Open Access

Comprehensive analysis of a microRNA expression profile in pediatric medulloblastoma

  • Authors:
    • Junqiang Dai
    • Qiao Li
    • Zhitong Bing
    • Yinian Zhang
    • Liang Niu
    • Hang Yin
    • Guoqiang Yuan
    • Yawen Pan
  • View Affiliations

  • Published online on: April 20, 2017     https://doi.org/10.3892/mmr.2017.6490
  • Pages: 4109-4115
  • Copyright: © Dai et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Medulloblastoma is the most common malignant brain tumor of the central nervous system among children. Medulloblastoma is an embryonal tumor, of which little is known about the pathogenesis. Several efforts have been made to understand the molecular aspects of its tumorigenic pathways; however, these are poorly understood. microRNA (miRNA), a type of non‑coding short RNA, has been proven to be associated with a number of physiological processes and pathological processes of serious diseases, including brain tumors. Differentially expressed miRNAs serve an important role in numerous types of malignancy. The present study aims to define a differentially expressed set of miRNAs in medulloblastoma tumor tissue, compared with normal samples, to improve the understanding of the tumorigenesis. It was identified that 22 miRNAs were upregulated and 26 miRNAs were downregulated in the tumor tissue compared with the normal group. However, when the medulloblastoma tissue was compared with normal cerebellum tissue, 9 miRNAs were identified to be up or downregulated in the tumor samples. The differentially expressed miRNAs in the tumor tissue were identified in order to clarify the networks and pathways of tumorigenesis using Ingenuity Pathway Analysis. Subsequently, key regulatory genes associated with the development of medulloblastoma were identified, including tumor protein p53, insulin like growth factor 1 receptor, argonaute 2, mitogen‑activated protein kinases 1 and 3, sirtuin 1 and Y box binding protein 1.

Introduction

Medulloblastoma is the most common pediatric brain tumor, with an incidence of ~0.5/100,000 children <15 years old (1). It is classified as grade IV according to the World Health Organization (WHO) (2). Current treatment consisting of surgery, radiotherapy and adjuvant chemotherapy has a limited role in improving the prognosis of patients (3,4). Novel therapeutic methods based on the specific mechanism of medulloblastoma carcinogenesis are required to improve treatment efficiency and avoid the side effects of traditional therapy. An increasing number of studies have revealed that microRNAs (miRNAs/miRs) serve a role in tumorigenesis (59).

miRNAs are a type of small non-coding RNA (18–24 nucleotides). Gene expression is regulated by thousands of known miRNAs, which bind to imperfect complementary sites within the 3′ untranslated regions of their target protein-coding mRNAs, and repressing the expression of these genes at the level of mRNA stability and translation (10). Previous research has revealed that miRNAs are involved in the regulation of the majority of physiological and pathological process, including development, life span, and metabolism, and their dysregulation contributes to a number of types of disease, notably cancer (11). These small RNAs may function as oncogenes or tumor suppressors by modulating the levels of critical proteins, and their relevance in human disease and therapy is currently under investigation (12).

An increasing number of studies have demonstrated abnormal miRNA expression levels in the central nervous system (CNS) of cancer patients, suggesting that miRNAs may serve as key regulatory components in cancer of the CNS (5,10,13). The present study analyzed the medulloblastoma miRNA expression profiles for pediatric brain tumors. It was observed that 22 miRNAs were upregulated and 26 miRNAs were downregulated in the tumor tissue. Ingenuity Pathway Analysis (IPA) was used to analyze the regulatory network of the differentially expressed miRNA in medulloblastoma. It was proposed that the pathways exhibited in the network may have an important role in the tumorigenesis of medulloblastoma.

Materials and methods

The data were downloaded from the NCBI Gene Expression Omnibus (GEO) database (GSE42657, www.ncbi.nlm.nih.gov/geo). The series matrix file was downloaded and a log2 transformation was performed. The data include 7 control samples (consisting of 3 cerebellum and 4 frontal lobe controls) and 9 medulloblastoma samples. The data from all samples was normalized using the limma package in R software version 3.3.0 (https://www.r-project.org/). The selected samples of this profile are exhibited in Table I. The differentially expressed genes (DEGs) were obtained with the thresholds of |logFC|>1.0 and P<0.01, using linear models and empirical Bayes methods for assessing differential expression in microarray experiments in the limma package (Table II). A heat map of the different groups was produced to visualize the DEGs and cluster the corresponding groups with the differentially expressed miRNAs from the tumor tissue, using the R package gplots (Fig. 1).

