MicroRNA expression profiles in supraglottic carcinoma

  • Authors:
    • Tianhong Zhang
    • Guojun Han
    • Yansong Wang
    • Kewa Chen
    • Yanan Sun
  • View Affiliations

  • Published online on: March 27, 2014     https://doi.org/10.3892/or.2014.3117
  • Pages: 2029-2034
Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

MicroRNAs (miRNAs) are single-stranded RNA molecules which regulate gene expression at the post-transcriptional level and several miRNAs have been found to be associated with some types of cancer. We sought to identify the expression and involvement of miRNAs in supraglottic carcinoma tissues compared with normal tissues and to determine whether miRNA expression is predictive of disease. Unsupervised clustering shows that miRNA profiles can distinguish tumor from normal tissues. Analysis of miRNA contents in supraglottic carcinoma highlighted nineteen differentially expressed miRNAs, three upregulated miRNAs (miR-21, miR-19a, miR-33a) and two downregulated miRNAs (miR-206, miR-375). The microarray results of supraglottic carcinoma and related computer analysis may be beneficial for further analysis of cancer diagnosis and therapy.

Introduction

Laryngeal squamous cell carcinoma (LSCC) is a very common malignant neoplasm of the head and neck. One of the key challenges in the treatment of LSCC is the management of metastasis to locoregional lymph nodes. Lymph node metastasis is common in supraglottic carcinoma and is the main cause of mortality for these patients. It has been reported that the incidence of lymph node metastasis may be as high as 25–50% in supraglottic carcinoma (1). Current treatments, including surgical intervention, radiation therapy and chemotherapy, have a moderate effect on early-stage cases, but are less effective in more advanced cases. Five-year overall survival for supraglottic cancer remains poor (2,3). Therefore, understanding the molecular pathways of carcinogenesis or progression is key to improving diagnosis, therapy and prevention of supraglottic carcinoma.

microRNAs (miRNAs) are a class of small, non-coding RNAs that are endogenously expressed in animal and plant cells. They regulate the expression of protein-coding genes at the translational level. One strand of the mature double-stranded miRNA is incorporated into the RNA-induced silencing complex, which downregulates target mRNAs either by degradation or by translational inhibition (4). miRNAs play important roles in normal regulation of gene expression for developmental timing, cell proliferation and apoptosis. Moreover, altered miRNA expression is implicated in cancer. Recently, miRNA genes were implicated in several types of cancer (57). The expression of miRNAs varies between cancer and normal cells and it also varies among different types of cancer. It has been shown that some miRNAs are aberrantly expressed in several different types of cancer (810), suggesting that they may play a role as a novel class of oncogenes or tumor-suppressor genes.

Several target genes have been experimentally identified for some miRNAs in various LSCCs; however, the global pattern of cellular functions and pathways affected by miRNAs in supraglottic cancer remains elusive. In the present study, expression profiling of miRNAs in clinical supraglottic carcinoma tissue samples was carried out, revealing the relationship between miRNA expression and supraglottic carcinoma. We also studied the candidate expression of genes that regulate supraglottic carcinoma miRNA processing and identified the possible gene ontology, pathway, evolution relationship of these candidate target genes. This information may be the basis of identifying a clinically applicable diagnostic tool in the future.

Materials and methods

Laryngeal carcinoma specimens and RNA isolation

Ten supraglottic carcinoma specimens and ten adjacent normal tissues were collected by surgical resection from the First Affiliated Hospital of Harbin Medical University between January and December 2008. These specimens were from patients between 42 to 78 years old, including 6 males and 4 females. Lymph metastasis was found in three cases. Prior to surgery, no patients were treated by radiotherapy or chemotherapy and they were in good condition without cancer transmission found from other parts of their bodies.

The samples were snap-frozen in liquid nitrogen and stored at −80°C. Total RNA was isolated from adjacent normal tissue and tumor tissues using the TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s protocol. RNA was quantified using the BioPhotometer (Eppendorf), aliquoted, and stored at −80°C briefly until needed.

