MicroRNA profiling in pediatric pilocytic astrocytoma reveals biologically relevant targets, including PBX3, NFIB, and METAP2

MicroRNA profiling in pediatric pilocytic astrocytoma reveals biologically relevant targets

including PBX3, NFIB, and METAP2

Cheng-Ying Ho1, Eli Bar

1, Caterina Giannini

2, Luigi Marchionni

3, Matthias A. Karajannis

4,5,

David Zagzag4,6,7

, David H. Gutmann8, Charles G. Eberhart

1, Fausto J. Rodriguez

1

1Division of Neuropathology, Department of Pathology, Johns Hopkins University; 720 Rutland

Avenue, Baltimore, MD 21205 (CH,EB,CGE, FJR); 2Department of Laboratory Medicine and

Pathology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905 (CG);

3Cancer Biology

Program, the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, CRB II

Rm 554, Baltimore, MD 21205(LM), 4NYU Cancer Institute, New York, New York;

5Division

of Pediatric Hematology/Oncology, Department of Pediatrics, 6Department of Neurosurgery, and

7Division of Neuropathology, Department of Pathology, NYU Langone Medical Center, 160

East 32nd Street New York, New York 10016 (MAK, DZ); 8Department of Neurology,

Washington University School of Medicine, 660 S. Euclid Avenue, St. Louis, MO 63110.

Running Title: MicroRNA profiles of pilocytic astrocytoma

Address Correspondence to:

Fausto J. Rodriguez M.D.

Department of Pathology

Division of Neuropathology

Johns Hopkins Hospital

Sheikh Zayed Tower, Room M2101

1800 Orleans Street

Baltimore, MD 21231

Phone: 443-287-6646

*Manuscript (Revised)

Fax: 410-614-9310

Email: [email protected]

Funding: This work was funded in part by the Childhood Brain Tumor Foundation (FJR), the

Pilocytic/Pilomyxoid Astrocytoma Fund (CGE), the Pediatric Low Grade Astrocytoma

Association (CGE), Ian’s Friends Foundation (MAK), Mayo Clinic CTSA through grant number

UL1 RR024150 from the National Center for Research Resources (NCRR) (FJR), and the P50

CA108961 Mayo Clinic SPORE in Brain Cancer (CG). Samples quality assessment and

microarray analysis were conducted at The Sidney Kimmel Cancer Center Microarray Core

Facility at Johns Hopkins University, supported by NIH grant P30 CA006973 entitled Regional

Oncology Research Center.

Conflict of Interest: The authors have no conflict of interest to disclose.

mailto:[email protected]

Abstract

Background: Pilocytic astrocytoma (PA) is a WHO grade I glioma that occurs most commonly

in children and young adults. Specific genetic alterations have been described in PA, but the

pathogenesis remains poorly understood. We studied microRNA (miRNA) alterations in a large

cohort of PA.

Methods: A total of 43 PA, including 35 sporadic grade I PA, 4 neurofibromatosis-1 (NF1)-

associated PA, 4 PA with pilomyxoid features, and 5 non-neoplastic brain controls were

examined. BRAF fusion status was assessed in most cases. RNA was examined using the Agilent

Human miRNA Microarray V3 platform. Expression of miRNAs subsets was validated by

quantitative real-time PCR (qRT-PCR) with Taqman probes. Validation of predicted protein

targets was performed on tissue microarrays by immunohistochemistry

Results: We identified a subset of miRNAs that were differentially expressed in pediatric PAs

versus normal brain tissue: 13 miRNAs were underexpressed and 20 miRNAs were

overexpressed in tumors. Differences were validated by qRT-PCR in a subset, with average fold

change in tumor vs. brain of -17 (miR-124), -15 (miR-129), and 19.8 (miR-21). Searching for

predicted protein targets in Targetscan, we identified a number of known and putative oncogenes

that were predicted targets of miRNA sets relatively underexpressed in PA. Predicted targets

with increased expression at the mRNA and/or protein level in PA, included PBX3, METAP2,

and NFIB.

Conclusions: A unique miRNA profile exists in PA as compared to brain tissue. These miRNAs

and their targets may play a role in the pathogenesis of PA.

Key Words: Glioma; microRNA; pilocytic astrocytoma; neurofibromatosis; BRAF

Introduction

Pilocytic astrocytoma (PA) is a WHO grade I neoplasm representing the most frequent primary

glioma of children and young adults. Most PAs have an excellent outcome after gross total

resection, particularly when they arise in accessible anatomic locations such as the cerebellum.

However, a subset may behave in a more aggressive fashion, and clinically progress despite the

use of conventional treatments. Histologic features associated with a more aggressive course

include the presence of monomorphous pilomyxoid features (i.e. pilomyxoid variant)1 and

anaplasia in the form of brisk mitotic activity with or without necrosis2.

Recent studies have highlighted novel genetic alterations associated with PA. Tandem

duplications of the BRAF kinase domain, leading to a BRAF:KIAA1549 fusion, is present in most

PAs3-10

. A subset of tumors have other, usually mutually exclusive, alterations including RAF1

rearrangements, a FAM131B-BRAF fusion mediated by a small interstitial deletion, or small

BRAF insertions10-12

. In addition, PA is the most frequent glioma in patients with

neurofibromatosis type 113

, resulting from germline mutations in the NF1 gene and homozygous

inactivation in associated tumors, leading to RAS activation. All these genetic alterations lead to

downstream activation of the MAPK signaling pathway14

.