Table I.

Sample descriptions.

Table I.

Sample descriptions.

DiagnosisNo. of sampleSerial no. of sample
Normal (frontal lobe)4GSM1047526, GSM1047529-GSM1047531
Normal (cerebellum)3GSM1047525, GSM1047527-GSM1047528
Medulloblastoma9 GSM1047507-GSM1047515

Table II.

miRs significantly differentially expressed (P<0.01) in medulloblastoma tissue compared with normal control samples.

Table II.

miRs significantly differentially expressed (P<0.01) in medulloblastoma tissue compared with normal control samples.

Gene namesLog fold change
Upregulated miRs
  hsa-miR-217  5.40
  hsa-miR-301  2.54
  hsa-miR-216  4.67
  hsa-miR-19a  2.11
  hsa-miR-15b  1.16
  hsa-miR-106b  1.19
  hsa-miR-18a  2.37
  hsa-miR-130b  1.56
  hsa-miR-182  3.52
  hsa-miR-345  1.62
  hsa-miR-17-3p  2.10
  hsa-miR-96  3.71
  hsa-miR-582  2.06
  hsa-miR-622  2.57
  hsa-miR-532  1.12
  hsa-miR-19b  1.02
  hsa-miR-18b  2.31
  hsa-miR-632  2.52
  hsa-miR-148a  1.13
  hsa-miR-17-5p  2.99
  hsa-miR-183  3.73
  hsa-miR-135a  1.19
Downregulated miRs
  hsa-miR-383−3.05
  hsa-miR-128a−2.53
  hsa-miR-433−2.38
  hsa-miR-488−2.22
  hsa-miR-584−2.97
  hsa-miR-128b−2.92
  hsa-miR-485-3p−1.89
  hsa-miR-329−2.43
  hsa-miR-299-5p−2.22
  hsa-miR-133b−1.60
  hsa-miR-330−2.37
  hsa-miR-181b−1.17
  hsa-miR-138−2.03
  hsa-miR-770-5p−2.03
  hsa-miR-149−1.57
  hsa-miR-133a−1.00
  hsa-miR-328−1.44
  hsa-miR-409-3p−1.63
  hsa-miR-656−2.17
  hsa-miR-642−2.37
  hsa-miR-432−1.15
  hsa-miR-539−1.03
  hsa-miR-668−1.79
  hsa-miR-487b−1.48
  hsa-miR-154*−1.15
  hsa-miR-212−1.81

[i] miR, microRNA. hsa-miR-154*, The precursor miRNA (pre-miRNA) undergoes another endonucleolytic cleavage, which is catalysed by Dicer, generating a miRNA-miRNA* duplex (where miRNA is the antisense, or guide, strand and miRNA* is the sense, or passenger, strand) of approx21-25 nucleotides (38).

The dataset (48 differentially expressed miRNAs) was uploaded to the Ingenuity Pathway Analysis software version 2016 (Qiagen, Inc., Valencia, CA, USA), using the microRNA target filter to find the targeting information. A total of 40 microRNAs targeting 11,757 mRNAs were identified by the filter. The core analysis function (rapid assessment of the signaling and metabolic pathways, molecular networks, and biological processes that are most markedly perturbed in the dataset of interest) to analyze the associated network functions of the miRNAs.