Microarray profiling

miRNA was synthesized, amplified and purified using the Illumina TotalPrep RNA Amplification kit (Ambion Inc.), following the manufacturer’s recommendations. Total RNA (500 ng) was sent for miRNA profiling studies using the Human miRNA BeadChip (V.12.0) in Illumina BeadStation 500GX (Illumina, Inc.), which is single-channel format according to the standard operating procedures of the company. RNA was reverse transcribed. After second strand synthesis, the cDNA was transcribed in vitro and ncRNA labelled with biotin-16-UTP. Labelled probe hybridization to BeadChip was carried out. Briefly, poly-A tail was added, cDNA synthesis (biotinylated) followed, combined with miRNA specific oligo, then extension and ligation. PCR (Mastercycler 5333), lable, hybridization (45°C, 14–20 h), wash and imaging were carried our. Human miRNA BeadChip (V.12.0) contains 1,145 probes specific to human miRNA assessed at ~30 different beads on average (~850 probes from Sanger miRBase V.12.0 and 296 from literature or novel content identified with Illumina Solexa sequencing). The Cy3 fluorescence on the arrays was scanned at an excitation wavelength of 532 nm using a BeadArray Reader GX scanner (Illumina). Illumina BeadStudio software (version 1.5.0.34) was used for preliminary data analysis. Several quality control procedures were implemented to assess the quality of the whole experiment. Total RNA control samples were analyzed in the process. The Illumina BeadStudio software was used to view control summary reports and scatter plots. The scatter plots indicated a reduction in assay performance and highlighted samples that were of lower quality. The control summary report is generated by the BeadStudio software, which evaluates the performance of the built-in controls of the BeadChips in the process. This allows the user to look for variations in signal intensity, hybridization signal, background signal and the background to noise ratio for all samples analyzed in that run. Data are expressed as log2 ratios of fluorescence intensities of the experimental and the common reference sample. The Illumina data were then normalized using the ‘normalize quantiles’ function in the BeadStudio software.

miRNA expression analysis

Cluster analysis was performed using the metric Euclidean distance to compute the distance matrix for supraglottic carcinoma patients based on the expression of 1,145 differentially expressed miRNAs. To agglomerate the patients in the hierarchical cluster, we used the Ward method. Function heatmap.2 from R package gplots was used for the graphical display of the dendrogram (11).

Predicted miRNA target analysis

Potential miRNA targets were predicted and analyzed using DIANA TarBase (12) (http://diana.pcbi.upenn.edu/). Predicted target lists were analyzed using DIANA mirPath for association with molecular pathways potentially altered by the expression of single or multiple miRNAs (13). Predicted target lists were also analyzed for association with Gene Ontology (GO) (14) terms using L2L microarray analysis tools (15).

Evolution analysis

Potential miRNA target sequences were obtained from the National Center for Biotechnology Information website (NCBI, USA). The sequences were aligned by the ClustalW program (http://www.ebi.ac.uk/clustalw/) (16). The evolutionary tree of protein sequences was constructed by the neighbor-joining (NJ) method with protein p-distances by MEGA4 (Molecular Evolutionary Genetics Analysis) software (http://www.megasoftware.net/index.html) (17) and the reliability of the tree topology was assessed by 1,000 bootstrap replications.

Results

miRNA expression profile in supraglottic carcinoma and normal tissues

Using the miRNA microarray containing 1,145 human miRNA probe sets, we first assessed the miRNA profiles in three paired supraglottic carcinoma and normal tissues.

Unsupervised hierarchical clustering based on all the miRNAs spotted on the chip revealed a marked, distinct separation of the supraglottic carcinoma miRNA profiles compared with those of normal tissues. Significance analysis of microarray identified 85 significantly differentially expressed miRNAs (P<0.01) between supraglottic carcinoma and normal tissues. These human miRNAs further identified by significance analysis of microarray method showed >5-fold difference between supraglottic carcinoma and normal tissues. This method identified 19 miRNAs to be significantly altered in their expression between supraglottic carcinoma and non-diseased tissues, with 3 being upregulated (miR-21, miR-19a, miR-33a) and 16 downregulated in supraglottic carcinoma (Table I). Unsupervised clustering analysis using these 19 miRNAs discriminated cancer from non-cancer tissues (Fig. 1), thus indicating that these miRNAs may be a valid diagnostic for supraglottic carcinoma, although due to the small number of samples analyzed, this requires further analysis.

