Literature DB >> 27350400

Identification of miRNA/mRNA-Negative Regulation Pairs in Nasopharyngeal Carcinoma.

Minglei Liu1, Kangru Zhu2, Xinmei Qian1, Wei Li1.   

Abstract

BACKGROUND Nasopharyngeal carcinoma (NPC) is a common malignancy in South-East Asia. NPC is characterized by distant metastasis and poor prognosis. The pathophysiological mechanism of nasopharyngeal carcinoma is unknown. This study aimed to identify the crucial miRNAs in nasopharyngeal carcinoma and their target genes, and to discover the potential mechanism of nasopharyngeal carcinoma development. MATERIAL AND METHODS Microarray expression profiling of miRNA and mRNA from the Gene Expression Omnibus database was downloaded, and we performed a significance analysis of differential expression. An interaction network of miRNAs and target genes was constructed. The underlying function of differentially expressed genes was predicted through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. To validate the microarray analysis data, significantly different expression levels of miRNAs and target genes were validated by quantitative real-time polymerase chain reaction. RESULTS We identified 27 differentially expressed miRNAs and 982 differentially expressed mRNAs between NPC and normal control tissues. 12 miRNAs and 547 mRNAs were up-regulated and 15 miRNAs and 435 mRNAs were down-regulated in NPC samples. We found a total of 1185 negative correlation pairs between miRNA and mRNA. Differentially expressed target genes were significantly enriched in pathways in cancer, cell cycle, and cytokine-cytokine receptor interaction signaling pathways. Significantly differentially expressed miRNAs and genes, such as hsa-miR-205, hsa-miR-18b, hsa-miR-632, hsa-miR-130a, hsa-miR-34b, PIGR, SMPD3, CD22, DTX4, and CDC6, may play essential roles in the development of nasopharyngeal carcinoma. CONCLUSIONS hsa-miR-205, hsa-miR-18b, hsa-miR-632, hsa-miR-130a, and hsa-miR-34b may be related to the development of nasopharyngeal carcinoma by regulating the genes involved in pathways in cancer and cell cycle signaling pathways.

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Year:  2016        PMID： 27350400      PMCID： PMC4928598          DOI： 10.12659/msm.896047

Source DB:  PubMed          Journal:  Med Sci Monit        ISSN： 1234-1010


Background

Nasopharyngeal carcinoma is a head and neck cancer characterized as highly malignancy and regional selection [1,2]. NPC is a rare cancer in Western countries, but it is common in Asian countries [3]. NPC frequently occurs in southern China, included Guangdong, Fujian, Hong Kong and Southeast Asia, including Malaysia, Indonesia, and Singapore. The incidence rate of NPC is 2 per 100 000 worldwide [4]. The NPC incidence in southern China is 20–50 times higher than in Western countries [5]. Radiotherapy is the main curative treatment for NPC to extend patient survival time [6,7]. NPC presents highly malignant recurrence with local tissue invasion and distant metastasis, which is the dominant reason for radiotherapy failure [8]. Currently, the major etiological factors of NPC are reported to be genetic susceptibility, environmental factors, and Epstein-Barr virus (EBV) infection. Familial and large-scale case-control studies report that HLA class I genes in the MHC locus at chromosome 6p21 are notably associated with high risk of NPC. In addition, CDK5, TEL2, CELF2, and IKKB [9-12] are also reported to be associated with NPC pathogenesis. Environmental risk factors include eating salt-preserved food [13], insufficient intake of fresh vegetables and fruits [14], alcohol consumption [15], and tobacco smoking [13,16]. Epstein-Barr virus (EBV) infection is an extensively researched etiological factor for NPC. EBV belongs to the gamma herpes virus family, persistently infects B lymphocytes in more than 90% of adults, and is related to NPC tumorigenesis [17]. In addition to the above-mentioned etiological factors of NPC, mounting evidence shows that microRNAs (miRNAs) may play essential roles in NPC tumorigenesis by regulating target genes. miRNAs are small (20–25 nucleotides) non-coding RNAs that negatively regulate expression level of target gene [18]. Numerous studies have reported that miRNAs are associated with NPC cell proliferation, migration, invasion, metastasis, and irradiation sensitivity by suppressing their target genes. miR-142-3p promotes NPC cell proliferation via suppressing SOCS6 expression [19]. miR-4649-3p inhibits NPC cell proliferation by targeting protein tyrosine phosphatase SHP-1 [20]. miR-29a/b regulates SPARC and COL3A1 gene expression to promote NPC cell migration and invasion [21]. miR-23a targets IL-8/Stat3 pathway results in radio-sensitivity in NPC [22], while miR-504 down-regulates nuclear respiratory factor 1 result in radio-resistance in NPC [23]. However, the mechanism of pathogenesis in NPC remains unclear. In this study we used bioinformatics methods to integrate miRNA and mRNA expression data, which are available in the GEO database, to identify differentially expressed miRNAs and target genes between NPC and normal control tissues, aiming to provide valuable information for use in defining the mechanism of pathogenesis in NPC.

Material and Methods

Gene expression datasets

We searched the Gene Expression Omnibus database (GEO, ) for mRNA and miRNA expression profiling of NPC, and downloaded the raw expression data. GEO is a public repository for high-throughput gene expression data [24]. We only retained the microarray studies between tumor and normal tissues. The following information was extracted from each identified study: GEO accession number, platform, number of cases and controls, time, and author.

Data processing

Different sequencing platforms and clinical samples commonly cause the heterogeneity among different microarray datasets, and make it difficult to compare microarray datasets directly. We downloaded the raw expression dataset and preprocessed it by log2 transformation and Z-score normalization.

Analysis of differentially expressed miRNA and mRNA

The miRNAs and mRNA differentially expressed between the NPC and normal control samples were identified using the limma method, which is a linear model for microarray data analysis [25]. We selected differentially expressed miRNA as false discovery rate (FDR) <0.05, and selected differentially expressed mRNA as FDR <0.001.

Identification of miRNA target genes

To obtain the target genes of miRNAs, the selected miRNAs were integrated into the miRWalk database () [26], in which the correlation between target genes and miRNAs have been confirmed. miRWalk is a comprehensive database that provides predicted and experimentally validated miRNA-target interactions for humans, mice, and rats. It combines the predicted and validated information with a comparison of binding sites resulting from 12 existing miRNA-target prediction programs [26,27]. In our study, we used 6 algorithms: DIANAmT, miRanda, miRDB, miRWalk, PICTAR, and TargetScan to predict target genes of miRNA; if more than 4 of 6 algorithms predicted the same gene of miRNA, the gene was considered as a target gene of the miRNA [26]. Reverse correlations of miRNA-target gene interacting pairs were subject to construct miRNA-RNA interaction network analysis.

miRNA-target gene network

Differentially expressed miRNA and differentially expressed target genes were used to construct the interaction network by using Cytoscape software () [28]. In the miRNA-gene network, a circular node represented the mRNA and a diamond node represented the miRNA, and their association was represented by a line.

Functional enrichment analysis

The underlying function of differentially expressed target genes was predicted by the Gene Ontology (GO) [29] function and Kyoto Encyclopedia of Genes and Genomes (KEGG) [30] pathway enrichment analysis using the DAVID tool (Database for Annotation, Visualization, and Integrated Discovery) () [31]. We set p<0.05 and FDR <0.05 as the cut-off for selecting significantly enriched functional GO terms and KEGG pathway, respectively.