Results

Using miRNA microarray data from the GEO database, 48 miRNAs which are differentially expressed in medulloblastoma were identified, compared with the control group (Table II). The core analysis demonstrated the molecular network interactions and signaling pathways associated with 28 differentially expressed miRNAs of medulloblastoma, and their predicted molecular targets were rebuilt using IPA. The network ‘Organismal Injury and Abnormalities, Reproductive System Disease, Cancer’ had an IPA score of 41, focusing on 17 miRNA molecules, and the network ‘Cancer, Organismal Injury and Abnormalities, Cell Death and Survival’ had an IPA score of 23, focusing on 11 miRNA molecules (Fig. 2). The most impacted biological processes and diseases regulated by the analyzed miRNAs included organismal injury and abnormalities, reproductive system disease and cancer. The molecular network maps demonstrated that three primary components were identified to be at the central hub of the most significant network with a score of 41; these were tumor protein p53 (TP53), argonaute 2 (AGO2) and mitogen activated kinases 3 and 1 (ERK1/2) (Fig. 2A). TP53, sirtuin 1 (SIRT1) and Y box protein 1 (YBX1) were located in the center of the second network with a core analysis score of 23 (Fig. 2B). The first network included miR-17-5p, miR-130a-3p, miR-182-5p, miR-18a-5p, miR-19b-3p, miR-4288, miR-96-5p, which were upregulated in the medulloblastoma samples, and miR-128-3p, miR-132-3p, miR-133a-3p, miR-299a-5p, miR-409, miR-668-3p, which were downregulated. The second notable network (Fig. 2B) involved upregulated miR-217-5p and miR-216a-5p, and downregulated miR-329, miR-330 and miR-584, which are associated with the regulation of key regulatory genes in medulloblastoma development, including TP53, SIRT1 and YBX1.

However, when the cerebellum samples were used as the control, it was observed that 4 miRNAs were overexpressed and 5 miRNAs were underexpressed in the medulloblastoma (Table III). The differentially expressed miRNAs were uploaded to the IPA and core analysis was performed. The network ‘Cell death and Survival, Gastrointestinal Disease, Hepatic System Disease’ had an IPA score of 9, focusing on 4 miRNA molecules (Fig. 3).

Table III.

miRs significantly differentially expressed (P<0.01) in medulloblastoma tissue compared with normal cerebellum samples.

Table III.

miRs significantly differentially expressed (P<0.01) in medulloblastoma tissue compared with normal cerebellum samples.

Gene namesLog fold change
Upregulated miRs
  hsa-miR-217   5.22
  hsa-miR-216   5.17
  hsa-miR-582   3.31
  hsa-miR-15b   1.28
Downregulated miRs
  hsa-miR-383−3.39
  hsa-miR-206−3.40
  hsa-miR-133b−2.47
  hsa-miR-128a−2.61
  hsa-miR-654−1.89

[i] miR, microRNA.

Discussion

miRNAs function as master regulators and signal modulators by fine-tuning gene expression in multiple complex pathways. Disruption of miRNAs may result in a permissive tumorigenic state (13). A number of the miRNAs which exhibit dysregulated expression in medulloepithelioma have been reported to serve various roles in carcinogenesis (6,7,9). Through analysis of a GEO miRNA expression profiling dataset, miRNAs which are over- and underexpressed in medulloblastoma were identified, including miR-217, miR-216, miR-183, miR-182 and miR-96, which are upregulated in tumor tissue. Previous research demonstrates that miR-183-96-182 regulates cell survival, proliferation and migration in medulloblastoma, and increased miR-183-96-182 expression is associated with aggressive clinical course, high rates of metastasis and poor overall survival (8,9,14).

miR-217 and miR-216 were overexpressed in medulloblastoma. However, the role of miR-217 was controversial. miR-217 is an oncogene that is overexpressed in aggressive human B cell lymphomas (15). However, it may be a tumor suppressor in hepatocellular carcinoma (16). In pancreatic intraepithelial neoplasm, pancreatic ductal adenocarcinomas and clear cell renal carcinomas, miR-217 was observed to be downregulated (17,18). Further studies are required to determine whether increased expression of miR-217 in medulloblastoma represents an oncogenic effect, or if the dysregulation functions as a potential tumor suppressor. While miR-216 was underexpressed in various types of cancer, including breast cancer, pancreatic cancer and nasopharyngeal carcinoma (1921), it may serve as a tumor suppressor to inhibit cell proliferation, invasion and tumor growth. Further studies are required to determine whether increased expression of miR-216 in medulloblastoma represents an oncogenic effect or is a potential tumor suppressor.