Table I

Differentially expressed miRNAs in supraglottic carcinoma tissues compared with normal tissues.

Table I

Differentially expressed miRNAs in supraglottic carcinoma tissues compared with normal tissues.

miRNAChromosomeExpression fold (cancer/normal tissue)P-value
miR-34b110.0614817.36E-38
miR-885-5p30.1075027.36E-38
miR-2184,50.1332697.36E-38
miR-34c-3p110.1333167.36E-38
miR-299-5p140.1341487.36E-38
miR-37520.1392677.36E-38
miR-30a60.1408643.62E-33
miR-20660.1481762.83E-10
miR-410140.160992.15E-27
miR-129990.166146.60E-25
miR-128a:9.1-0.1684937.36E-38
miR-432140.1709296.93E-37
miR-136140.1739216.49E-13
miR-486-3p80.1821581.16E-36
miR-337-3p140.1863347.86E-35
miR-125b-2210.1872133.62E-20
miR-21178.4538340.008087
miR-19a1313.150160.0054576
miR-33a2213.331460.0057371

[i] The miRNA microarray analysis was performed to detect the differentially expressed miRNAs in supraglottic carcinoma and normal tissues. P-values were generated in BeasStudio Software. miRNA, microRNA.

Bioinformatics analysis of supraglottic carcinoma miRNA target genes

To investigate the involvement of miRNAs in supraglottic carcinoma, we performed systemic bioinformatics analysis to identify potential gene targets, including pathway analysis (Fig. 2). Several pathways were enriched in the target genes. Notably, multiple genes were related to signaling pathways. The list of regulated gene targets was also used to perform a GO (14) analysis to evaluate the specific functional categories of genes from broad GO categories (Table II). The results indicated that seven biological processes, six molecular functions and two cellular components were statistically enriched with these miRNA targets. Furthermore, we found that 25 potential target genes related to 12 miRNAs which were strongly supported by references to avoid the false negative predictions (Table III). As a result, 3 target genes were involved in mRNA cleavage and 13 target genes were involved in mRNA repression. The evolutionary tree of miRNA target genes was further analyzed to illustrate the function of these genes (Fig. 3). These genes showed a high level of sequence divergence. It is reasonable to infer that these genes are members of a multigene family, since they shared low sequence similarity. Generally, a node with >70% bootstrap supporting value is a stable node. The NJ-tree of miRNA target genes exhibited low bootstrap supporting values on each deep branch node and, therefore, few strong conclusions may be drawn from the evolutionary tree.

Table II

Gene Ontology analysis in supraglottic carcinoma.

Table II

Gene Ontology analysis in supraglottic carcinoma.

GO typeGO termDescriptionP-value
ProcessGO: 0006725Cellular aromatic compound metabolic process2.79E-4
ProcessGO: 0050776Regulation of immune response4.64E-4
ProcessGO: 0001919Regulation of receptor recycling4.94E-4
ProcessGO: 0003015Heart process5E-4
ProcessGO: 0060047Heart contraction5E-4
ProcessGO: 0019882Antigen processing and presentation5.76E-4
ProcessGO: 0006066Alcohol metabolic process8.35E-4
ProcessGO: 0008152Metabolic process9.43E-4
ProcessGO: 0031943Regulation of glucocorticoid metabolic process9.83E-4
FunctionGO: 0005488Binding5.05E-7
FunctionGO: 0003824Catalytic activity1.08E-5
FunctionGO: 0004423 Iduronate-2-sulfatase activity5.37E-4
FunctionGO: 0046872Metal ion binding5.9E-4
FunctionGO: 0000062Acyl-CoA binding9.21E-4
FunctionGO: 0043169Cation binding9.35E-4
ComponentGO: 0044424Intracellular part3.63E-6
ComponentGO: 0044444Cytoplasmic part8.87E-5

[i] Gene Ontology (GO) analysis was performed to detect the differentially expressed miRNAs in supraglottic carcinoma and normal tissues. P-values were calculated from binomial distribution. miRNAs, microRNAs.