Quantitative real-time polymerase chain reaction (qRT-PCR)

Total RNA of fresh frozen tissues was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. The SuperScript III Reverse Transcription Kit (Invitrogen, Carlsbad, CA, USA) was used to synthesize the cDNA according to the manufacturer’s instructions. qRT-PCR reactions were performed using Power SYBR Green PCR Master Mix [32] (Applied Biosystems, Foster City, CA) on the Applied Biosystems 7500 (Applied Biosystems, Foster City, CA). The miRcute miRNA First-Strand cDNA Kit (Tiangen, Beijing, China) and miRcute miRNA qPCR Detection Kit (Tiangen, Beijing, China) were used for miRNA expression level detection. U6 and β-actin were used as internal control for miRNA and mRNA detected. The relative expression of target genes was calculated using the 2−ΔΔCT equation. The PCR primers were used as shown in Table 1.
Table 1

Genes and primers for qRT-PCR.

miRNA/mRNAPrimer sequence (5′to3′)
SMPD3Forward-CCAACAAGTGTAACGACGATGCC
Reverse-CGCTGGACGAGGAGGTAGATTTTC
CD22Forward-ATGCCGATTCGAGAAGGAGACAC
Reverse-CCACGAGCACCAACTATTACAAGC
DTX4Forward-AGAAAGGTAAAACCCCAGAGGAAGT
Reverse-ATGGCAACCAAGCAGTAGATGTG
CDC6Forward-TTGAGCCAAGAAGGAGCACAAGATT
Reverse-CTTCCAAGAGCCCTGAAAGTGACA
PIGRForward-AGGTGCTAGACTCTGGTTTTCGG
Reverse-TCTGCTCCCATCGGCTTGA
β-actinForward-ACTTAGTTGCGTTACACCCTT
Reverse-GTCACCTTCACCGTTCCA
hsa-Mir-205Forward-TCCTTCATTCCACCGGAGTCTG
hsa-Mir-18bForward-TAAGGTGCATCTAGTGCAGTTAGAA
hsa-Mir-632Forward-GTGTCTGCTTCCTGTGGG
hsa-Mir-130aForward-CAGTGCAATGTTAAAAGGGC
hsa-Mir-34bForward-CAATCACTAACTCCACTGCCAT
U6Forward-CTCGCTTCGGCAGCACA
Reverse-AACGCTTCACGAATTTGCGT

Statistical analysis

At least 3 independent experiments were performed for statistical evaluation. qRT-PCR experimental data were expressed as means ±SD. The statistical significance was evaluated using the Student’s t-test and p<0.05 was considered as a significant difference.

Results

Differentially expressed miRNAs and mRNAs in the NPC

In this work, we collected a total of 3 mRNA expression profiles including 74 NPC and 31 normal control (NC) samples and 5 miRNA expression profiling including 402 NPC and 38 NC samples, as shown in Table 2. After normalization of the raw microarray data, significantly differentially expressed genes including 27 miRNA and 982 mRNA were identified in NPC compared to normal nasopharyngeal tissues; 27 miRNAs consisted of 12 up-regulated and 15 down-regulated miRNAs; 982 mRNA consisted of 547 up-regulated and 435 down-regulated mRNAs (Supplementary Table 1). hsa-miR-205, hsa-miR-196b, and hsa-miR-632 was the most significantly up-regulated miRNAs, while hsa-miR-130a, hsa-let-7a, and hsa-miR-34b were the most significantly down-regulated miRNAs in NPC compared with the normal control (Table 3).
Table 2

Characteristics of mRNA and miRNA expression profiling of the nasopharyngeal carcinoma.

GEO IDPlatformSamples (N:P)TimeAuthor
mRNA expression profiling
GSE53819GPL6480Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Probe Name version)18:182014Qian CN
GSE13597GPL96[HG-U133A] Affymetrix Human Genome U133A Array3:252009Wei W
GSE12452GPL570[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array10:312008Ahlquist P
miRNA expression profiling
GSE32906GPL11350 Illumina Custom Prostate Cancer DASL Panel miRNA6:162014Luo Z
GSE46172GPL16770Agilent-031181 Unrestricted_Human_miRNA_V16.0_Microarray (miRBase release 16.0 miRNA ID version)4:42013Bethony JM
GSE22587GPL8933Illumina Human Beta-version microRNA expression BeadChip4:82012YANG S
GSE32960GPL14722microRNA array18:3122012Ma J
GSE36682Human miRNA 1K6:622012Wei R

N – normal samples; P – patients’ samples.

Table 3

Significantly dysregulated miRNAs.

miRNAsp-valueFDR
Up-regulated miRNAs
 hsa-miR-2050.00E+000.00E+00
 hsa-miR-196b1.58E-051.85E-03
 hsa-miR-6328.13E-053.56E-03
 hsa-miR-18b7.37E-053.56E-03
 hsa-miR-933.81E-049.52E-03
 hsa-miR-3263.78E-049.52E-03
 hsa-miR-2103.18E-049.52E-03
 hsa-miR-376a*1.19E-032.46E-02
 hsa-miR-200c1.64E-033.02E-02
 hsa-miR-18a*2.26E-033.77E-02
 hsa-miR-542-3p2.60E-034.09E-02
 hsa-miR-9*3.47E-034.49E-02
Down-regulated miRNAs
 hsa-miR-130a2.02E-113.54E-09
 hsa-let-7a3.18E-052.78E-03
 hsa-miR-34b4.60E-053.22E-03
 hsa-let-7e7.38E-053.56E-03
 hsa-let-7d1.46E-045.67E-03
 hsa-miR-30d2.24E-047.83E-03
 hsa-miR-146a3.31E-049.52E-03
 hsa-miR-981.09E-032.46E-02
 hsa-miR-10b1.18E-032.46E-02
 hsa-miR-1381.45E-032.83E-02
 hsa-miR-311.75E-033.05E-02
 hsa-miR-3632.69E-034.09E-02
 hsa-miR-5642.90E-034.23E-02
 hsa-let-7g3.09E-034.33E-02
 hsa-miR-29a3.31E-034.45E-02

FDR – false discovery rate.

The interaction network of miRNAs and target genes

Based on the identified miRNA-target gene interaction pairs of reverse association, we compared the interaction network between miRNAs and target genes in NPC and visualized them with Cytoscape software. We used 1185 miRNA-target gene pairs of reverse correlation, including 316 pairs of up-regulated miRNA and 735 pairs of down-regulated miRNA, to construct the miRNA-target genes interaction network. The target predictions of hsa-miR-376a*and hsa-miR-18a* are not available in miRWalk databases. In this network, the significantly differentially expressed hsa-miR-632, hsa-miR-205, hsa-miR-18b, hsa-miR-34b, and hsa-miR-130a were targeted in significantly differentially expressed PIGR, CDC6, CD22, SMPD3 and DTX4, respectively, as shown in Figure 1.
Figure 1

miRNA-mRNA interaction network of NPC. (A) Down-regulation miRNA and up-regulation mRNA interaction network. The green and blue diamond nodes represent down-regulation, and red and blue circular nodes represent up-regulation. (B) Up-regulation miRNA and down-regulation mRNA interaction network. The green and blue circular nodes represent down-regulation, and the red and blue diamond nodes represent up-regulation. Circular nodes represent mRNAs and diamond nodes represent miRNAs. Solid lines indicate interaction associations between the miRNAs and mRNAs. The blue diamond nodes and blue circular nodes represent verified miRNA and mRNA through qRT-PCR.

GO classification of miRNA target genes

To obtain insights into the biological roles of differentially expressed miRNA target genes, we analyzed the predicted target gene of miRNAs using GO annotation. The threshold of GO terms was p-value<0.05. Nuclear division (GO: 0000280, p=3.05E-05) and cell cycle G2/M phase transition (GO: 0044839, p=4.02E-05) were the most significant enrichments of targets genes biological process. Intracellular organelle part (GO: 0044446, p=1.95E-05) and organelle part (GO: 0044422, p=3.62E-05) were the highest enrichments of cellular component. Catalytic activity (GO: 0003824, p=1.09E-04) and hydrolase activity (GO: 0016787, p=1.75E-04) were the highest enrichments of molecular function, as shown in Table 4.
Table 4

GO function enrichment analysis of differentially expressed miRNA target genes (top 15).

GO IDGO TermCountP-valueFDR
Biological process
GO: 0000280Nuclear division73.05E-051.48E-01
GO: 0044839Cell cycle G2/M phase transition64.02E-059.76E-02
GO: 0000086G2/M transition of mitotic cell cycle64.02E-056.50E-02
GO: 0007067Mitotic nuclear division67.71E-059.37E-02
GO: 0022617Extracellular matrix disassembly68.75E-058.50E-02
GO: 0030574Collagen catabolic process68.75E-057.08E-02
GO: 0044243Multicellular organismal catabolic process68.75E-056.07E-02
GO: 0048285Organelle fission79.46E-055.74E-02
GO: 0051301Cell division91.45E-047.83E-02
GO: 0044763Single-organism cellular process1911.87E-049.08E-02
GO: 1903047Mitotic cell cycle process122.80E-041.24E-01
GO: 0044772Mitotic cell cycle phase transition92.84E-041.15E-01
GO: 0044770Cell cycle phase transition92.84E-041.06E-01
GO: 0051726Regulation of cell cycle412.92E-041.01E-01
GO: 0032963Collagen metabolic process63.49E-041.13E-01
Cellular component
GO: 0044446Intracellular organelle part1591.95E-051.09E-02
GO: 0044422Organelle part1623.62E-051.01E-02
GO: 0005581Collagen trimer56.87E-051.28E-02
GO: 0005829Cytosol424.30E-046.01E-02
GO: 0044424Intracellular part2227.32E-048.19E-02
Molecular function
GO: 0003824Catalytic activity881.09E-041.06E-01
GO: 0016787Hydrolase activity71.75E-048.55E-02
GO: 0004000Adenosine deaminase activity25.17E-041.68E-01
GO: 0016814Hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amidines25.17E-041.26E-01

FDR – false discovery rate.