In recent years, an increasing role for non-coding small RNA (i.e. microRNA) has been

uncovered in carcinogenesis. Mature microRNAs are small single stranded RNA molecules that

bind to regulatory sequences of key mRNAs, promoting their degradation and/or inhibiting

translation. In the process of carcinogenesis the upregulation of particular microRNAs may

affect tumor suppressors, while downregulation may allow overexpression of oncoproteins

resulting in deregulation of cell proliferation and survival.

Several microRNAs have been shown to be involved in brain tumorigenesis, including

miR-21, miR-7, miR-181a/b, miR-221 and miR-22215-19

, and also seem to regulate oncogenic

signaling pathways in diffuse gliomas, such as glioblastoma20,21

. PTEN in particular is a key

tumor suppressor gene frequently inactivated in diffusely infiltrating gliomas, and may in fact be

targeted, albeit not exclusively, by specific microRNAs such as miR-2122

. Since microRNAs

may have more than one target, microRNA profiling may stratify biological and clinically

relevant subgroups more accurately than conventional mRNA profiling23

. Low grade pediatric

gliomas, such as PAs in particular, are attractive for microRNA study since they lack gross

genomic alterations24

, suggesting undiscovered epigenetic and subtle genetic changes may

contribute to their pathogenesis. In addition, microRNA profiling may lead to further, biology-

based refinements in classifying histopathologically ambiguous low grade gliomas that defy

traditional classification schemes, and potentially may have prognostic or therapeutic

significance.

Given the low level of genetic instability observed in PAs, we hypothesized that

epigenetic or posttranscriptional regulation may play an important role in its pathogenesis, as

described in other low grade neuroectodermal tumors such as schwannomas25-27

. Some studies

have also highlighted a possible role for microRNAs in pediatric brain tumors28,29

, including

medulloblastoma and ependymoma30

. A small number of PAs have also been tested for

microRNA levels, which may be differentially expressed in this tumor type29

. Furthermore,

overexpression of specific microRNAs has been observed in papillary carcinoma of the thyroid,

another tumor characterized by BRAF aberrations31

. Identification of key microRNAs also

provides a rationale for developing inhibitory RNA strategies for therapeutic purposes in cancer

patients 32

.In the current study, we investigated global microRNA expression in a large series of

genetically characterized PAs, including various pathologic subtypes, followed by

characterization of possible relevant biologic targets.

Methods

Patients and tumor samples

A total of 43 PAs were obtained from patients undergoing surgery at Johns Hopkins Hospital,

New York University or Mayo Clinic, including 35 sporadic WHO grade I PAs, 4 NF1-

associated PAs and 4 PAs with pilomyxoid features. All patients (except for 2) were 18 years or

younger at the time of surgery (median 10 years). Non-neoplastic tissue controls included fetal

cerebellum (n=1) pediatric cerebellum (n=1) obtained from autopsy, as well as cerebral cortex

obtained during seizure surgery (n=2) or biopsy for a non-specific neurologic disorder with

gliosis (n=1). Nine additional autopsy-derived cerebellar tissues were included for qRT-PCR

experiments.

Anatomic locations for tumor samples included optic pathways (n=10), supratentorial

(n=11), and infratentorial compartments (n=22). Most tumors were previously evaluated for

BRAF alterations as part of a separate study and by PCR and sequencing as reported7. Tumors

were classified as aggressive if they progressed significantly within a year despite conventional

therapies, or recurred after gross total resections. Five patients met these criteria. Patient and

tumor data is summarized in table 1. All studies were approved by the Johns Hopkins, NYU and

Mayo Clinic Institutional Review boards.

MicroRNA profiling

Snap frozen fresh tumor tissue was used for the microRNA studies. Sample quality assessment

and microarray analysis were performed at the Sidney Kimmel Comprehensive Cancer Center

Microarray Core Facility at Johns Hopkins University, Baltimore. Briefly, total RNA was

isolated using miRNeasy Mini kits (Qiagen, Valencia, CA) followed by quality checks of both

total RNA and small RNA using a 2100 Bioanalyzer and software which detect 28S and 18S

ribosomal RNA ratio, total RNA Integrity Number (RIN), small RNA and miRNA

concentrations in the total RNA isolated. Only samples with adequate total and microRNA were

used in the study.

Samples were hybridized to a Human miRNA Microarray V3 kit (G4470C, Agilent

Technologies, Santa Clara, CA) platform, previously proven to produce reproducible results as

described 33

. This array contains 866 human and 89 human viral microRNAs from the Sanger

database v12.0. (URL:http://microrna.sanger.ac.uk/sequences/). Each miRNA species is printed

20 times with replicate probes on the array. Total RNA (150 ng) was first dephosphorylated with

11.2 units of calf intestine alkaline phosphatase at 37°C for 30 minutes and followed by end-

labeling with pCp-Cy3 (Agilent Technologies) and 15 units of T4 RNA ligase (GE Healthcare,

Little Chalfont, United Kingdom) at 16°C for 2 hours. Labeled samples were purified with Micro

Bio-Spin 6 columns (Bio-Rad, Hercules, CA). Labeling efficiency and nucleic acid concentration

were measured using Nanodrop 1000. Samples were then mixed with 10x blocking agent and 2x

Hi-RPM hybridization buffer (Agilent Technologies) and hybridizations were carried out at 55°C

with rotation at 20 rpm in a designated Agilent G2545A hybridization oven for 20 hours. Finally,

microarrays were washed and scanned using an Agilent scanner controlled by Agilent Scan

Control 7.0 software. Data were acquired with Agilent Feature Extraction 9.5.3.1 software for

miRNA microarray generating a GeneView file that contains summarized signal intensities for

each miRNA by combining intensities of replicate probes and background subtraction.

