-191N8.2Chr 1: 222001007-222014008DNM_144729
ENST00000440357RP4-738P15.1Chr 20: 25124000-25129876DENST00000480798
ENST00000440357RP4-738P15.1Chr 20: 25124000-25129876UNM_021067
ENST00000441287AC011193.1Chr 17: 32806352-32806976UNM_002982
NR_024475LOC100216001Chr 10: 4692376-4720262UNM_001353
NR_026878FOXD2-AS1Chr 1: 47897806-47900313DENST00000337817
NR_027994NHEG1Chr 6: 137303295-137314368UNM_181310
uc003qhh.4NHEG1Chr 6: 137303295-137314368UNM_181310

Chr, chromosome; GR, genome relationship; U, upstream; D, downstream.

Prediction of potential targets within significant pathway

In total, 31 significantly enriched signaling pathways (18 associated with upregulated and 13 associated with downregulated genes) were obtained via pathway analysis. Among the 31 signaling pathways corresponding to DE mRNAs, each included a different number of DEGs associated with the pathway identifier. The most significant enrichment pathway was upregulated and included 20 targeted genes associated with ‘cytokine-cytokine receptor interaction-Homo sapiens (human)’. The least significantly enriched pathways were downregulated and only included two target genes; the target genes, SULT2B1 and SUOX, were involved in ‘Sulfur Metabolism-Homo sapiens (human)’, while the CTSG and MME genes were identified within the ‘Renin-angiotensin system-Homo sapiens (human)’. In summary, 221 potential target genes are closely associated with 31 key signaling pathways in NPC (Tables I and II).

Prediction of potential target miRNAs of DE circRNAs

All DE circRNAs were annotated in detail with their respective circRNA-miRNA interaction network information (Table SVI). A total of five miRNA response elements for each DE circRNA were predicted from the results of the circRNA microarray (Table III).

Prediction of potential targets of DE circRNAs

In combination with the lncRNA, mRNA and circRNA microarray datasets, the original results of the overlap analysis were exported (Table SIX). Upon induction, 23 DE circRNAs (16 upregulated and 7 downregulated) and their associated target genes (37 miRNAs and 50 mRNAs) were selected. A regulatory network, including 37 circRNA-miRNA interactions and 50 miRNA-mRNA interactions, was then constructed (Table VI and Fig. 4), in which 14 circRNA-miRNA-mRNA regulatory modules were identified (Fig. 5). According to the data, there is no one-to-one correspondence between circRNA and its target genes. For example, hsa_circRNA_104405 is associated with 2 target miRNAs (hsa-miR-122-5p and hsa-miR-205-5p), while hsa-miR-122 is associated with 1 target mRNA (RIMS1) and hsa-miR-205-5p is associated with 3 target mRNAs (CENPF, FRK and SCD5).
Table VI.

Regulatory network components of differentially expressed circRNAs in nasopharyngeal carcinoma.