Pathway analysis of miRNA target genes

We performed the KEGG pathway enrichment analysis for differentially expressed miRNA-target genes. FDR <0.05 was used as the criteria for pathway detection. The highest enrichment of pathways in our analysis was the pathway in cancer (FDR=1.78E-11) and cell cycle (FDR=1.48E-07) (Table 5).
Table 5

KEGG pathway enrichment analysis of differentially expressed miRNA target genes (top 15).

KEGG IDKEGG termCountFDRGenes
hsa05200Pathways in cancer271.78E-11CCNE2, STAT1, KITLG, RARB, BIRC5, E2F3, EPAS1, FZD7, BMP2, NRAS, COL4A5, CDK4, NFKBIA, RAD51, WNT2, BID, FZD4, MYC, PIK3CD, ITGAV, BRCA2, ITGB1, ARNT2, ZBTB16, HDAC2, PTGS2, MMP1
hsa04110Cell cycle141.48E-07CCNE2, ESPL1, CDC14A, GADD45A, E2F3, CHEK1, CCND2, CDK4, CDC25C, CDC6, RBL2, MYC, CDC25A, HDAC2
hsa05222Small cell lung cancer119.81E-07CCNE2, RARB, E2F3, COL4A5, CDK4, NFKBIA, MYC, PIK3CD, ITGAV, ITGB1, PTGS2
hsa04060Cytokine-cytokine receptor interaction163.08E-05INHBB, IL15RA, KITLG, IL15, BMP2, CXCL3, CLCF1, TNFSF15, PRLR, CCR1, GHR, CD40LG, CCL8, TNFRSF9, TNFSF4, CCR6
hsa04662B cell receptor signaling pathway83.15E-04CD22, CD79A, NRAS, NFKBIA, LYN, PIK3CD, PPP3CA, CD19
hsa05162Measles103.87E-04CCNE2, STAT1, EIF2AK2, TNFAIP3, CCND2, CDK4, NFKBIA, PIK3CD, OAS3, ADAR
hsa04115p53 signaling pathway54.14E-04CCNE2, GADD45A, CHEK1, CCND2, CDK4
hsa04640Hematopoietic cell lineage84.18E-04CD22, KITLG, CD1D, TFRC, CD1C, CD19, MS4A1, CR1
hsa05220Chronic myeloid leukemia76.01E-04E2F3, NRAS, CDK4, NFKBIA, MYC, PIK3CD, HDAC2
hsa04630Jak-STAT signaling pathway107.27E-04IL15RA, STAT1, IL15, CCND2, CLCF1, SOCS5, PRLR, MYC, PIK3CD, GHR
hsa05140Leishmaniasis48.58E-04STAT1, NFKBIA, ITGB1, PTGS2
hsa05160Hepatitis C98.98E-04STAT1, EIF2AK2, CLDN8, NRAS, NFKBIA, TRADD, PIK3CD, MAPK14, OAS3
hsa05340Primary immunodeficiency59.13E-04CD79A, ADA, CD40LG, CD19, UNG
hsa05219Bladder cancer31.22E-03E2F3, CDK4, MYC
hsa04610Complement and coagulation cascades61.77E-03PLAUR, PROS1, C3AR1, C7, CR1, PLAU

FDR – false discovery rate.

qRT-PCR validation of differentially expressed miRNAs and target genes

To validate the microarray analysis data, the levels of significantly differentially expressed miRNAs (hsa-miR-632, hsa-miR-34b, hsa-miR-130a, hsa-miR-205, and hsa -miR-18b) and target genes (CD22, CDC6, DTX4, PIGR, and SMPD3) were quantified by qRT-PCR in 2 NPC samples and 2 normal control samples. As shown in Figure 2A–2C, the expression levels of hsa-miR-632, hsa-miR-205, and hsa-miR-18b were significantly up-regulated and the respective target genes CD22, PIGR, and SMPD3 were significantly down-regulated in NPC samples (p<0.01). The expressions of hsa-miR-34b and hsa-miR-130a were significantly down-regulated and the respective target genes CDC6 and DTX4 were significantly up-regulated in NPC samples (p<0.01), as shown in Figure 2D, 2E. In conclusion, the qRT-PCR validation of differentially expressed miRNAs and target genes between NPC and normal control samples was in accordance with results of our microarray data bioinformatics analysis.
Figure 2

miRNA and mRNA expression level in NPC and control tissues by qRT-PCR. (A) hsa-miR-632 and CD22; (B) hsa-miR-205 and PIGR; (C) hsa-miR-18b and SMPD3; (D) hsa-miR-34b and CDC6; (E) hsa-miR-130a and DTX4. Control tissues mean adjacent cancer tissues of NPC.

Discussion

miRNAs play essential roles in many fundamental biological processes, including cell proliferation, migration, invasion, and metastasis. miRNAs can function as oncogenes or oncosuppressor, depending on the targets suppressed. In our study, we found that hsa-miR-205, hsa-miR-632, hsa-miR-196b, hsa-miR-18b, and hsa-miR-93 are the top 5 up-regulated miRNAs and we found that hsa-miR-130a, hsa-let-7a, hsa-miR-34b, hsa-let-7e, and hsa-let-7d are the top 5 down-regulated miRNAs in NPC patients. hsa-miR-205 and hsa-miR-18a are significantly up-regulated, and hsa-miR-34b is significantly down-regulated in NPC biopsy tissues [33,34], which is in accordance with our bioinformatics analysis and further was validated through qRT-PCR (Table 3, Figure 2D). Moreover, hsa-miR-205 and hsa-miR-34b expression level influence the development of NPC [33]. Functions of hsa-miR-632 up-regulation and hsa-miR-130a down-regulation in NPC are not reported. Tang et al. reported that miR-205-5p has significant diagnostic value as a novel candidate biomarker in NPC. They performed a comprehensive analysis of microRNA expression patterns of 3 NPC biopsies and 3 normal nasopharyngeal epithelium specimens, then validated the differentially expressed miRNAs in 67 NPC and 25 normal tissues with qRT-PCR, finding that miR-205-5p is 1 of 5 significantly differentially expressed miRNAs in NPC [35]. In addition, miR-205 is related to radio-resistance of NPC, and miR-205 up-regulation results in radio-resistance of NPC through suppressing the PTEN pathway [36,37]. In our work, PIGR was predicted as target gene of miR-205, and this was validated in NPC tissues by qRT-PCR (Figure 2B). PIGR had significantly lower expression in NPC specimens, but was frequently expressed in non-tumor controls [38], which is consistent with our results (Figure 2B). Target CDC6 was up-regulated by hsa-miR-34b in NPC tissues, as Figure 2D shows. miR-34b/c and TP-53 polymorphisms may contribute to the risk of NPC, and gene-gene interaction of miR-34b/c rs4938723 and TP-53 Arg72-Pro increases the risk of NPC [39]. It is reported that CDC6 is associated with cancer prognosis and proliferation, while CDC6 functions in NPC are not reported. CDC6 is significantly up-regulated by miR-26a/b in lung cancer specimens compared with the adjacent normal tissues, suggesting that CDC6 is associated with poorer prognosis of lung cancer [40]. Knockdown of CDC 6 effectively inhibits proliferation of tongue squamous cell carcinoma Tca8113 cells [41]. In our work, CDC6 was enriched in cell cycle, which is the top 2 KEGG enrichment pathway of differentially expressed miRNA target genes (Table 5). hsa-miR-18b was up-regulated and consequentially caused significant down-regulation of target genes ABLIM1 and SMPD3 (NSMASE2) in NPC (Table 3, Figure 2C). SMPD3 was down-regulated by both of hsa-miR-18b and hsa-miR-632 in our study. hsa-miR-18b influences cancer cell proliferation, growth, and metastasis, while hsa-miR-18b up-regulation is mediated by loss of connective tissue growth factor through PI3K/AKT/C-Jun and C-Myc signaling to promote cell growth and cell proliferation of NPC [42]. Oxidative stress modulates NSMASE2 sub-cellular localization in plasma membranes to generate ceramide and induces apoptosis of lung carcinoma A549 cell line [43]. NSMASE2 regulates exosomal miRNA secretion and promotes angiogenesis within the tumor microenvironment, as well as in metastasis [44]. Deltex 4, E3 ubiquitin ligase is the official name of DTX4; it functions as a negative regulator of Notch signaling [45], which is crucial for the T-cell development in early stages and angiogenesis during carcinogenesis [46,47]. Dysregulation of Notch and Wnt promotes cell differentiation and tumorigenesis. In our study, DTX4 was up-regulated by hsa-miR-130a, hsa-miR-7a, hsa-miR-7g, hsa-miR-7d, and hsa-miR-7e in NPC patients. CD22 was down-regulated by both of hsa-miR-632 and hsa-miR-210. CD22 was enriched in B cell receptor and hematopoietic cell lineage signaling pathway via KEGG pathway analysis (Table 5). It is reported that pan-B lymphocytes have scant peri-tumoral areas and are absent in 29 out of 50 NPC biopsies through using immunohistological detection [48]. hsa-miR-632 is reportedly associated with breast cancer tumorigenesis, and high expression level of hsa-miR-632 down-regulates DNAJB6, leading to significantly increased invasive and metastatic ability of breast cancer cells compared to mammary epithelial cells [49]. Pathway in cancer was identified as the most significantly enriched pathway in NPC (Table 5). Pathway in cancer is related to pathogenesis of various cancer types, such as colorectal, pancreatic, thyroid, and lung cancer [50-54], indicating that pathways in cancer may play an important role in NPC pathogenesis. There are limitations in our work. We have constructed the regulatory network of miRNAs and mRNA inverse correlations pairs, and the pathogenesis of key miRNAs and mRNAs in NPC need to be elucidated through in vivo and in vitro experiments.