MicroRNA Data analysis

Data normalization and analysis were performed using GeneSpring GX 11 following the

standard software recommendations (Agilent Technologies). Briefly, miRNA signal intensities

from GeneView files were subjected to quantile normalization. Expression differences were

compared using the T Test unpaired unequal variance (Welch) between pairs of interest. An

adjusted p value was obtained using the Benjamini-Hochberg false discover rate (FDR) for

multiple comparisons.

MicroRNA validation

Validation of selected targets (mature miR-21, miR-124, mir-129) was performed with Taqman®

microRNA assays (Applied Biosystems, Foster City, CA) using the manufacturers

recommendations. All samples were tested in triplicate using 96 well plates. The non-coding

small nuclear RNA U6 was used as internal control. Wells lacking template were used as

negative controls. Expression changes were compared by relative quantification in the form of

fold changes obtained with the ΔΔCt method34

.

MicroRNA target prediction

Predicted mRNA targets for differentially expressed microRNAs were obtained from the

TargetScan Human online database (Release 6.0). Using this strategy, microRNA targets are

predicted by matching the seed region of the specific microRNA with conserved 8mer and 7mer

sites as described35

(http://www.targetscan.org).

http://www.targetscan.org/

Analysis of Functional Annotation (AFA) results

We tested the association between the lists of mRNA targets for selected differentially miRs and

relevant cancer signaling pathways available from the NCI Pathway Commons database via

Analysis of Functional Annotation, as previously described36

. Briefly, the chi-squared test was

used to identify the specific signaling pathways significantly over-represented among the genes

targeted by the microRNAs differentially expressed in PA. The Benjamini-Hochberg method

was applied to correct the obtained P-values for multiple testing, and a FDR of less than 5% was

considered significant.

Gene Expression analysis

Differences in candidate mRNA targets were studied using data files obtained with Affymetrix

HG-U133 Plus 2.0 chips. Sources included newly profiled 17 sporadic PAs (5 of which subjected

to microRNA analysis as well) and one non-neoplastic cortex; 47 PA samples from our

previously reported study37

, and 19 normal brain samples from various anatomic regions

(cerebral cortex (n=4), occipital lobe (n=4), cerebellum (n=3), and hypothalamus (n=8) obtained

from a publically available dataset at the NCBI gene expression omnibus(GSE7307). Raw gene

expression data preprocessing and normalization was performed at the probe level using the

Frozen Robust Multi-array Analysis (FRMA) approach described by McCall and colleagues38

.

Tissue microarray (TMA) and Immunohistochemistry

Validation of protein targets was performed using three PA and one diffuse glioma TMAs

containing 103 sporadic PAs, 17 NF1-associated PAs, 15 anaplastic PAs, and 79 diffuse gliomas

of various grades. TMAs contained 3-4 cores per tumor. Immunohistochemical studies were

performed using the following antibodies: PBX3 (Clone 1A11, LifeSpan BioSciences Inc.,

Seattle, WA, 1:400), METAP2 (Rabbit polyclonal, Abnova, Taipei, Taiwan, 1:50), and NFIB

(Clone 2D6, LifeSpan BioSciences Inc., 1:100). Immunohistochemical stains were scored by two

independent observers (FJR, CH) using the following semiquantitative scale which combined

staining intensity and number of positive cells: 3+(strong immunoreactivity in>50% tumor cells),

2+( medium to weak immunoreactivity in 50-100% of cells or strong immunoreactivity in 10-

50% tumor cells), 1+(strong immunoreactivity in 1-10% or medium to weak immunoreactivity in

10-50% tumor cells), 0=negative. For PBX3 and NFIB only nuclear reactivity was considered

significant, while only cytoplasmic METAP2 reactivity was scored. Chi Square or Fisher exact

tests were used to compare proportions, and student t-test or Wilcoxon rank sum to compare

quantitative variables. All tests were two sided with p-values less than 0.05 considered

statistically significant.

Results

A subset of microRNAs are differentially expressed in pediatric PAs

Comparisons were made between tumors and non-neoplastic brain, as well as different tumor

subsets. The most robust differences were evident between tumors as a group and non-neoplastic

brain tissues, as illustrated by unsupervised hierarchichal clustering (Figure 1). The volcano plot

comparing tumor tissue and non-neoplastic brain using an adjusted p value of 0.05 and fold

change of 2 as cutoffs demonstrated 13 human microRNAs relatively underexpressed in tumors

(Table 2) and 20 overexpressed (Table 3)(Figure 2). Next, we tested a subset of the cases (9

sporadic PA, 5 non-neoplastic brains) for hsa-miR-124, hsa-miR-129, and hsa-miR-21 using

quantitative PCR. Average miR-124 expression fold change in tumor vs. brain was -17

(p=0.006), miR-129: -15 (p=0.009), and miR-21: 19.8 (p=0.003), respectively (Figure 3A,B.C).