In the present study, marked downregulation of miR-383, miR-206, miR-138, miR-128a/b and miR-133b was identified. A previous study suggested that miR-383 is downregulated in medulloblastoma. miR-383, repressing peroxiredoxin 3 at transcriptional and translational levels, suppressed the cell growth of medulloblastoma (6). Additionally, miR-206 was downregulated in all the four molecular subgroups of medulloblastoma as well as in cell lines. Orthodenticle homeobox 2 (OTX2), a target gene of miR-206 which is overexpressed in all non-sonic-hedgehog-driven medulloblastomas, is an oncogene in medulloblastoma. Overexpressed OTX2 supported the growth and proliferation of medulloblastomas. Therefore, underexpression of miR-206 contributed to the upregulation of OTX2 expression and enhanced growth of medulloblastoma cell lines (7). Studies have demonstrated a reduction in the miRNAs miR-383, miR-138, miR-128a/b, miR-133, miR-124 and let-7g in medulloblastoma. Nevertheless, miR-328, miR-133b, miR-149, miR-181b and miR-154 were downregulated in the study of Ferretti et al (5) and the present study. In addition, the present study revealed that miR-433, miR-488, miR-584, miR-329, miR-299-5p, miR-330, miR-770-5p, miR-656 and miR-642 were underexpressed in medulloblastoma, and further research is required to identify whether these miRNAs have a specific role in the tumorigenesis of medulloblastoma.

Based on microarray analysis, miRNAs that distinguish normal brain from medulloblastoma tissue were identified. As presented in the heat map, the dysregulated miRNAs are more closely associated with the medulloblastoma samples compared with the normal control samples. Additionally, by uploading the miRNAs from the first network to the IPA, it was identified that the dysregulated miRNAs were associated with key regulatory genes in tumorigenesis, including TP53, insulin-like growth factor 1 receptor, AGO2 and ERK1/2. The overexpressed miRNAs miR-17-5p and miR-182 (log fold change >2) have interactions with TP53; studies have demonstrated an association between these miRNAs and TP53 in tumorigenesis (22,23). AGOs are associated with miRNAs, due to their function in the RNA induced silencing complex (RISC), wherein they use small RNA guides to recognize targets. AGO2 had an interaction with miRNAs that are dysregulated in medulloblastoma, including miR-17-5p, miR-128, miR-96, miR-433, miR-383 and miR-138.

The second network, including miR-217 and miR-216, which are upregulated in medulloblastoma, exhibits associations with key regulatory genes in medulloblastoma development, including TP53, SIRT1 and YBX1. SIRT1 is a well understood member of the sirtuin protein class, preventing cell aging and apoptosis of normal cells (24,25). The effects of SIRT1 are controversial; the protein was considered to be a tumor promoter in neuroblastoma, prostate and skin cancer (2628), whereas it was considered to be a tumor suppressor in breast and colon cancer (29,30). Therefore, SIRT1 may serve a role in medulloblastoma and correlate with the formation and prognosis of human medulloblastomas (31). Recently, Dey et al (32) proved that YBX1 (YB1) is upregulated across human medulloblastoma subclasses and is required for medulloblastoma cell proliferation. Furthermore, the study identified that the SHH:YAP:YB1:IGF2 axis may be a powerful target for therapeutic intervention in medulloblastoma.

When the medulloblastoma samples were compared with the normal cerebellum, 9 dysregulated miRNAs were identified. The 9 miRNAs were uploaded to the IPA in order to perform the core analysis. The network involved 4 downregulated miRNAs and was associated with important regulators of tumorigenesis, including MET proto-oncogene (MET), AKT serine-threonine kinase 1 and sterol regulatory element-binding factor 1 (SREBF1). Previous evidence has demonstrated the association between aberrant MET signaling and medulloblastoma development (33,34). Previous studies have revealed that overexpressed miR-206 may inhibit MET in lung cancer (35,36), however the function of downregulated miR-206 in medulloblastoma is unknown. SREBF1 is a transcription factor, regulating lipogenesis, which is also involved in tumorigenesis (37). These network components alone are not sufficient to initiate medulloblastoma formation; the interactions between these dysregulated factors and additional genetic insults promote tumorigenesis.

In conclusion, based on the miRNA array data in the GEO database, miRNAs which are differentially expressed in medulloblastoma samples were identified. Certain dysregulated miRNAs have been confirmed; however, further research is required to verify the miRNAs which have not been previously identified. Additionally, the IPA core analysis, presenting the interaction of miRNAs and counterpart targets, provides a method to understand the tumorigenesis of medulloblastoma. The results of the present study may provide a potent target for therapeutic intervention or diagnosis in medulloblastomas.