circRNA identifierRegulationmiRNAmRNA
hsa_circRNA_100181Uphsa-miR-148a-3pGADD45A, ROBO1
hsa-miR-148b-3pGADD45A, ROBO1
hsa-miR-152-3pGADD45A, ROBO1, WDR47
hsa_circRNA_104405Uphsa-miR-122-5pRIMS1
hsa-miR-205-5pCENPF, FRK, SCD5
hsa-miR-214-3pGALNTL4
hsa_circRNA_102682Uphsa-miR-486-3pSNCA
hsa-miR-18a-5pBTG3
hsa_circRNA_102539Uphsa-miR-506-3pDLX5, E2F5, FOXQ1, FRAS1, PARP9
hsa-miR-522-3pF5, TBCE
hsa_circRNA_103578Uphsa-miR-149-5pFAM18B2
hsa-miR-650ANXA4
hsa_circRNA_101252Uphsa-miR-145-5pITGB8, MPZL1
hsa_circRNA_101965Uphsa-miR-489-3pPKP1
hsa-miR-298SAMD9
hsa_circRNA_103965Uphsa-miR-377-3pIRX3, PCDH10
hsa_circRNA_104204Uphsa-miR-448BTG3, NTF3, SPRY2, TBX3
hsa-miR-18b-5pBTG3
hsa-miR-18a-5pBTG3
hsa_circRNA_102817Uphsa-miR-298SAMD9
hsa-miR-588RIMS1
hsa_circRNA_104244Uphsa-miR-149-5pFAM18B2
hsa_circRNA_103964Uphsa-miR-200a-3pGATA6, MPPED2, RIMS1
hsa_circRNA_100989Uphsa-miR-509-3pC1orf172
hsa-miR-608FXYD7
hsa_circRNA_100160Uphsa-miR-518a-5pJAKMIP1, RAPGEF5
hsa_circRNA_100491Uphsa-miR-223-3pGALNTL4, RCN2
hsa-miR-26a-5pNFE2L3, RCN2
hsa-miR-26b-5pNFE2L3, RCN2
hsa_circRNA_104589Uphsa-miR-548d-5pBTG3, NTF3
hsa-miR-548b-5pBTG3, NTF3
hsa-miR-548c-5pBTG3, NTF3
hsa_circRNA_100044Downhsa-miR-486-3pDMKN, NR2F1
hsa_circRNA_104652Downhsa-miR-25-3pADAMTSL1, KLF2, PPP1R12C
hsa_circRNA_000526Downhsa-miR-542-3pKLHL14
hsa-miR-525-5pALPL, DCHS1, PFKFB2
hsa_circRNA_001430Downhsa-miR-16-5pC18orf34, SH3GL2
hsa_circRNA_102062Downhsa-miR-485-5pCD79A
hsa_circRNA_102224Downhsa-miR-422aADD2
hsa_circRNA_102226Downhsa-miR-802MSI1
hsa-miR-516b-5pALPL

circRNA, circular RNA; miRNA/miR, micro RNA.

Figure 4.

circRNA-miRNA-mRNA regulatory network in nasopharyngeal carcinoma. Green circle, miRNA; pink circle, circRNA; light-blue circle, mRNA. circRNA, circular RNA; miRNA, micro RNA.

Figure 5.

circRNA-miRNA-mRNA regulatory modules in nasopharyngeal carcinoma. Green rectangle, miRNA; pink rectangle, circRNA; light-blue rectangle, mRNA. circRNA, circular RNA; miRNA, micro RNA.