Conclusions

We identified 27 differentially expressed miRNAs and 982 differentially expressed mRNAs between NPC and normal tissues. We used 1185 miRNA-target gene pairs of inverse correlations to construct an interaction network. In this network, we found several miRNAs and genes that may play important roles in NPC, such as hsa-miR-205, hsa-miR-34b, hsa-miR-18b, hsa-miR-632, hsa-miR-130a, PIGR, CDC6, CD22, SMPD3, and DTX4. The pathway in cancer may be involved in the pathogenesis mechanism of NPC. Our findings may provide an important contribution to further elucidate the pathogenesis mechanisms of NPC. Full list of differentially expressed mRNA in nasopharyngeal carcinoma. FDR: false discovery rate
Supplementary Table 1

Full list of differentially expressed mRNA in nasopharyngeal carcinoma.

GenesFDRUp/down regulation
LMNB21.00E−11Up
HDGFRP32.32E−11Up
FJX12.89E−11Up
RBBP84.91E−11Up
LHX25.18E−11Up
TNFAIP68.16E−11Up
ECT21.02E−10Up
C12orf481.74E−10Up
CHAF1B2.93E−10Up
VRK24.51E−10Up
NFE2L38.20E−10Up
TFRC1.08E−09Up
NPL1.08E−09Up
FAM64A1.42E−09Up
GALNT112.59E−09Up
TNFSF42.98E−09Up
GPSM23.06E−09Up
MAD2L14.03E−09Up
GAD14.59E−09Up
CDC65.29E−09Up
PFDN45.31E−09Up
ATF56.91E−09Up
MRPL427.85E−09Up
ZWILCH8.93E−09Up
FOXM19.23E−09Up
RAN1.00E−08Up
OLA11.31E−08Up
OIP51.99E−08Up
PSRC12.17E−08Up
FAP2.17E−08Up
MINPP12.86E−08Up
SEC61A23.30E−08Up
CCNF3.30E−08Up
C12orf54.54E−08Up
ARNT24.54E−08Up
RIF14.86E−08Up
RCN25.33E−08Up
DTX46.03E−08Up
RAD54B7.31E−08Up
CLASP17.31E−08Up
KIF147.46E−08Up
GNPDA17.46E−08Up
EXO17.46E−08Up
PMAIP18.23E−08Up
KCTD31.10E−07Up
GRB101.31E−07Up
GINS31.63E−07Up
PTGS21.69E−07Up
PUS71.82E−07Up
XPOT2.00E−07Up
PLA2G32.04E−07Up
PALB22.20E−07Up
PRMT32.36E−07Up
SAC3D12.45E−07Up
VCAN2.52E−07Up
FGF12.78E−07Up
ESM12.78E−07Up
C12orf112.86E−07Up
P4HA13.59E−07Up
PBK3.83E−07Up
RNASEH2A3.91E−07Up
DOCK43.91E−07Up
CNIH43.91E−07Up
NUAK13.91E−07Up
AHCY3.99E−07Up
TBCE4.26E−07Up
UBE2S4.33E−07Up
COL5A14.39E−07Up
NOX44.49E−07Up
GAPDH4.52E−07Up
EIF4E24.52E−07Up
DTL4.52E−07Up
STK34.94E−07Up
PSMD144.94E−07Up
DSG24.94E−07Up
CENPF5.08E−07Up
PTTG3P5.15E−07Up
FANCL5.16E−07Up
PPIF5.26E−07Up
PSMA45.52E−07Up
HDAC26.06E−07Up
MCM46.38E−07Up
CKS1B6.38E−07Up
UCK26.89E−07Up
EIF2S26.90E−07Up
TIPIN7.68E−07Up
FSCN17.68E−07Up
HSPE17.86E−07Up
MMP128.05E−07Up
TMEM194A8.19E−07Up
KIF18A8.22E−07Up
INSM18.24E−07Up
RAI148.77E−07Up
UNG9.44E−07Up
PIK3CB1.12E−06Up
TRIP61.15E−06Up
MTX21.21E−06Up
CEP1521.27E−06Up
STAR1.30E−06Up
PLAU1.37E−06Up
HOMER31.38E−06Up
ME31.39E−06Up
RIC8B1.40E−06Up
ZFP641.56E−06Up
NUP1551.58E−06Up
TOMM401.65E−06Up
PGAP11.67E−06Up
ITGAV1.88E−06Up
KIF4A1.90E−06Up
SRD5A12.03E−06Up
ZNF1242.17E−06Up
RAD51AP12.17E−06Up
PTTG12.26E−06Up
BRCA22.31E−06Up
GGCT2.35E−06Up
ANXA42.41E−06Up
ADA2.74E−06Up
TMEM14A2.77E−06Up
PAK1IP13.08E−06Up
TMEM333.10E−06Up
HSPD13.11E−06Up
GORASP13.11E−06Up
USP183.12E−06Up
MARK13.14E−06Up
SLC39A63.17E−06Up
ZNF5623.39E−06Up
CDC25C3.48E−06Up
RPE3.50E−06Up
POLQ3.50E−06Up
SNRPF3.79E−06Up
SSX2IP3.81E−06Up
STAP23.84E−06Up
CDC453.84E−06Up
POMGNT13.86E−06Up
BID3.88E−06Up
STC24.00E−06Up
BRIP14.00E−06Up
TM7SF34.21E−06Up
DUSP104.50E−06Up
PSMG14.62E−06Up
CCL44.74E−06Up
MED214.88E−06Up
NOV5.14E−06Up
SUMO15.21E−06Up
CPOX5.30E−06Up
COL7A15.30E−06Up
CAT5.39E−06Up
STIL5.84E−06Up
CCT25.93E−06Up
DNM1L5.95E−06Up
SLC25A136.29E−06Up
IMPACT6.52E−06Up
CACYBP6.61E−06Up
RGS46.63E−06Up
PHF147.10E−06Up
ILF27.10E−06Up
ENO17.13E−06Up
PAIP17.30E−06Up
TNFSF157.34E−06Up
EPHB47.34E−06Up
ATP6V1B27.34E−06Up
OAS37.83E−06Up
GSTO17.93E−06Up
TPI17.99E−06Up
IL15RA8.23E−06Up
HOXA78.40E−06Up
SETD28.66E−06Up
C1QB8.95E−06Up
IFRD19.15E−06Up
NT5M9.92E−06Up
PITPNB1.01E−05Up
CENPN1.02E−05Up
GPR137B1.03E−05Up
TK11.05E−05Up
TMEFF11.09E−05Up
SULF11.12E−05Up
ERCC6L1.13E−05Up
DENR1.21E−05Up
C5orf131.21E−05Up
ATP2C11.22E−05Up
STAT11.27E−05Up
TTK1.30E−05Up
SS18L11.30E−05Up
GABPB11.30E−05Up
BIRC51.33E−05Up
WNT21.38E−05Up
HRSP121.42E−05Up
SNRPG1.46E−05Up
PFDN21.48E−05Up
POLR3D1.54E−05Up
GJB11.57E−05Up
POGLUT11.58E−05Up
TNIP31.63E−05Up
PSMB31.64E−05Up
YWHAQ1.69E−05Up
NCBP21.80E−05Up
PLD31.80E−05Up
TWSG11.87E−05Up
SKP22.00E−05Up
YWHAH2.03E−05Up
CASP62.08E−05Up
IRF62.10E−05Up
CKAP42.12E−05Up
SLC5A62.20E−05Up
GPI2.27E−05Up
C1orf1122.29E−05Up
MYC2.32E−05Up
NIT22.40E−05Up
LGR42.49E−05Up
HOXC62.53E−05Up
FPR32.53E−05Up
GLT25D12.56E−05Up
RACGAP12.62E−05Up
MIF2.65E−05Up
CXCL32.69E−05Up
CTPS2.73E−05Up
FGD62.75E−05Up
FNDC3B2.76E−05Up
LHFPL22.81E−05Up
CDC25A2.90E−05Up
CD702.97E−05Up
TOMM70A3.03E−05Up