Acknowledgements

The authors of the preset study would like to acknowledge the submitters of all these GEO data arrays and all the participants of this study. The present study was supported by the Lanzhou Science and Technology Bureau Project (grant nos. 2013-3-27 and 2015-3-86), Gansu Province Health Industry Research Project (grant nos. GSWSKY-2015-58 and GSWSKY-2015-58) and the doctoral research fund of Lanzhou University Second Hospital (grant no. ynbskyjj2015-1-02).

References

1 

Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW and Kleihues P: The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 114:97–109. 2007. View Article : Google Scholar :

2 

Roussel MF and Hatten ME: Cerebellum development and medulloblastoma. Curr Top Dev Biol. 94:235–282. 2011. View Article : Google Scholar :

3 

Gottardo NG and Gajjar A: Current therapy for medulloblastoma. Curr Treat Options Neurol. 8:319–334. 2006. View Article : Google Scholar

4 

Packer RJ, Rood BR and MacDonald TJ: Medulloblastoma: Present concepts of stratification into risk groups. Pediatr Neurosurg. 39:60–67. 2003. View Article : Google Scholar

5 

Ferretti E, De Smaele E, Po A, Di Marcotullio L, Tosi E, Espinola MS, Di Rocco C, Riccardi R, Giangaspero F, Farcomeni A, et al: MicroRNA profiling in human medulloblastoma. Int J Cancer. 124:568–577. 2009. View Article : Google Scholar

6 

Li KK, Pang JC, Lau KM, Zhou L, Mao Y, Wang Y, Poon WS and Ng HK: MiR-383 is downregulated in medulloblastoma and targets peroxiredoxin 3 (PRDX3). Brain pathol. 23:413–425. 2013. View Article : Google Scholar

7 

Panwalkar P, Moiyadi A, Goel A, Shetty P, Goel N, Sridhar E and Shirsat N: MiR-206, a Cerebellum Enriched miRNA is downregulated in all medulloblastoma subgroups and its overexpression is necessary for growth inhibition of medulloblastoma cells. J Mol Neurosci. 56:673–680. 2015. View Article : Google Scholar

8 

Weeraratne SD, Amani V, Teider N, Pierre-Francois J, Winter D, Kye MJ, Sengupta S, Archer T, Remke M, Bai AH, et al: Pleiotropic effects of miR-183~96~182 converge to regulate cell survival, proliferation and migration in medulloblastoma. Acta Neuropathol. 123:539–552. 2012. View Article : Google Scholar

9 

Zhang Z, Li S and Cheng SY: The miR-183-96-182 cluster promotes tumorigenesis in a mouse model of medulloblastoma. J Biomed Res. 27:486–494. 2013.

10 

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

11 

Esquela-Kerscher A and Slack FJ: Oncomirs-microRNAs with a role in cancer. Nat Rev Cancer. 6:259–269. 2006. View Article : Google Scholar

12 

Malumbres M: miRNAs versus oncogenes: The power of social networking. Mol Systems Biol. 8:5692012. View Article : Google Scholar

13 

Adams BD, Kasinski AL and Slack FJ: Aberrant regulation and function of microRNAs in cancer. Curr Biol. 24:R762–R776. 2014. View Article : Google Scholar :

14 

Cho YJ, Tsherniak A, Tamayo P, Santagata S, Ligon A, Greulich H, Berhoukim R, Amani V, Goumnerova L, Eberhart CG, et al: Integrative genomic analysis of medulloblastoma identifies a molecular subgroup that drives poor clinical outcome. J Clin Oncol. 29:1424–1430. 2011. View Article : Google Scholar

15 

de Y, ébenes VG, Bartolomé-Izquierdo N, Nogales-Cadenas R, Pérez-Durán P, Mur SM, Martínez N, Di Lisio L, Robbiani DF, Pascual-Montano A, Cañamero M, et al: miR-217 is an oncogene that enhances the germinal center reaction. Blood. 124:229–239. 2014. View Article : Google Scholar

16 

Su J, Wang Q, Liu Y and Zhong M: miR-217 inhibits invasion of hepatocellular carcinoma cells through direct suppression of E2F3. Mol Cell Biochem. 392:289–296. 2014. View Article : Google Scholar