Discussion

NPC is a type of head and neck cancer with a high incidence and poor overall survival rate, particularly in the endemic regions of Southeast Asia (4). Although a clear understanding of its etiology is yet to be determined, NPC is widely suspected to be the result of both genetic susceptibility, exposure to certain environmental factors or Epstein-Barr virus infection (1,2). Genome-wide association and regulatory ncRNA studies may improve understanding of the etiological and essential molecular mechanisms underpinning NPC progression (14,16,17). The non-coding regions of the human genome have been closely associated with the biological processes of disease (6). Furthermore, it has been demonstrated that lncRNAs, miRNAs and circRNAs all regulate the physiological and pathological processes of numerous types of cancer, and that these regulatory ncRNAs can affect the functions of their target mRNAs (11,17–19). It has been reported that ncRNA molecules influence tumorigenesis and tumor progression by forming regulatory networks with their target genes (10), which corresponds with the multi-gene and multi-step regulation of tumor development. Certain studies have investigated the ncRNA regulatory networks that influence the occurrence and development of various types of tumor, including NPC (10,11,13,20). Therefore, in order to delineate an NPC-specific regulatory gene network containing ncRNAs in >10,000 human genes, the comprehensive identification of NPC-associated DE ncRNAs and their targets represents the initial step in establishing this network. To the best of our knowledge, the present study is the first to simultaneously screen and predict the possible target genes of DE ncRNAs (lncRNAs, miRNAs and circRNAs) using three sets of high-throughput microarray data based on transcriptome profiling of NPC tissues. The results constitute a foundation for subsequent comprehensive studies into the regulatory network behind the molecular pathogenesis of NPC. In the present study, >100 DE lncRNAs and mRNAs were identified in NPC tissues. Subsequently, the NPC-associated nearby coding genes that may represent targets of DE lncRNAs were predicted via bioinformatics analysis, and their associations between DE lncRNAs, nearby coding genes and genome coordinates were also evaluated. Pathway analysis was conducted to determine the biological function of the selected DE mRNAs in NPC pathogenesis, and to predict the essential genes regulating various NPC-associated signaling pathways. Additional target genes of DE lncRNAs were identified from 31 significantly enriched signaling pathways associated with NPC. The results suggest that aberrantly expressed lncRNAs may influence NPC development and progression through certain mechanisms, such as the interaction between a DE lncRNA and its adjacent protein-coding gene, or via interaction with its target gene in the corresponding signaling pathway. Thus, lncRNA-mRNA networks may serve an important role in the transcriptional regulation of NPC. Pathway analysis demonstrated that 31 signaling pathways were associated with DEGs, including 18 pathways associated with upregulated, and 13 associated with downregulated genes; three of these pathways (‘cytokine-cytokine receptor interaction’, ‘chemokine signaling pathway’ and ‘neuroactive ligand-receptor interaction’) were simultaneously associated with upregulated and downregulated signaling pathways. Of the 74 target genes associated with the above three pathways, 14 genes (LEP, CCL17, CCL19, CCL21, CXCL12, CCL2, CCL4, CCR8, CXCL10, CXCL2, CXCL3, CXCL6, CXCR6 and GHR) were separately involved in ≥2 of these pathways. The functions of these target genes were associated with the following biological processes: ‘Signal transduction’, ‘cell adhesion and migration’, ‘cell proliferation’, ‘inflammatory cell infiltration’, ‘angiogenesis’ and ‘immunoregulation’. Additionally, various pathways and target genes were associated with the development and progression of several other human cancer types (21–23). The present results indicate that the three aforementioned pathways, and 14 identified genes, may represent key regulators of NPC tumorigenesis. Additionally, multiple DE circRNAs associated with NPC, and their target miRNAs/mRNAs, were investigated alongside their corresponding association and genome mapping. However, thus far, no detailed reports are available on the association between circRNAs and their targets in NPC. The present study identified regulatory circRNA-miRNA-mRNA networks in NPC, which contained different modules consisting of relevant target genes. The current results indicate that aberrantly expressed circRNAs may influence different pathophysiological mechanisms of NPC via interaction with miRNAs and mRNAs, and also that the circRNA-miRNA-mRNA motifs serve a key regulatory function in NPC. Taken together, the present data indicate that lncRNAs do not serve an isolated role, but target the mRNAs of various other genes, and influence other associated genes involved in the tumorigenesis and progression of NPC, by forming regulatory networks. In the circRNA-miRNA-mRNA network, 50 mRNAs were identified as the final functional genes. According to the National Center for Biotechnology Information gene database, the functions of these target genes were associated with the following physiological and pathological mechanisms: ‘Environmental stress’, ‘cell motility and migration’, ‘cytoskeleton’, ‘antiproliferative activity’, ‘regulation of voltage-gated calcium channels’, ‘cell proliferation and apoptosis’, ‘desmosome formation’, ‘annexin’, ‘B lymphocyte antigen receptor’ ‘bimodal regulator of epidermal growth factor receptor and mitogen-activated protein kinase signaling’, ‘extracellular matrix protein’ and ‘chromosome segregation’. Notably, previous studies have revealed that certain identified target genes are associated with cancer cell proliferation and metastasis (24,25), and thus serve important roles in NPC development and progression (26–28). Overall, the present study simultaneously identified DE lncRNAs, circRNAs and mRNAs between NPC and CNP tissues via the integrated analysis of three transcriptome profiling datasets. Furthermore, potential target genes for these DE ncRNAs, and key signaling pathways associated with NPC, were identified using bioinformatics analysis. Finally, possible regulatory networks comprised of different modules in NPC were predicted and constructed. The present study serves to evaluate the association between these genes and NPC at the RNA transcriptome level. It also provides novel information to elucidate the molecular pathogenesis of NPC from a networking perspective. In further studies, the biological functions of these regulatory networks in NPC should be verified.
  2 in total

1.  TMPO-AS1 Regulates the Aggressiveness-Associated Traits of Nasopharyngeal Carcinoma Cells Through Sponging miR-320a.

Authors:  Biao Xing; Xiao-Feng Qiao; Yan-Hua Qiu; Xin Li
Journal:  Cancer Manag Res       Date:  2021-01-15       Impact factor: 3.989

2.  CircTMTC1 contributes to nasopharyngeal carcinoma progression through targeting miR-495-MET-eIF4G1 translational regulation axis.

Authors:  Yajie Zhao; Chao Li; Yan Zhang; Zhanzhan Li
Journal:  Cell Death Dis       Date:  2022-03-18       Impact factor: 9.685
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.