Table III

Comparison of pathways associated with miRNA and mRNA signatures.

Table III

Comparison of pathways associated with miRNA and mRNA signatures.

miRNAGeneTypeAuthor/(Ref.)
miR-10aHOXA1mRNA repressionGarzon et al (29)
miR-10bHOXD10mRNA repressionMa et al (33)
miR-125bLIN28mRNA cleavageWu and Belasco (39)
miR-133aPKM2UnknownWong et al (38)
miR-133bPKM2UnknownWong et al (38)
miR-136RTL1mRNA cleavageDavis et al (28)
miR-19aMECP2UnknownLewis et al (32)
miR-19aPTENmRNA repressionLewis et al (32)
miR-19aHOXA5UnknownLewis et al (32)
miR-206FSTL1mRNA repressionRosenberg et al (36)
miR-206UTRNmRNA repressionRosenberg et al (36)
miR-206GJA1mRNA repressionAnderson et al (27)
miR-206ESR1mRNA cleavageAdams et al (26)
miR-206TAC1mRNA repressionGreco and Rameshwar (30)
miR-370MAP3K8UnknownMeng et al (34)
miR-375MTPNmRNA repressionPoy et al (35)
miR-375AhrUnknownKrek et al (31)
miR-375C1QBPmRNA repressionKrek et al (31)
miR-375Insm1UnknownKrek et al (31)
miR-375ADIPOR2mRNA repressionKrek et al (31)
miR-375JAK2mRNA repressionKrek et al (31)
miR-375Mxi1UnknownKrek et al (31)
miR-375USP1mRNA repressionKrek et al (31)
miR-433FGF20UnknownWang et al (37)
miR-21BTG2mRNA repressionLiu et al (22)

[i] Pathway analysis was performed to detect the differentially expressed miRNAs in supraglottic carcinoma and normal tissues. miRNAs, microRNAs.

Discussion

The present study demonstrated that multiple microRNAs (miRNAs) are upregulated or downregulated in supraglottic carcinoma, including an extensively validated subset that may be potential clinical biomarkers of disease. Microarray profiling of more than 1,000 miRNAs identified 85 miRNAs that were significantly differentially expressed in tumor tissues compared with normal tissues. Of the 85 human miRNAs, three upregulated miRNAs (miR-21, miR-19a, miR-33a) and two downregulated miRNAs (miR-206, miR-375) were highlighted. Twenty-five target genes were identified to help characterize the diverse functions of miRNAs. Furthermore, biology process and evolution were explored to find the function of miRNAs.

miRNA expression profiles can be used to classify human cancer (14,18). Distinct signatures for several epithelial cancers have been reported, such as breast, lung, pancreatic and gastric cancer (9,1921). Our study explored miRNA expression in supraglottic carcinoma. Consistent with previous reports of miRNA expression in head and neck cancer (2225), some miRNAs were significantly deregulated, although changes in individual miRNAs did not match completely. For example, miR-21 was significantly upregulated in all head and neck cancer analysis (2225). Tran et al found miR-21 and miR-205 were highly expressed in head and neck cancer cell lines (25). Chang et al found miR-21, let-7, 18, 29c, 142-3p, 155 and 16b were significantly overexpressed in primary head and neck squamous cell carcinoma (24). Avissar et al found miR-221 and miR-375 were significantly altered and upregulated in head and neck squamous cell carcinoma (23). Liu et al identified 13 miRNAs that were differentially expressed, 7 miRNAs were downregulated and 6 miRNAs (let-7a-1, miR-203, miR-205, miR-21, miR-98 and miR-16-1) were upregulated in laryngeal carcinoma tissues based on 210 human miRNA probe sets (22). This indicated that the molecular biology of head and neck squamous cell carcinoma is complex. It is important to analyze the miRNA mechanism on a larger scale.