17 

Li H, Zhao J, Zhang JW, Huang QY, Huang JZ, Chi LS, Tang HJ, Liu GQ, Zhu DJ and Ma WM: MicroRNA-217, down-regulated in clear cell renal cell carcinoma and associated with lower survival, suppresses cell proliferation and migration. Neoplasma. 60:511–515. 2013. View Article : Google Scholar

18 

Xue Y, Tayoun AN Abou, Abo KM, Pipas JM, Gordon SR, Gardner TB, RJ Jr, Suriawinata AA Barth and Tsongalis GJ: MicroRNAs as diagnostic markers for pancreatic ductal adenocarcinoma and its precursor, pancreatic intraepithelial neoplasm. Cancer Genet. 206:217–221. 2013. View Article : Google Scholar

19 

Deng M, Tang H, Zhou Y, Zhou M, Xiong W, Zheng Y, Ye Q, Zeng X, Liao Q, Guo X, et al: miR-216b suppresses tumor growth and invasion by targeting KRAS in nasopharyngeal carcinoma. J Cell Sci. 124:2997–3005. 2011. View Article : Google Scholar

20 

Rachagani S, Macha MA, Menning MS, Dey P, Pai P, Smith LM, Mo YY and Batra SK: Changes in microRNA (miRNA) expression during pancreatic cancer development and progression in a genetically engineered KrasG12D; Pdx1-Cre mouse (KC) model. Oncotarget. 6:40295–40309. 2015.

21 

Faraji F, Hu Y, Goldberger N, Wu G..Buetow KH, Zhang J and Hunter KW: Abstract A6: microRNA sequencing of AKXD recombinant inbred panel identifies miR-216b as a candidate metastasis suppressor in a murine model of breast cancer. Cancer Res. 72:A62012. View Article : Google Scholar

22 

Kanaan Z, Rai SN, Eichenberger MR, Barnes C, Dworkin AM, Weller C, Cohen E, Roberts H, Keskey B, Petras RE, et al: Differential microRNA expression tracks neoplastic progression in inflammatory bowel disease-associated colorectal cancer. Hum Mutat. 33:551–560. 2012. View Article : Google Scholar :

23 

Lin LT, Chiou SH and Lee YJ: Abstract 3548: Study of the tumor suppressive machinery of arsenic trioxide-induced glioblastoma multiforme inhibition via microRNA-182-Sestrin2 regulatory circuit. Cancer Res. 74:35482014. View Article : Google Scholar

24 

Kim D, Nguyen MD, Dobbin MM, Fischer A, Sananbenesi F, Rodgers JT, Delalle I, Baur JA, Sui G, Armour SM, et al: SIRT1 deacetylase protects against neurodegeneration in models for Alzheimer's disease and amyotrophic lateral sclerosis. EMBO. 26:3169–3179. 2007. View Article : Google Scholar

25 

Law IK, Liu L, Xu A, Lam KS, Vanhoutte PM, Che CM, Leung PT and Wang Y: Identification and characterization of proteins interacting with SIRT1 and SIRT3: Implications in the anti-aging and metabolic effects of sirtuins. Proteomics. 9:2444–2456. 2009. View Article : Google Scholar

26 

Chu F, Chou PM, Zheng X, Mirkin BL and Rebbaa A: Control of multidrug resistance gene mdr1 and cancer resistance to chemotherapy by the longevity gene sirt1. Cancer Res. 65:10183–10187. 2005. View Article : Google Scholar

27 

Hida Y, Kubo Y, Murao K and Arase S: Strong expression of a longevity-related protein, SIRT1, in Bowen's disease. Arch Dermatol Res. 299:103–106. 2007. View Article : Google Scholar

28 

Yu W, Fu YC, Zhou XH, Chen CJ, Wang X, Lin RB and Wang W: Effects of resveratrol on H(2)O(2)-induced apoptosis and expression of SIRTs in H9c2 cells. J Cell Biochem. 107:741–747. 2009. View Article : Google Scholar

29 

Firestein R, Blander G, Michan S, Oberdoerffer P, Ogino S, Campbell J, Bhimavarapu A, Luikenhuis S, de Cabo R, Fuchs C, et al: The SIRT1 deacetylase suppresses intestinal tumorigenesis and colon cancer growth. PLoS One. 3:e20202008. View Article : Google Scholar :