Next, we expanded the control group to include 9 additional non-neoplastic cerebellar examples,

and also tested the 4 NF1-PA. The lowest expression of miR-124 and miR-129 was actually

present in the NF1-PA group, compared to sporadic PA and non-neoplastic brain, while the

converse was true for miR-21 (Figure 3D,E,F). These last differences were also present in the

initial Agilent array profiling experiments (not shown).

Differentially expressed microRNA in NF1 associated vs. BRAF fusion positive tumors

We also searched for specific differences in hsa-microRNA expression among different PA

groups, in particular by anatomic location, pathologic subtype, genetic background, and clinical

aggressiveness, although the differences were not as robust as between tumor and non-neoplastic

brain. A volcano plot using an adjusted p value of 0.05 and fold change of 2 demonstrated 4

microRNAs differentially expressed between NF1-associated and tumors with BRAF alterations:

hsa-miR-650 and hsa-miR-1276 were differentially overexpressed in NF1 tumors (3.7 fold),

while hsa-miR-744* and hsa-miR-187

* were underexpressed (2 and 2.5 fold). However, the

overall expression levels of these microRNAs were relatively low, and further validation of miR-

650 by qRT-PCR was non contributory (data not shown). These findings suggest that expression

differences between NF1-PA and sporadic PA may exist at the miRNA level in addition to the

mRNA level as previously reported37

.

Biologically relevant microRNA targets in PAs

We used the online program TargetScan to discover target mRNAs for microRNAs

differentially expressed in PAs. A total of 36 mRNAs were predicted targets of 4 or more

microRNAs differentially underexpressed in PAs compared to brain, several encoding known

and putative oncoproteins (Table 4), while 10 mRNAs were predicted targets for microRNAs

differentially overexpressed in PAs (Table 5). We searched for these predicted mRNAs in a

combined dataset containing a total of 64 PA and 20 normal brain samples representing different

anatomical regions. Most of these predicted targets were overexpressed in PA compared to

normal brain, and unsupervised clustering demonstrated clear separation between tumor and

brain samples (Figure 4). Conversely, none of the mRNAs predicted targets of overexpressed

microRNAs in PA were underexpressed. When searching for genes differentially expressed in

NF1 vs. BRAF altered tumors, Targetscan search revealed 7 genes that were predicted targets of

the overexpressed microRNAs (miR-650 and miR-1276)(Table 6). There were no combined

predicted targets of -miR-744 and hsa-miR-187.

Next, we performed Analysis of Functional Annotation to compare microRNA PA targets

to signaling pathways. Interestingly, many of the gene sets identified for two of the microRNAs

with the lowest differential expression (miR-124 and miR-129-5p) targeted multiple components

of receptor tyrosine kinase/MAPK/ERK signaling pathways (Supplementary tables 1 and 2).

Since microRNAs may have a more important role in interfering with protein translation,

we further tested candidate targets by immunohistochemistry using commercially available

antibodies. From the cases studied for microRNA expression by qRT-PCR, paraffin sections

were available for 5 tumors and 3 cerebral cortex controls. Most of these cases underexpressed

miR-124 (-3.5 to -67 fold) and miR-129 (-1.33 to -772.16 fold) compared to normal brain. In this

subset 5 (of 5) tumor samples showed increased (moderate to marked expression) of the

corresponding predicted targets PBX3 and NFIB, while 2 (of 5) tumors showed increased

expression of METAP2. In this limited dataset, the tightest inverse correlation was noted

between miR-124 expression and NFIB IHC (R2 =0.47, p=0.05), followed by miR-129 and NFIB

(R2=0.30) and miR-129 and PBX3 (R

2=0.23). A linear correlation between miR-124 and

METAP2 was not observed (R2=0.006).

Next, we searched for expression differences of these proteins in a large set of

independent tumors obtained from four different microarrays. We identified strong (3+)PBX3

nuclear staining in most PAs, independent of subtype when compared to non-neoplastic brain

and diffuse gliomas (p<0.001) (Figure 5)(Table 7). Cytoplasmic METAP2 was expressed most

strongly in NF1-associated PAs compared to sporadic PAs (p<0.001), and less so in sporadic

examples, which in turn were mildly overexpressed compared to diffuse gliomas and non-

neoplastic brain(p=0.001) (Figure 5)(Table 7). Strong (3+) nuclear NFIB labeling was also

highest in anaplastic PAs followed by sporadic PAs, compared to non-neoplastic brain and

diffuse gliomas (p<0.001). The kappa statistic comparing the scores from each observer were

0.25 (PBX3), 0.26 (NFIB) and 0.37 (METAP2) reflecting fair agreement on each. Importantly,

the major differences between the groups were also reflected in the individual scores from each

observer (data not shown).

Discussion

Numerous roles have been proposed and studied for microRNA in physiologic and pathologic

states, including cancer. MicroRNA regulation has been a fruitful area of research in brain

cancer, particularly glioblastoma. In fact, extensive post-transcriptional regulatory networks in

this specific tumor type, where microRNAs are key components, have emerged using

bioinformatic and experimental approaches20

.