miRNAs modulate gene expression by targeting mRNAs for translational suppression or mRNA cleavage and it is well known that miRNAs regulate a variety of cellular activities through their effect on the expression of multiple target genes (4). The identification of target genes regulated by a specific miRNA has been proved difficult despite the development of computational approaches to predict miRNA targets. The ability to find target genes is further complicated by the fact that target selectivity of miRNAs may depend on the cellular microenvironment. Our studies identified 25 reliable target genes, supported by previous studies (22,2639). The evolutionary tree of these target genes showed a low sequence similarity and it indicated that miRNAs may play different roles in supraglottic carcinoma. Also, these results provided a possible way to address biological meaning from the global pattern of cellular functions and pathways that are affected by miRNAs in supraglottic carcinoma. Gene Ontology (GO) (14) was developed into three structured controlled vocabularies (ontologies) that describe gene products in components and molecular functions in a species-independent manner (40). GO enrichment analysis was used to reduce the number of targets of a large group of co-expressed miRNAs and to find biological functions potentially affected by multiple miRNAs in our research. We performed a statistical enrichment analysis of GO categories to find categories that are enriched with targets of co-expressed miRNAs. It allowed us to reduce a very large raw list of predicted target genes to a smaller set of target genes from significantly enriched GO categories. We assumed that filtering GO categories on the total number of hits by miRNAs targeting the same category would reduce the number of false positive target predictions and at the same time would allow narrowing down of the large target lists and determining those biological functions.

In conclusion, our global analysis of miRNA array in human supraglottic carcinoma tissues demonstrated that co-expressed miRNAs may collectively provide systemic compensatory response to the abnormal functional and phenotypic changes in supraglottic carcinoma by targeting a broad range of functional categories and abnormally activated pathways known to be affected in supraglottic carcinoma. Such system biology based approach may provide new avenues for biological interpretation of miRNA profiling data as well as generation of experimentally testable hypotheses regarding collective regulatory functions of miRNAs in supraglottic carcinoma biology.

Acknowledgements

This study was supported by grants from the Heilongjiang Postdoctoral Foundation (LBHZ12194), and the Research Foundation of Heilongjiang Provincial Department of Education.

References

1 

Rudolph E, Dyckhoff G, Becher H, Dietz A and Ramroth H: Effects of tumour stage, comorbidity and therapy on survival of laryngeal cancer patients: a systematic review and a meta-analysis. Eur Arch Otorhinolaryngol. 268:165–179. 2011. View Article : Google Scholar : PubMed/NCBI

2 

Cosetti M, Yu GP and Schantz SP: Five-year survival rates and time trends of laryngeal cancer in the US population. Arch Otolaryngol Head Neck Surg. 134:370–379. 2008.PubMed/NCBI

3 

Petrakos I, Kontzoglou K, Nikolopoulos TP, Papadopoulos O and Kostakis A: Glottic and supraglottic laryngeal cancer: epidemiology, treatment patterns and survival in 164 patients. J BUON. 17:700–705. 2012.PubMed/NCBI

4 

Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 116:281–297. 2004. View Article : Google Scholar

5 

Ferretti E, De Smaele E, Po A, et al: MicroRNA profiling in human medulloblastoma. Int J Cancer. 124:568–577. 2009. View Article : Google Scholar : PubMed/NCBI

6 

Khoshnaw SM, Green AR, Powe DG and Ellis IO: MicroRNA involvement in the pathogenesis and management of breast cancer. J Clin Pathol. 62:422–428. 2009. View Article : Google Scholar : PubMed/NCBI

7 

Raponi M, Dossey L, Jatkoe T, et al: MicroRNA classifiers for predicting prognosis of squamous cell lung cancer. Cancer Res. 69:5776–5783. 2009. View Article : Google Scholar : PubMed/NCBI

8 

Akao Y, Nakagawa Y and Naoe T: MicroRNAs 143 and 145 are possible common onco-microRNAs in human cancers. Oncol Rep. 16:845–850. 2006.PubMed/NCBI