COPS23.12E−05Up
CCND23.12E−05Up
MARCO3.17E−05Up
WDR413.40E−05Up
TAF53.40E−05Up
AP3M23.49E−05Up
TMEM39A3.55E−05Up
TMEM185B3.55E−05Up
PLK43.64E−05Up
COL10A13.64E−05Up
C11orf413.68E−05Up
KITLG3.70E−05Up
PLOD13.79E−05Up
UBFD13.79E−05Up
NFKBIA3.79E−05Up
CORO1C3.85E−05Up
MORC43.95E−05Up
HOXA103.98E−05Up
MRPL134.11E−05Up
GINS14.17E−05Up
ZFP1124.20E−05Up
EXT14.23E−05Up
KIF13A4.25E−05Up
TMEM974.31E−05Up
PIK3CD4.44E−05Up
IFIT34.59E−05Up
ETV74.76E−05Up
NCAPD34.77E−05Up
ANGPT24.83E−05Up
SNRPD15.10E−05Up
NDUFA95.24E−05Up
TMPO5.33E−05Up
CD145.38E−05Up
CDK55.44E−05Up
DKC15.55E−05Up
ASB95.76E−05Up
TRMT61B5.82E−05Up
C1QBP5.91E−05Up
WRN5.97E−05Up
TMEM106C5.97E−05Up
SYNE16.22E−05Up
ICAM16.27E−05Up
FAM60A6.41E−05Up
SRSF96.72E−05Up
PRR5L6.72E−05Up
MLF1IP6.78E−05Up
GNL26.80E−05Up
PSMB26.94E−05Up
ASPN7.03E−05Up
YRDC7.17E−05Up
KPNA27.25E−05Up
TYROBP7.30E−05Up
EXOSC57.33E−05Up
TNFRSF97.34E−05Up
GAS87.37E−05Up
TBCA7.38E−05Up
FAM162A7.38E−05Up
MAPKAPK57.41E−05Up
POSTN7.82E−05Up
SSTR27.89E−05Up
SUPV3L18.01E−05Up
RSAD28.05E−05Up
RFC28.05E−05Up
H2AFZ8.05E−05Up
UBE2V28.11E−05Up
MFHAS18.29E−05Up
SYNCRIP8.60E−05Up
C17orf758.72E−05Up
WDR478.75E−05Up
FASTKD38.81E−05Up
NAA508.85E−05Up
ITGB18.94E−05Up
CAND19.04E−05Up
PTPN129.22E−05Up
MAT2A9.40E−05Up
DERA9.71E−05Up
KIAA01469.90E−05Up
EPCAM9.97E−05Up
C12orf291.05E−04Up
PYCARD1.06E−04Up
MDK1.07E−04Up
BMP31.07E−04Up
RAB171.08E−04Up
PLAUR1.08E−04Up
FASTKD21.09E−04Up
AIMP21.12E−04Up
WDR121.13E−04Up
ABCE11.13E−04Up
SERPINH11.15E−04Up
DEPDC11.16E−04Up
CYC11.18E−04Up
ITGB61.22E−04Up
PSMA71.23E−04Up
ZNF741.24E−04Up
SLMO21.24E−04Up
SCO21.24E−04Up
DMXL21.26E−04Up
BST21.26E−04Up
XPO11.27E−04Up
ENOPH11.27E−04Up
SCG51.28E−04Up
PAICS1.29E−04Up
WASF11.31E−04Up
BCL2A11.32E−04Up
LGALS11.32E−04Up
RXRB1.32E−04Up
RRM11.34E−04Up
PXDN1.37E−04Up
PTRH21.38E−04Up
JMJD41.38E−04Up
MBD51.39E−04Up
RNF19B1.40E−04Up
PRC11.43E−04Up
PRDM41.46E−04Up
NPM31.48E−04Up
UBE2Z1.48E−04Up
KIF231.53E−04Up
RRN31.55E−04Up
ZNF5321.58E−04Up
PIGA1.58E−04Up
WBP51.59E−04Up
PIGN1.59E−04Up
IL13RA21.60E−04Up
GTF3C31.60E−04Up
ARMC11.63E−04Up
NOL101.64E−04Up
COL4A51.65E−04Up
GADD45A1.65E−04Up
FOXK21.71E−04Up
CCT31.71E−04Up
IDH11.72E−04Up
ADIPOR21.72E−04Up
PSMC41.72E−04Up
FCGR1B1.72E−04Up
ROBO11.72E−04Up
CST11.74E−04Up
MEST1.78E−04Up
CLSTN21.79E−04Up
UPF3B1.81E−04Up
URB21.81E−04Up
TPT11.81E−04Up
FAH1.81E−04Up
B4GALT21.82E−04Up
PPIA1.82E−04Up
NME11.89E−04Up
AQP91.95E−04Up
UQCRH1.96E−04Up
UBE2C1.96E−04Up
MRPS351.97E−04Up
ADAM91.97E−04Up
DKK32.00E−04Up
RBL22.00E−04Up
DDX602.07E−04Up
ZBTB392.09E−04Up
MCTP22.13E−04Up
LOC1004991772.13E−04Up
SCMH12.15E−04Up
CUEDC22.17E−04Up
ZNF1402.17E−04Up
TAP12.18E−04Up
E2F62.20E−04Up
PA2G42.26E−04Up
BMP22.29E−04Up
DIABLO2.35E−04Up
PSMD12.37E−04Up
CKAP52.41E−04Up
PRLR2.43E−04Up
ERO1LB2.44E−04Up
SPIN12.45E−04Up
PRNP2.45E−04Up
PIWIL12.45E−04Up
TLN22.47E−04Up
SRP92.49E−04Up
SOCS52.52E−04Up
CCR12.55E−04Up
CEP762.58E−04Up
SCARB22.60E−04Up
MRPL122.65E−04Up
ISYNA12.66E−04Up
RPL322.70E−04Up
PKD22.70E−04Up
KAT2B2.78E−04Up
TRIB12.80E−04Up
KDSR2.84E−04Up
IFI44L2.84E−04Up
UGCG2.86E−04Up
POLR3G2.89E−04Up
THBS22.97E−04Up
IGSF62.97E−04Up
STRAP3.12E−04Up
EIF2AK23.14E−04Up
ADAM123.14E−04Up
SNX273.15E−04Up
KREMEN23.15E−04Up
CEP1923.15E−04Up
PPP6C3.21E−04Up
LOC7301013.23E−04Up
PSMA63.25E−04Up
TGFBI3.25E−04Up
PSTPIP23.25E−04Up
CCL83.28E−04Up
RPS293.29E−04Up
PLA2G73.30E−04Up
PNO13.32E−04Up
RARB3.45E−04Up
CLIP23.45E−04Up
LAMP23.45E−04Up
CCT73.45E−04Up
ABTB23.45E−04Up
ABCA33.47E−04Up
NCK13.53E−04Up
CDK83.59E−04Up
WSB23.62E−04Up
DNMT3B3.63E−04Up
EVL3.69E−04Up
PTPLAD13.74E−04Up
GLA3.81E−04Up
ADH53.88E−04Up
CDK43.94E−04Up
UMPS3.99E−04Up
UCP23.99E−04Up
PTGES34.01E−04Up
LOC1001279724.01E−04Up
FZD74.01E−04Up
TUBGCP44.04E−04Up
PPA14.07E−04Up
ARPC1A4.08E−04Up
PDSS14.12E−04Up
IFIT14.17E−04Up
INPP14.17E−04Up
KCNE14.22E−04Up
PXMP24.24E−04Up
IL154.28E−04Up
PCSK74.33E−04Up
PRPF40A4.34E−04Up
MLLT114.34E−04Up
MKKS4.52E−04Up
NIPSNAP14.57E−04Up
TSR24.59E−04Up
LDHA4.62E−04Up
CHCHD34.76E−04Up
NCAPH4.82E−04Up
R3HDM14.98E−04Up
SPAST4.98E−04Up
PDHX5.01E−04Up
C2orf475.01E−04Up
CBX15.02E−04Up
PACS25.10E−04Up
C3AR15.23E−04Up
ANAPC105.24E−04Up
CCNJ5.25E−04Up
TARS5.41E−04Up
ATP5G35.49E−04Up
HSPA135.52E−04Up
TCF125.54E−04Up
EIF1AX5.59E−04Up
CBX55.75E−04Up
YBX15.79E−04Up
CTSL15.82E−04Up
RFWD35.83E−04Up
ZNF4735.84E−04Up
PDCD115.85E−04Up
TGIF25.86E−04Up
GSTK15.90E−04Up
GPR1435.94E−04Up
NMD35.96E−04Up
JAG25.97E−04Up
DIAPH15.99E−04Up
GJA16.03E−04Up
KCNMB26.13E−04Up
ACOT76.15E−04Up
TMEM666.15E−04Up