However, less is known about microRNA regulation of low grade gliomas, and PAs in

particular. MicroRNA and mRNA profiling studies using 4 PA samples have demonstrated

separate clustering for PAs from other pediatric brain tumors28,29

. In a study of WHO grade I–III

astrocytomas, Li et al. identified a number of microRNAs to be underexpressed in all glioma

grades compared to brain, including miR-12421

, which in our study was differentially

underexpressed as well.

miR-21 seems to target multiple components of key tumor suppressor and anti-apoptotic

pathways, including p53, transforming growth factor beta, and mitochondria 39-42

, as well as

matrix metalloproteinases, which contribute to invasion in glioma43

. Elevation of miR-21 has

been found in a variety of tumor types compared to normal tissues, and highlights the potential

of microRNA to serve as glioma biomarkers, for example in cerebrospinal fluid samples44

. One

important (but not exclusive) target, of miR-21 is PTEN, a key suppressor of the

PI3K/AKT/mTOR pathway. PTEN loss is a frequent molecular property of high grade gliomas,

and we have previously observed decreased levels in PAs with aggressive histologic features45

.

One of the main patterns identified in the current study is relative underexpression of a

subset of microRNAs in PAs compared to non-neoplastic brain, several of which may target

known and putative oncogenes. MiR-124 is enriched in brain tissue, is downregulated in

glioblastoma, and negatively affects glioblastoma proliferation and migration in vitro21,46

.

Several miR-129 cluster members were underexpressed in PAs in our study. Other investigators

have also found this microRNA to be underexpressed in pediatric brain tumors, including four

PAs28

.

We also found increased gene expression and protein levels of putative oncogenes that

may be important in the biology of PA, and appeared to be targets of microRNA sets relatively

underexpressed in PAs compared to non-neoplastic brain. This is a novel aspect of our study,

since these oncoproteins have not been associated with PA before, but may provide insights into

low grade glioma biology. Many oncoproteins in pediatric brain tumors play essential roles

during central nervous system development. For example, PBX3 (pre-B-cell leukemia homeobox

3) is one member of a group of PBX transcription factors belonging to the TALE (three amino

acid loop extension) homeobox gene family. They seem to play important developmental roles

and affect gene signatures associated with cancers, particularly of hematolymphoid origin. In the

nervous system PBX3 is expressed in many anatomical regions during development, including

areas of the medulla oblongata responsible for respiration control47

. In fact, Pbx3 null mice die

from central respiratory failure47

. Pbx3 also seems to be important in the development of a subset

of glutaminergic neurons in the developing dorsal horn of the spinal cord in murine studies48

, as

well as the striatum and intercalated cell masses of the amygdala in monkey and rat49,50

.

Recent studies have described post-transcriptional regulation of PBX3 in cancer,

particularly by microRNAs. Downregulation of miR-181a and upregulation of a gene expression

signature that includes PBX3, was associated with an adverse outcome in cytogenetically

abnormal acute myeloid leukemia51

. However, PBX3 has been found to play a role in non

lymphoid malignancies as well, including prostate cancer, where it may be regulated by a

different microRNA (e.g. Let 7d) in response to androgen52

. In the current study we identified

underexpression of miR-181a and increased PBX3 mRNA and protein levels in PAs compared to

normal brain. Furthermore, PBX3 nuclear staining was higher in PAs compared to diffuse

gliomas, which raises the possibility that PBX3 may play a relatively specific role in PAs

compared to other gliomas. However, this finding will require further confirmation by alternative

methods, functional experiments and independent samples in the future.

In the current study a subset of underexpressed microRNAs also had methionine

aminopeptidase 2 (METAP2) as a predicted target. METAP2 functions include facilitation of

protein translation53

, probably by protecting eukaryotic initiation factor 2 from inhibition by

phosphorylation54]. When evaluating protein levels by immunohistochemistry, the highest levels

were present in NF1-associated PAs, with a more modest elevation in some sporadic PAs. Of

relevance to our present, METAP2 was identified to be elevated in the cerebrospinal fluid of

mice with optic gliomas, and NF1-associated PAs in human and murine optic glioma tissue54

. In

the same study, METAP2 was not overexpressed in Tsc1-deficient mouse brains or TSC-

associated tumors, nor was it overexpressed in the small number of sporadic PA we examined,

and therefore increased levels were a consequence of neurofibromin loss and a property of NF1-

associated tumors. METAP2 represents an attractive therapeutic target, since it is inactivated by

fumagillin, a known fungal toxin55

. METAP2 mRNA and/or protein overexpression has been

reported in several cancers, including cholangiocarcinoma56

, colorectal carcinoma57

, and

mesothelioma58

. Fumagillin also inhibits hepatocellular carcinoma growth in vivo59

, and

pharmacologic inhibition of METAP2 also inhibits melanoma growth60

.

Another predicted target of a subset of underexpressed microRNAs with increased

mRNA and protein levels in PAs was NFIB. NFIB, a CCAAT box-binding transcription factor,

has been implicated as an oncogene in other tumors, being amplified in small cell carcinoma of

the lung61

, overexpressed in breast cancer subsets62

, and is involved in a novel fusion in salivary

gland tumors and adenoid cystic carcinoma of the breast63,64

. Additionally, NFIB functionally

validated as a target of miR-124, with a number of proteins being downregulated by miR-124

introduction in cultured HeLa cells at the protein and mRNA level, including NFIB65

.

Interestingly, we saw an inverse correlation between miR-124 and NFIB extent of expression in

the limited number of cases tested (5 S-PA and 3 brain controls).

Our results highlight the complementary value of microRNA profiling in the

identification of biologically relevant targets in pediatric low grade glioma. The most notable

differences were between PA tumors and pediatric brain tissues. As a cautionary note, it must be

noted that for the initial profiling experiments, 3 (of 5) of the non-neoplastic brain samples were

obtained from cortex, while most PA arise in the cerebellum or in the optic pathways. We did

find the same microRNA expression differences in additional cerebellar samples by qRT-PCR,

but unfortunately we were unable to test optic pathway normal samples, given the difficulties in

obtaining them as normal controls.