9 

Iorio MV, Ferracin M, Liu CG, et al: MicroRNA gene expression deregulation in human breast cancer. Cancer Res. 65:7065–7070. 2005. View Article : Google Scholar : PubMed/NCBI

10 

Michael MZ, O’ Connor SM, van Holst Pellekaan NG, Young GP and James RJ: Reduced accumulation of specific microRNAs in colorectal neoplasia. Mol Cancer Res. 1:882–891. 2003.PubMed/NCBI

11 

Gentleman RC, Carey VJ, Bates DM, et al: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5:R802004. View Article : Google Scholar : PubMed/NCBI

12 

Papadopoulos GL, Reczko M, Simossis VA, Sethupathy P and Hatzigeorgiou AG: The database of experimentally supported targets: a functional update of TarBase. Nucleic Acids Res. 37:D155–D158. 2009. View Article : Google Scholar : PubMed/NCBI

13 

Papadopoulos GL, Alexiou P, Maragkakis M, Reczko M and Hatzigeorgiou AG: DIANA-mirPath: integrating human and mouse microRNAs in pathways. Bioinformatics. 25:1991–1993. 2009. View Article : Google Scholar : PubMed/NCBI

14 

Lu J, Getz G, Miska EA, et al: MicroRNA expression profiles classify human cancers. Nature. 435:834–838. 2005. View Article : Google Scholar : PubMed/NCBI

15 

Newman JC and Weiner AM: L2L: a simple tool for discovering the hidden significance in microarray expression data. Genome Biol. 6:R812005. View Article : Google Scholar : PubMed/NCBI

16 

Thompson JD, Higgins DG and Gibson TJ: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22:4673–4680. 1994. View Article : Google Scholar

17 

Kumar S, Nei M, Dudley J and Tamura K: MEGA: a biologist-centric software for evolutionary analysis of DNA and protein sequences. Brief Bioinform. 9:299–306. 2008. View Article : Google Scholar : PubMed/NCBI

18 

Volinia S, Calin GA, Liu CG, et al: A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA. 103:2257–2261. 2006. View Article : Google Scholar : PubMed/NCBI

19 

Petrocca F, Visone R, Onelli MR, et al: E2F1-regulated microRNAs impair TGFβ-dependent cell-cycle arrest and apoptosis in gastric cancer. Cancer Cell. 13:272–286. 2008.

20 

Bloomston M, Frankel WL, Petrocca F, et al: MicroRNA expression patterns to differentiate pancreatic adenocarcinoma from normal pancreas and chronic pancreatitis. JAMA. 297:1901–1908. 2007. View Article : Google Scholar : PubMed/NCBI

21 

Yanaihara N, Caplen N, Bowman E, et al: Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell. 9:189–198. 2006. View Article : Google Scholar : PubMed/NCBI

22 

Liu M, Wu H, Liu T, et al: Regulation of the cell cycle gene, BTG2, by miR-21 in human laryngeal carcinoma. Cell Res. 19:828–837. 2009. View Article : Google Scholar

23 

Avissar M, Christensen BC, Kelsey KT and Marsit CJ: MicroRNA expression ratio is predictive of head and neck squamous cell carcinoma. Clin Cancer Res. 15:2850–2855. 2009. View Article : Google Scholar : PubMed/NCBI

24 

Chang SS, Jiang WW, Smith I, et al: MicroRNA alterations in head and neck squamous cell carcinoma. Int J Cancer. 123:2791–2797. 2008. View Article : Google Scholar : PubMed/NCBI

25 

Tran N, McLean T, Zhang X, et al: MicroRNA expression profiles in head and neck cancer cell lines. Biochem Biophys Res Commun. 358:12–17. 2007. View Article : Google Scholar

26 

Adams BD, Furneaux H and White BA: The micro-ribonucleic acid (miRNA) miR-206 targets the human estrogen receptor-α (ERα) and represses ERα messenger RNA and protein expression in breast cancer cell lines. Mol Endocrinol. 21:1132–1147. 2007.PubMed/NCBI