REPIN16.18E−04Up
TRAP16.28E−04Up
SPDEF6.32E−04Up
YTHDF36.46E−04Up
TNFAIP36.63E−04Up
SNRPE6.63E−04Up
WDYHV16.64E−04Up
FNBP1L6.64E−04Up
C6orf2116.65E−04Up
ATP5B6.65E−04Up
SEH1L6.81E−04Up
EFNB26.81E−04Up
C1orf1096.81E−04Up
SOX126.84E−04Up
SCAI7.20E−04Up
ISG157.29E−04Up
RTF17.30E−04Up
ANKRD57.35E−04Up
AURKB7.40E−04Up
DARS7.52E−04Up
SUMO27.58E−04Up
WDR17.68E−04Up
LRP47.68E−04Up
MYO1B7.73E−04Up
MCM107.73E−04Up
BYSL7.73E−04Up
MMP17.81E−04Up
CCNE27.91E−04Up
ADAR7.96E−04Up
MTCH27.97E−04Up
CHEK18.17E−04Up
XAF18.22E−04Up
LRP128.42E−04Up
PAFAH1B38.43E−04Up
STAMBP8.44E−04Up
NRAS8.54E−04Up
GHR8.54E−04Up
ATP5J28.68E−04Up
HMGCR8.75E−04Up
SENP28.78E−04Up
SLC35E39.08E−04Up
ORC19.19E−04Up
C5orf309.42E−04Up
GSTM29.49E−04Up
RSRC19.61E−04Up
DEGS19.70E−04Up
RPS6KC19.88E−04Up
RFC39.89E−04Up
ESPL19.93E−04Up
FKBP1A9.96E−04Up
FBXO119.98E−04Up
EHBP19.98E−04Up
COL1A19.98E−04Up
DNALI10.00E+00Down
ALDH1L11.36E−12Down
SPAG84.00E−11Down
SCRN14.64E−11Down
FOXJ14.64E−11Down
CLU4.64E−11Down
KLF21.91E−10Down
SCGB1A12.34E−10Down
SCGB2A12.93E−10Down
VPS116.74E−10Down
SYBU8.20E−10Down
PDCD59.56E−10Down
GPR1622.24E−09Down
STIM12.40E−09Down
SLC44A43.44E−09Down
PCYT23.97E−09Down
EPAS13.97E−09Down
ABLIM14.29E−09Down
MT34.59E−09Down
ATP2C25.67E−09Down
CRYL17.85E−09Down
C6orf977.93E−09Down
PROM18.64E−09Down
BLK8.81E−09Down
B3GALT41.31E−08Down
CD221.88E−08Down
MSLN1.99E−08Down
CIRBP2.02E−08Down
WDR782.08E−08Down
MSRA2.08E−08Down
PTGDS2.47E−08Down
SLC16A73.25E−08Down
SMPD33.41E−08Down
CD1C3.97E−08Down
VIPR14.54E−08Down
GNMT6.09E−08Down
TCTA7.46E−08Down
C10orf811.06E−07Down
PIGR1.23E−07Down
IGHD1.23E−07Down
FAM107A1.23E−07Down
ASL1.26E−07Down
NBEA1.28E−07Down
AQP51.61E−07Down
SGSM31.66E−07Down
MSMB1.76E−07Down
IK1.78E−07Down
PDLIM22.21E−07Down
DDAH22.22E−07Down
FBXO222.29E−07Down
PIK3C2B2.77E−07Down
VPREB33.67E−07Down
PDCD6IP4.01E−07Down
TREML24.33E−07Down
FCRL24.33E−07Down
FAM174B4.33E−07Down
DPEP24.52E−07Down
CLMN4.87E−07Down
RNASE45.08E−07Down
PPP3CA5.08E−07Down
GNG75.18E−07Down
AGR25.39E−07Down
GLTSCR25.44E−07Down
GALNT125.54E−07Down
ABHD14A6.19E−07Down
KLHDC26.55E−07Down
C6orf1037.80E−07Down
CD727.96E−07Down
AK18.20E−07Down
LXN8.22E−07Down
FAM102A8.56E−07Down
CR18.77E−07Down
CD1D8.77E−07Down
MEIS3P18.84E−07Down
LMO21.01E−06Down
CYP2F11.04E−06Down
BAIAP31.05E−06Down
RPS6KA31.21E−06Down
WFDC21.24E−06Down
CD191.35E−06Down
BACE21.40E−06Down
DALRD31.41E−06Down
SIDT21.44E−06Down
PIP1.61E−06Down
ABCB11.88E−06Down
FAM65B1.90E−06Down
RRAGA1.92E−06Down
AGBL22.06E−06Down
C5orf452.19E−06Down
E2F32.55E−06Down
SFMBT12.56E−06Down
TUBA4A2.58E−06Down
BANK12.58E−06Down
TNFRSF13B2.64E−06Down
HMGCL2.67E−06Down
TTC122.84E−06Down
GCNT32.87E−06Down
VTCN12.88E−06Down
C10orf1163.00E−06Down
MFNG3.01E−06Down
CD1803.10E−06Down
AHCTF13.12E−06Down
TFF33.13E−06Down
IQSEC13.17E−06Down
POU2AF13.24E−06Down
ENPP43.24E−06Down
BEND53.51E−06Down
CLDN103.59E−06Down
CD40LG3.79E−06Down
ANG4.04E−06Down
GPR1104.04E−06Down
SERHL24.17E−06Down
ACAA14.21E−06Down
KAT54.62E−06Down
TBC1D22A4.74E−06Down
FZD45.30E−06Down
ATP1A25.30E−06Down
KCNJ165.30E−06Down
HSPB85.99E−06Down
CCDC696.02E−06Down
CYB561D26.59E−06Down
TINF27.58E−06Down
ST6GAL17.74E−06Down
QARS7.89E−06Down
SELENBP19.03E−06Down
CRY29.09E−06Down
DUSP269.93E−06Down
MPG1.01E−05Down
COX7A11.02E−05Down
SWAP701.05E−05Down
MS4A11.05E−05Down
IFT881.09E−05Down
DND11.12E−05Down
KCNQ11.16E−05Down
CH25H1.21E−05Down
LYL11.22E−05Down
SRPX1.24E−05Down
NME51.30E−05Down
CEBPG1.30E−05Down
PLA2G161.32E−05Down
ZNF3951.41E−05Down
PLEKHB11.47E−05Down
C11orf711.49E−05Down
RAD511.54E−05Down
BEX41.63E−05Down
C31.71E−05Down
SMPD11.75E−05Down
CLCF11.76E−05Down
GPRIN21.77E−05Down
SGSM21.79E−05Down
CAND22.00E−05Down
WDHD12.10E−05Down
ARMCX62.10E−05Down
FGD22.18E−05Down
FMO22.21E−05Down
C62.35E−05Down
LMBRD12.53E−05Down
AK22.53E−05Down
ABCA72.71E−05Down
NXF22.75E−05Down
GABRP2.82E−05Down
DNAH62.84E−05Down
CTDSP12.86E−05Down
PLAC82.97E−05Down
BCAS43.03E−05Down
BAALC3.03E−05Down
GTF2F23.05E−05Down
VILL3.12E−05Down
POLD43.17E−05Down
ITIH53.17E−05Down
DAZAP23.17E−05Down
SIGIRR3.22E−05Down
EPHB63.30E−05Down
MEIS23.35E−05Down
ICAM33.35E−05Down
CCR63.35E−05Down
PSD43.49E−05Down
EFCAB13.64E−05Down
TGFBR33.78E−05Down
TMEM184B3.84E−05Down
SPATA63.85E−05Down
CRLF13.95E−05Down
CLDN84.17E−05Down
ADCY24.30E−05Down
PIGH4.32E−05Down
TFF14.33E−05Down
TSPAN14.55E−05Down
SIDT14.63E−05Down
PHF14.65E−05Down
ARHGAP444.76E−05Down
TMEM1214.97E−05Down
CRIP24.99E−05Down
APOM5.07E−05Down
FUCA15.18E−05Down
NEIL15.19E−05Down