Modest differences between different tumor subsets, by location, histologic and

molecular subtypes, were also observed but to a lesser extent. By looking at combined fold

change differences and statistical significance, asubset of microRNAs were differentially

expressed in NF1-associated PA compared to tumors with BRAF alterations. In addition, the

expression differences for miR-21, miR-124, and miR-129 that were subsequently validated by

qRT-PCR were more conspicuous in the NF1-PA group. However, given the relatively small

number (4 tumors) tested in this study, the findings must be interpreted with caution and must be

validated in the future in larger tumor datasets.

Prior mRNA gene expression studies which have identified clear molecular differences in

PAs by anatomic site and NF1 status37,66

, as well as specific transcriptional regulatory networks

for PA67

. Of note, in a recent study repressed expression of ONECUT2 was part of a PA specific

transcriptional network67

, and we identified it as a predicted target of two microRNAs

differentially overexpressed in NF1-associated PA.

Our study has revealed some novel, putative oncogene targets that may be specifically

upregulated in PA compared to brain and diffuse gliomas. However, it is possible that specific

microRNAs in PA may be regulated (or regulate) by canonical pathways that have been

validated to play key roles in PA biology. One such pathway is MAPK, which is constitutively

activated in the vast majority of PA by rearrangements and/or mutations involving BRAF and its

family members3,5,6,8,9,12

. Interestingly, miR-21 is up-regulated by increased MAPK activity in

breast cancer68

, as well as in v-Ki-ras transformed NIH3T3 (DT) cells (compared to

nontransformed NIH3T3 cells)69

, and therefore acts as a downstream effector in this pathway. As

mentioned above, miR-124 is enriched in brain, and is evolutionarily conserved even in other

species, including snails. In a study focusing on the marine snail Aplysia, serotonin

downregulates miR-124, an effect that is abolished by MAPK inhibition70

, suggesting that

MAPK is a negative regulator of miR-124. Interestingly, through our analysis of functional

annotation analysis we found multiple sets of receptor tyrosine kinase/MAPK/ERK signaling

components as possible targets. Collectively, these prior observations and our findings raise the

intriguing possibility that MAPK pathway activation leads to alterations in key microRNA

levels, that these microRNAs may be important downstream mediators of the pathway in PA,

and may also themselves regulate pathway components. This interesting possibility merits

further study.

In summary, we have identified molecular differences based on microRNA expression

levels in a large cohort of pediatric PAs compared with human brain samples. Some investigators

have highlighted current limitations in microRNA profiling in pediatric brain tumors29

, which

may require platforms with larger microRNA coverage and increased samples sizes. However, in

the current study we demonstrate the feasibility in microRNA profiling as a novel approach to

identify relevant targets in PA. Furthermore, we have identified increases in protein levels of

putative oncogenes that may be of relevance to PA biology. At the present time suitable in vivo

or in vitro models to follow these observations are lacking but may become available in the near

future. Additional studies are needed to confirm our findings, address the possible functional

consequences and clinical relevance for pathologic diagnosis and management of PA patients.

Acknowledgements

The authors also acknowledge Wayne Yu at the Sidney Kimmel Cancer Center Microarray Core

Facility of Johns Hopkins for excellent technical assistance.

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Figure Legends

Figure 1: A subset of microRNAs are differentially expressed between pilocytic

astrocytomas (PAs) and non-neoplastic brain. Heatmap illustrates expression differences

between PAs and non-neoplastic brain as illustrated by hierarchical clustering. The non-

neoplastic brain controls, representing cortex (C1-3), as well as fetal (C4) and pediatric

cerebellum (C5)(box). Data was obtained using the Agilent miRNA Microarray V3 kit platform.

Figure 2: Differential microRNA expression in PAs compared with non-neoplastic brain.

Volcano plot illustrates microRNA expression differences between PA and non-neoplastic brain

based on a cutoff value of 2 fold differential expression and adjusted p value <0.05.

Figure 3: Differential expression of microRNAs between PAs and non-neoplastic brain

confirmed by qRT-PCR. miR-124 (A) and miR-129 (B) were relatively underexpressed in

sporadic PA (n=9) compared to non-neoplastic brain (pediatric cerebellum (n=1), cortex (n=3),

and fetal cerebellum (n=1)), while miR-21 was relatively overexpressed (C) in an internal

validation experiment. Additionally, independent non-neoplastic cerebellar samples and the 4

NF1-associated PA were analyzed. The lowest expression of miR-124 (D) and miR-129 (E) was

evident in NF1-PA (n=4), compared to sporadic PA (n=9) and non-neoplastic brain controls

(cortex n=3, cerebellum n=11). An opposite effect was observed with miR-21 (F). Each dot

represents the average of three replicates. Error bars are illustrated. p values were obtained by the

Wilcoxon rank sum test.

Figure 4: microRNA predicted mRNA target differences in PAs and non-neoplastic brain

samples. Unsupervised hierarchical clustering of normal brain and PA cases according to the

expression of the genes targeted by the identified microRNAs down-regulated in PA (profiling

by Affymetrix HG-U133 Plus 2.0 chips). Target genes are shown by row, while the different

samples are shown by column. A total of 64 PAs were studied. Non-neoplastic controls included

cerebral cortex (CC)(n=9), hypothalamus (n=8), and cerebellum (Cbll)(n=3). The centered

Pearson’s distance and the Ward’s clustering methods were used. The color scale represents

increased (red) or decreased (green) gene expression.