30 

Wang RH, Zheng Y, Kim HS, Xu X, Cao L, Luhasen T, Lee MH, Xiao C, Vassilopoulos A, Chen W, et al: Interplay among BRCA1, SIRT1, and Survivin during BRCA1-associated tumorigenesis. Mol Cell. 32:11–20. 2008. View Article : Google Scholar :

31 

Ma JX, Li H, Chen XM, Yang XH, Wang Q, Wu ML, Kong QY, Li ZX and Liu J: Expression patterns and potential roles of SIRT1 in human medulloblastoma cells in vivo and in vitro. Neuropathology. 33:7–16. 2013. View Article : Google Scholar

32 

Dey A, Robitaille M, Remke M, Maier C, Malhotra A, Gregorieff A, Wrana JL, Taylor MD, Angers S and Kenney AM: YB-1 is elevated in medulloblastoma and drives proliferation in Sonic hedgehog-dependent cerebellar granule neuron progenitor cells and medulloblastoma cells. Oncogene. 35:4256–4268. 2016. View Article : Google Scholar :

33 

Guessous F, Yang YZ, Johnson E, Marcinkiewicz L, Smith M, Zhang Y, Kofman A, Schiff D, Christensen J and Abounader R: Cooperation between c-met and focal adhesion kinase family members in medulloblastoma and implications for therapy. Mol Cancer Ther. 11:288–297. 2012. View Article : Google Scholar

34 

Li Y, Lal B, Kwon S, Fan X, Saldanha U, Reznik TE, Kuchner EB, Eberhart C, Laterra J and Abounader R: The scatter factor/hepatocyte growth factor: c-met pathway in human embryonal central nervous system tumor malignancy. Cancer Res. 65:9355–9362. 2005. View Article : Google Scholar

35 

Chen QY, Jiao DM, Wu YQ, Chen J, Wang J, Tang XL, Mou H, Hu HZ, Song J, Yan J, et al: MiR-206 inhibits HGF-induced epithelial-mesenchymal transition and angiogenesis in non-small cell lung cancer via c-Met/PI3k/Akt/mTOR pathway. Oncotarget. 7:18247–18261. 2016.

36 

Chen QY, Jiao DM, Yan L, Wu YQ, Hu HZ, Song J, Yan J, Wu LJ, Xu LQ and Shi JG: Comprehensive gene and microRNA expression profiling reveals miR-206 inhibits MET in lung cancer metastasis. Mol Biosyst. 11:2290–2302. 2015. View Article : Google Scholar

37 

Sun Y, He WW, Luo M, Zhou Y, Chang G, Ren W, Wu K, Li X, Shen J, Zhao X and Hu Y: SREBP1 regulates tumorigenesis and prognosis of pancreatic cancer through targeting lipid metabolism. Tumor Biol. 36:4133–4141. 2015. View Article : Google Scholar

38 

Jinek M and Doudna JA: A three-dimensional view of the molecular machinery of RNA interference. Nature. 457:405–412. 2009. View Article : Google Scholar

Related Articles

Journal Cover

June-2017
Volume 15 Issue 6

Print ISSN: 1791-2997
Online ISSN:1791-3004

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Dai J, Li Q, Bing Z, Zhang Y, Niu L, Yin H, Yuan G and Pan Y: Comprehensive analysis of a microRNA expression profile in pediatric medulloblastoma. Mol Med Rep 15: 4109-4115, 2017
APA
Dai, J., Li, Q., Bing, Z., Zhang, Y., Niu, L., Yin, H. ... Pan, Y. (2017). Comprehensive analysis of a microRNA expression profile in pediatric medulloblastoma. Molecular Medicine Reports, 15, 4109-4115. https://doi.org/10.3892/mmr.2017.6490
MLA
Dai, J., Li, Q., Bing, Z., Zhang, Y., Niu, L., Yin, H., Yuan, G., Pan, Y."Comprehensive analysis of a microRNA expression profile in pediatric medulloblastoma". Molecular Medicine Reports 15.6 (2017): 4109-4115.
Chicago
Dai, J., Li, Q., Bing, Z., Zhang, Y., Niu, L., Yin, H., Yuan, G., Pan, Y."Comprehensive analysis of a microRNA expression profile in pediatric medulloblastoma". Molecular Medicine Reports 15, no. 6 (2017): 4109-4115. https://doi.org/10.3892/mmr.2017.6490