LPAR15.20E−05Down
PSMA25.25E−05Down
LRIG15.32E−05Down
SRSF55.44E−05Down
ZFP1065.54E−05Down
CRIP15.64E−05Down
MAST35.82E−05Down
DHX385.82E−05Down
PRKCB6.01E−05Down
MRPL196.11E−05Down
ABCD46.37E−05Down
HSD17B86.72E−05Down
GPR186.72E−05Down
DNAH36.72E−05Down
ADAMTSL36.72E−05Down
INHBB6.77E−05Down
NCR36.79E−05Down
CLDN36.79E−05Down
RNASE16.80E−05Down
CCNA16.86E−05Down
GOLGA8A7.00E−05Down
PDZD27.03E−05Down
CCDC197.03E−05Down
EIF1B7.25E−05Down
DLEC17.32E−05Down
EIF3J7.41E−05Down
S1PR47.82E−05Down
ACTR107.96E−05Down
CHL17.99E−05Down
PPRC18.05E−05Down
TSPAN328.06E−05Down
LRRC238.06E−05Down
CGRRF18.29E−05Down
EFCAB68.32E−05Down
LAMB28.40E−05Down
CAP28.51E−05Down
DUSP228.73E−05Down
ADAM288.76E−05Down
LMAN18.77E−05Down
TDG8.81E−05Down
AES9.04E−05Down
VNN29.22E−05Down
NUCB29.22E−05Down
TIMP49.25E−05Down
TLE29.39E−05Down
PROS19.40E−05Down
EPHX19.74E−05Down
PGK19.76E−05Down
CLCN49.78E−05Down
YPEL59.90E−05Down
ERCC11.01E−04Down
TMC61.01E−04Down
RASGRP21.05E−04Down
SERPINB71.05E−04Down
ZFYVE211.05E−04Down
GAK1.06E−04Down
GFOD11.07E−04Down
ARHGAP11A1.07E−04Down
MAGIX1.08E−04Down
SPAG61.16E−04Down
LRMP1.17E−04Down
FLII1.18E−04Down
SNED11.21E−04Down
PPOX1.24E−04Down
UBA71.24E−04Down
BASP11.24E−04Down
IGJ1.27E−04Down
C5orf41.32E−04Down
CTNS1.34E−04Down
TEX2641.35E−04Down
ABHD61.36E−04Down
TIMM131.39E−04Down
CCDC28A1.40E−04Down
P4HTM1.40E−04Down
IFIH11.43E−04Down
HIP1R1.44E−04Down
STS1.46E−04Down
ATP7B1.46E−04Down
STAG31.54E−04Down
ZNF137P1.56E−04Down
ZNF5281.57E−04Down
TMEM9B1.57E−04Down
PACRG1.57E−04Down
DEPDC51.63E−04Down
CCDC811.65E−04Down
PRIM21.70E−04Down
RPS6KA11.72E−04Down
C14orf11.73E−04Down
CDT11.74E−04Down
NLRP11.76E−04Down
LPPR31.77E−04Down
ANKHD11.77E−04Down
CD521.79E−04Down
TCL1A1.79E−04Down
ERCC51.79E−04Down
RNASET21.81E−04Down
KRT71.81E−04Down
FAM184A1.81E−04Down
TMEM132A1.81E−04Down
C11orf161.82E−04Down
CAMTA11.82E−04Down
HDC1.93E−04Down
NIPSNAP3B1.96E−04Down
LIMD21.98E−04Down
BAP12.02E−04Down
ZNF8212.06E−04Down
ZBBX2.09E−04Down
ZCWPW12.18E−04Down
TNNC22.18E−04Down
CALM12.22E−04Down
MAPK142.23E−04Down
SEL1L32.26E−04Down
VTI1B2.28E−04Down
P2RX52.28E−04Down
CHAF1A2.29E−04Down
SGTA2.30E−04Down
MNS12.35E−04Down
FYCO12.35E−04Down
IL332.35E−04Down
SCUBE22.38E−04Down
TRIM242.41E−04Down
HSD17B22.42E−04Down
RASAL12.42E−04Down
ANXA112.44E−04Down
B3GNT32.44E−04Down
GLT8D12.45E−04Down
BCAR32.45E−04Down
ZBTB162.47E−04Down
ADRA2A2.49E−04Down
USP192.51E−04Down
RARRES22.51E−04Down
SLC46A32.60E−04Down
MAGOHB2.62E−04Down
SIRT32.63E−04Down
STAP12.64E−04Down
INPP4B2.66E−04Down
PEPD2.66E−04Down
JHDM1D2.80E−04Down
COBL2.80E−04Down
ITIH42.81E−04Down
DOCK32.84E−04Down
ADH1C2.86E−04Down
C11orf22.87E−04Down
RHOBTB23.00E−04Down
SNW13.01E−04Down
CD79A3.02E−04Down
TMC53.06E−04Down
ALDH3B13.14E−04Down
CDC14A3.22E−04Down
SNTA13.23E−04Down
LYN3.32E−04Down
LTF3.33E−04Down
MLYCD3.41E−04Down
DNAI13.45E−04Down
C1orf1153.45E−04Down
CBX63.46E−04Down
KCNJ123.63E−04Down
ZBTB253.65E−04Down
UNC93B13.69E−04Down
C7orf443.72E−04Down
KIAA01253.80E−04Down
PARP33.94E−04Down
LOC2842443.99E−04Down
FOLR13.99E−04Down
PPFIA44.13E−04Down
PKIG4.15E−04Down
MOAP14.17E−04Down
C17orf594.24E−04Down
EFHC14.26E−04Down
CRISP24.26E−04Down
ANK24.29E−04Down
RHOH4.40E−04Down
APOD4.45E−04Down
TFEB4.49E−04Down
PAIP2B4.49E−04Down
CREBZF4.52E−04Down
OCEL14.64E−04Down
SNX34.72E−04Down
TFB2M4.82E−04Down
ALDH6A14.90E−04Down
RPGRIP14.93E−04Down
PNMA15.02E−04Down
TCEB15.18E−04Down
ATP5L5.25E−04Down
LOC7288555.28E−04Down
RPL36AL5.52E−04Down
XRCC45.69E−04Down
TMEM63A5.74E−04Down
C9orf95.92E−04Down
SLC15A26.00E−04Down
FAIM36.20E−04Down
VAV16.20E−04Down
CHKB6.24E−04Down
CP6.41E−04Down
PAF16.53E−04Down
MAGED26.58E−04Down
RRAD6.58E−04Down
TXNIP6.64E−04Down
ZFP1616.79E−04Down
DIO16.79E−04Down
PTGER26.81E−04Down
SATB16.82E−04Down
PRR15L6.82E−04Down
TRADD6.87E−04Down
SLC1A16.88E−04Down
CHD77.19E−04Down
CRYM7.20E−04Down
C77.20E−04Down
RBM387.34E−04Down
WWP27.40E−04Down
DTYMK7.48E−04Down
OR7E47P7.52E−04Down
NIP77.54E−04Down
FAM50B7.57E−04Down
XPA7.58E−04Down
KLHL77.73E−04Down
ZNF8397.81E−04Down
SLC34A17.81E−04Down
FBXL27.85E−04Down
PLSCR47.92E−04Down
PBXIP17.93E−04Down
NAT67.94E−04Down
PPIL27.97E−04Down
KLHL368.19E−04Down
MAN2B28.24E−04Down
EHD18.42E−04Down
GPR1838.75E−04Down
MFAP48.84E−04Down
HCLS18.99E−04Down
APEH9.01E−04Down
LOC1001299739.17E−04Down
DPAGT19.23E−04Down
TREX19.39E−04Down
PPP3CC9.39E−04Down
ATG4A9.44E−04Down
LY99.49E−04Down
TERF2IP9.53E−04Down
HSPA99.53E−04Down
ITGA79.61E−04Down
CAPNS19.63E−04Down