Figure 5: microRNA protein target differences in PAs and non-neoplastic brain samples.

Proteins that were predicted to be targets of differentially underexpressed microRNAs in PAs,

included putative oncogenes such as PBX3, METAP2 and NFIB. Using immunohistochemistry

on tissue microarray sections, PBX3 and NFIB demonstrated strong nuclear staining in PAs

compared to brain, while METAP2 demonstrated modest cytoplasmic staining in a subset of

PAs, and was strongest in NF1-associated PAs.

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Table 1: Patients and tumor characteristics

Case Age

(years)

Diagnosis Location Clinically

Aggressive

Genetic

alteration

1 6 Sporadic PA L FRONTAL

LOBE

Yes No BRAF fusiona

2 15 Sporadic PA BRAINSTEM No No BRAF fusiona

3 13 Sporadic PA CEREBELLUM No KIAA1549:BRAF

fusion

4 17 Sporadic PA MEDULLA No No BRAF fusiona

5 16 Sporadic PA L TEMPORAL No No BRAF fusiona


fusion

7 5 Sporadic PA CEREBELLUM Yes No BRAF fusiona


fusion

9 16 Sporadic PA POSTERIOR

FOSSA

No No BRAF fusiona

10 10 Sporadic PA CEREBELLUM NA KIAA1549:BRAF

fusion


fusion


fusion

tables

13 11 Sporadic PA CEREBELLUM No None


fusion


fusion

16 25 Sporadic PA OPTIC CHIASM No KIAA1549:BRAF

fusion


fusion

18 5 Sporadic PA BRAINSTEM No KIAA1549:BRAF

fusion

19 4 Sporadic PA POSTERIOR

FOSSA

No KIAA1549:BRAF

fusion

20 9 Sporadic PA CEREBELLUM No None

21 20 Sporadic PA THALAMUS Yes None

22 10 Sporadic PA CERVICAL No KIAA1549:BRAF

fusion

23 9 Sporadic PA OPTIC TRACT Yes KIAA1549:BRAF

fusion

24 16 Sporadic PA TECTUM No KIAA1549:BRAF

fusion

25 5 Sporadic PA THALAMUS No KIAA1549:BRAF

fusion

26 4 Sporadic PA HYPOTHALMUS Yes None

27 7 Sporadic PA THALAMUS No KIAA1549:BRAF

fusion

28 6 Sporadic PA CEREBELLUM NA None

29 7 Sporadic PA MIDBRAIN/

THALAMUS

No None

30 17 Sporadic PA L TEMPORAL

LOBE

No None

31 10 Sporadic PA HYPOTHALMUS No KIAA1549:BRAF

fusion

32 11 Sporadic PA HYPOTHALMUS NA None

33 4 Sporadic PA POST FPSSA NA None

34 12 Sporadic PA THALMAUS NA None

35 1 Sporadic PA CEREBELLUM No BRAFV600E

36 13 NF1-PA R FRONTAL

LOBE

No NF1 (clinical)

37 2 NF1-PA OPTIC NERVE No NF1 (clinical)

38 18 NF1-PA OPTIC TRACT No NF1 (clinical)

39 18 NF1-PA THALAMUS NA NF1 (clinical)