27 

Anderson C, Catoe H and Werner R: MIR-206 regulates connexin43 expression during skeletal muscle development. Nucleic Acids Res. 34:5863–5871. 2006. View Article : Google Scholar : PubMed/NCBI

28 

Davis E, Caiment F, Tordoir X, et al: RNAi-mediated allelic trans-interaction at the imprinted Rtl1/Peg11 locus. Curr Biol. 15:743–749. 2005.PubMed/NCBI

29 

Garzon R, Pichiorri F, Palumbo T, et al: MicroRNA fingerprints during human megakaryocytopoiesis. Proc Natl Acad Sci USA. 103:5078–5083. 2006. View Article : Google Scholar : PubMed/NCBI

30 

Greco SJ and Rameshwar P: MicroRNAs regulate synthesis of the neurotransmitter substance P in human mesenchymal stem cell-derived neuronal cells. Proc Natl Acad Sci USA. 104:15484–15489. 2007. View Article : Google Scholar : PubMed/NCBI

31 

Krek A, Grün D, Poy MN, et al: Combinatorial microRNA target predictions. Nat Genet. 37:495–500. 2005. View Article : Google Scholar

32 

Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP and Burge CB: Prediction of mammalian microRNA targets. Cell. 115:787–798. 2003. View Article : Google Scholar : PubMed/NCBI

33 

Ma L, Teruya-Feldstein J and Weinberg RA: Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature. 449:682–688. 2007. View Article : Google Scholar : PubMed/NCBI

34 

Meng F, Wehbe-Janek H, Henson R, Smith H and Patel T: Epigenetic regulation of microRNA-370 by interleukin-6 in malignant human cholangiocytes. Oncogene. 27:378–386. 2008. View Article : Google Scholar : PubMed/NCBI

35 

Poy MN, Eliasson L, Krutzfeldt J, et al: A pancreatic islet-specific microRNA regulates insulin secretion. Nature. 432:226–230. 2004. View Article : Google Scholar : PubMed/NCBI

36 

Rosenberg MI, Georges SA, Asawachaicharn A, Analau E and Tapscott SJ: MyoD inhibits Fstl1 and Utrn expression by inducing transcription of miR-206. J Cell Biol. 175:77–85. 2006. View Article : Google Scholar : PubMed/NCBI

37 

Wang G, van der Walt JM, Mayhew G, et al: Variation in the miRNA-433 binding site of FGF20 confers risk for Parkinson disease by overexpression of α-synuclein. Am J Hum Genet. 82:283–289. 2008.PubMed/NCBI

38 

Wong TS, Liu XB, Chung-Wai Ho A, et al: Identification of pyruvate kinase type M2 as potential oncoprotein in squamous cell carcinoma of tongue through microRNA profiling. Int J Cancer. 123:251–257. 2008. View Article : Google Scholar : PubMed/NCBI

39 

Wu L and Belasco JG: Micro-RNA regulation of the mammalian lin-28 gene during neuronal differentiation of embryonal carcinoma cells. Mol Cell Biol. 25:9198–9208. 2005.PubMed/NCBI

40 

Ashburner M, Ball CA, Blake JA, et al: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

May-2014
Volume 31 Issue 5

Print ISSN: 1021-335X
Online ISSN:1791-2431

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Zhang T, Han G, Wang Y, Chen K and Sun Y: MicroRNA expression profiles in supraglottic carcinoma. Oncol Rep 31: 2029-2034, 2014
APA
Zhang, T., Han, G., Wang, Y., Chen, K., & Sun, Y. (2014). MicroRNA expression profiles in supraglottic carcinoma. Oncology Reports, 31, 2029-2034. https://doi.org/10.3892/or.2014.3117
MLA
Zhang, T., Han, G., Wang, Y., Chen, K., Sun, Y."MicroRNA expression profiles in supraglottic carcinoma". Oncology Reports 31.5 (2014): 2029-2034.
Chicago
Zhang, T., Han, G., Wang, Y., Chen, K., Sun, Y."MicroRNA expression profiles in supraglottic carcinoma". Oncology Reports 31, no. 5 (2014): 2029-2034. https://doi.org/10.3892/or.2014.3117