FDR: false discovery rate

  54 in total

1.  MicroRNA-4649-3p inhibits cell proliferation by targeting protein tyrosine phosphatase SHP-1 in nasopharyngeal carcinoma cells.

Authors:  Xiaofen Pan; Gang Peng; Sha Liu; Ziyi Sun; Zhenwei Zou; Gang Wu
Journal:  Int J Mol Med       Date:  2015-06-12       Impact factor: 4.101

2.  Interactions of miR-34b/c and TP-53 polymorphisms on the risk of nasopharyngeal carcinoma.

Authors:  Lijuan Li; Jian Wu; Xiutian Sima; Peng Bai; Wei Deng; Xueke Deng; Lin Zhang; Linbo Gao
Journal:  Tumour Biol       Date:  2013-03-17

3.  Etiological factors of nasopharyngeal carcinoma.

Authors:  Sai Wah Tsao; Yim Ling Yip; Chi Man Tsang; Pei Shin Pang; Victoria Ming Yi Lau; Guitao Zhang; Kwok Wai Lo
Journal:  Oral Oncol       Date:  2014-03-12       Impact factor: 5.337

4.  Five miRNAs as novel diagnostic biomarker candidates for primary nasopharyngeal carcinoma.

Authors:  Jin-Feng Tang; Zhong-Hua Yu; Tie Liu; Zi-Ying Lin; Ya-Hong Wang; La-Wei Yang; Hui-Juan He; Jun Cao; Hai-Li Huang; Gang Liu
Journal:  Asian Pac J Cancer Prev       Date:  2014

5.  Independent effect of EBV and cigarette smoking on nasopharyngeal carcinoma: a 20-year follow-up study on 9,622 males without family history in Taiwan.

Authors:  Wan-Lun Hsu; Jen-Yang Chen; Yin-Chu Chien; Mei-Ying Liu; San-Lin You; Mow-Ming Hsu; Czau-Siung Yang; Chien-Jen Chen
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2009-03-31       Impact factor: 4.254

6.  An in silico analysis of dynamic changes in microRNA expression profiles in stepwise development of nasopharyngeal carcinoma.

Authors:  Zhaohui Luo; Liyang Zhang; Zheng Li; Xiayu Li; Gang Li; Haibo Yu; Chen Jiang; Yafei Dai; Xiaofang Guo; Juanjuan Xiang; Guiyuan Li
Journal:  BMC Med Genomics       Date:  2012-01-19       Impact factor: 3.063

7.  miR-504 mediated down-regulation of nuclear respiratory factor 1 leads to radio-resistance in nasopharyngeal carcinoma.

Authors:  Luqing Zhao; Min Tang; Zheyu Hu; Bin Yan; Weiwei Pi; Zhi Li; Jing Zhang; Liqin Zhang; Wuzhong Jiang; Guo Li; Yuanzheng Qiu; Fang Hu; Feng Liu; Jingchen Lu; Xue Chen; Lanbo Xiao; Zhijie Xu; Yongguang Tao; Lifang Yang; Ann M Bode; Zigang Dong; Jian Zhou; Jia Fan; Lunquan Sun; Ya Cao
Journal:  Oncotarget       Date:  2015-06-30

8.  Molluscs as models for translational medicine.

Authors:  Fabio Tascedda; Davide Malagoli; Alice Accorsi; Giovanna Rigillo; Johanna M C Blom; Enzo Ottaviani
Journal:  Med Sci Monit Basic Res       Date:  2015-04-30

9.  The expression and significance of five types of miRNAs in breast cancer.

Authors:  Weili Min; Baofeng Wang; Jie Li; Jia Han; Yang Zhao; Wenjun Su; Zhijun Dai; Xijing Wang; Qingyong Ma
Journal:  Med Sci Monit Basic Res       Date:  2014-07-21

10.  MiR-23a sensitizes nasopharyngeal carcinoma to irradiation by targeting IL-8/Stat3 pathway.

Authors:  Jia-Quan Qu; Hong-Mei Yi; Xu Ye; Li-Na Li; Jin-Feng Zhu; Ta Xiao; Li Yuan; Jiao-Yang Li; Yuan-Yuan Wang; Juan Feng; Qiu-Yan He; Shan-Shan Lu; Hong Yi; Zhi-Qiang Xiao
Journal:  Oncotarget       Date:  2015-09-29
View more
  5 in total

1.  [Down-regulation of miR-205-5p enhances pro-apoptotic effect of 3-bromopyruvate on human nasopharyngeal carcinoma CNE2Z cells].

Authors:  Zongfen Shi; Pei Zhang; Xingyue Lu; Chenlu Zhu; Changjiang Chen; Surong Zhao; Hao Liu
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2019-10-30

Review 2.  MiRNAs in Radiotherapy Resistance of Nasopharyngeal Carcinoma.

Authors:  Yutong Tian; Lu Tang; Pin Yi; Qing Pan; Yaqian Han; Yingrui Shi; Shan Rao; Shiming Tan; Longzheng Xia; Jinguan Lin; Linda Oyang; Yanyan Tang; Jiaxin Liang; Xia Luo; Qianjin Liao; Hui Wang; Yujuan Zhou
Journal:  J Cancer       Date:  2020-04-06       Impact factor: 4.207

3.  Identification of novel prognostic biomarkers in renal cell carcinoma.

Authors:  Yuanzhang Zou; Qiu Lu; Qin Yao; Di Dong; Binghai Chen
Journal:  Aging (Albany NY)       Date:  2020-11-21       Impact factor: 5.682

4.  Deltex3 inhibits Epithelial Mesenchymal Transition in Papillary Thyroid Carcinoma via promoting ubiquitination of XRCC5 to regulate the AKT signal pathway.

Authors:  Lidong Wang; Yonglian Huang; Chenxi Liu; Mingyue Guo; Zhennan Ma; Jingni He; Ailian Wang; Xiaodan Sun; Zhen Liu
Journal:  J Cancer       Date:  2021-01-01       Impact factor: 4.207

5.  The Expression of miR-205 in Prostate Carcinoma and the Relationship with Prognosis in Patients.

Authors:  Zhuifeng Guo; Xuwei Lu; Fan Yang; Liang Qin; Ning Yang; Peiran Cai; Conghui Han; Jiawen Wu; Hang Wang
Journal:  Comput Math Methods Med       Date:  2022-08-30       Impact factor: 2.809
  5 in total

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