40 2 Pilomyxoid

features

OPTIC PATHWAY No None

41 2 Pilomyxoid

features

THALAMUS No None

42 4 Pilomyxoid

features

HYPOTHALAMUS NA KIAA1549:BRAF

fusion

43 1 Pilomyxoid

features

HYPOTHALMUS NA KIAA1549:BRAF

fusion

C1 18 Pediatric

cortex

FRONTAL LOBE ND

C2 26 Cortex L TEMPORAL

LOBE

ND

C3 4 Pediatric

cortex

TEMPORAL LOBE ND

C4 NA Fetal brain CEREBELLUM ND

C5 NA Pediatric

cerebellum

CEREBELLUM ND

aBRAFV600E not evaluated; NA=not available; ND=not done

Table 2: MicroRNAs relatively underexpressed in tumor tissue compared with non-

neoplastic brain

SystematicName

Corrected

p-value p-value

Fold

Change

hsa-miR-124 0.02 4.39E-04 67.40

hsa-miR-129* 0.04 0.001513 22.60

hsa-miR-129-3p 6.50E-04 4.70E-06 54.25

hsa-miR-129-5p 0.006 6.74E-05 35.07

hsa-miR-138-2* 0.01 2.01E-04 2.83

hsa-miR-181a 0.04 0.001238 2.95

hsa-miR-323-3p 0.03 9.26E-04 2.53

hsa-miR-329 0.04 0.001171 2.48

hsa-miR-383 0.02 3.58E-04 14.71

hsa-miR-490-3p 0.006 7.30E-05 10.76

hsa-miR-490-5p 0.05 0.002093 2.75

hsa-miR-7 0.02 6.42E-04 20.54

hsa-miR-873 0.02 4.77E-04 26.30

Table 3: MicroRNAs relatively overexpressed in tumor tissue compared with non-

neoplastic brain

SystematicName

Corrected

p-value p-value

Fold

change

hsa-let-7c* 6.50E-04 4.74E-06 2.69

hsa-miR-10b 2.60E-10 2.71E-13 17.79

hsa-miR-10b* 0.02 4.75E-04 3.58

hsa-miR-1260 0.004 3.77E-05 2.50

hsa-miR-1274a 0.02 5.10E-04 2.17

hsa-miR-1288 0.01 2.29E-04 3.28

hsa-miR-142-3p 0.02 4.24E-04 4.65

hsa-miR-143* 4.65E-04 1.45E-06 2.02

hsa-miR-146b-5p 0.04 0.001 2.42

hsa-miR-155 6.50E-04 4.72E-06 4.08

hsa-miR-21 0.04 0.002 18.55

hsa-miR-21* 0.01 2.25E-04 30.37

hsa-miR-23a 0.02 5.76E-04 3.21

hsa-miR-542-5p 0.04 0.001 4.62

hsa-miR-650 0.03 8.54E-04 3.71

hsa-miR-886-3p 0.004 4.64E-05 5.53

hsa-miR-92b 0.04 0.001 2.07

hsa-miR-92b* 3.73E-08 7.77E-11 2.25

Table 4: Predicted Targetscan protein targets by microRNAs underexpressed in pilocytic

astrocytoma (PA)

Target

gene

Gene name

Representative microRNA

BACH2

BTB and CNC homology 1,

basic leucine zipper

transcription factor 2 miR-124 miR-129-5p miR-323-3p miR-490-3p

BCL7A B-cell CLL/lymphoma 7A miR-124 miR-129-3p miR-329 miR-873

BMPR2

bone morphogenetic protein

receptor, type II

(serine/threonine kinase) miR-129-3p miR-129-5p miR-181a miR-329

miR-

490-5p

miR-

873

BRWD1

bromodomain and WD repeat

domain containing 1 miR-124 miR-129-3p miR-129-5p miR-7

DDX3X

DEAD (Asp-Glu-Ala-Asp)

box polypeptide 3, X-linked hsa-miR-124 miR-129-5p miR-181a miR-323-3p

FAM135B

family with sequence

similarity 135, member B -miR-124 miR-129-3p miR-129-5p miR-490-3p

FOXN3 forkhead box N3 miR-129-5p miR-323-3p miR-329 miR-383 miR-7

G3BP2

GTPase activating protein

(SH3 domain) binding

protein 2 miR-124 miR-181a miR-323-3p miR-7

GLIS3 GLIS family zinc finger 3 miR-129-5p miR-181a miR-323-3p miR-383

HIC2 hypermethylated in cancer 2 miR-129-5p miR-181a miR-490-3p miR-873

HIPK2

homeodomain interacting

protein kinase 2 miR-124 miR-129-3p miR-181a miR-490-5p miR-7

HNRNPA1

heterogeneous nuclear

ribonucleoprotein A1 miR-129-5p miR-323-3p miR-383 miR-490-3p

KIAA0182 KIAA0182 miR-129-3p miR-129-5p miR-181a miR-7

KIAA2018 KIAA2018 miR-124 miR-129-5p miR-181a miR-7

KLF12 Kruppel-like factor 12 miR-129-3p miR-323-3p miR-329 miR-7

KLHL28 kelch-like 28 (Drosophila) miR-124 miR-129-3p miR-129-5p miR-7

KPNA6

karyopherin alpha 6

(importin alpha 7) miR-129-5p miR-490-5p miR-7 miR-873

MECP2

methyl CpG binding protein

2 (Rett syndrome) miR-129-5p miR-181a miR-7 miR-873

METAP2 methionyl aminopeptidase 2 miR-124 miR-181a miR-323-3p miR-490-5p

MOBKL1A

MOB1, Mps One Binder

kinase activator-like 1A

(yeast) miR-124 miR-323-3p miR-490-3p miR-7

NFIB

nuclear factor I/B

miR-124 miR-129-5p miR-323-3p miR-490-5p miR-7

miR-

873

NR2C2

nuclear receptor subfamily 2,

group C, member 2 miR-129-5p miR-181a miR-329 miR-490-3p

PBX3 pre-B-cell leukemia miR-129-5p miR-181a miR-323-3p miR-7

homeobox 3

PHF21A PHD finger protein 21A miR-129-3p miR-129-5p miR-383 miR-7

PRPF40A

PRP40 pre-mRNA

processing factor 40 homolog

A (S. cerevisiae) miR-124 miR-129-5p miR-323-3p miR-490-5p

PTAR1

protein prenyltransferase

alpha subunit repeat

containing 1 miR-124 miR-129-3p miR-129-5p miR-329 miR-7

SFRS18

splicing factor,

arginine/serine-rich 18 miR-129-3p miR-181a miR-323-3p miR-490-5p

SP1 Sp1 transcription factor miR-124 miR-181a miR-329 miR-7

ST8SIA4

ST8 alpha-N-acetyl-

neuraminide alpha-2,8-

sialyltransferase 4 miR-124 miR-129-3p miR-181a miR-383

TET3

tet oncogene family member

3 miR-124 miR-129-3p miR-490-3p miR-873

TNRC6B

trinucleotide repeat

containing 6B miR-124 miR-129-3p miR-129-5p miR-181a

miR-

323-3p

miR-

7

TRPS1

trichorhinophalangeal

syndrome I

miR-124 miR-129-3p miR-129-5p miR-323-3p miR-383

miR-

490-

5p

WIPF2

WAS/WASL interacting

protein family, member 2 miR-124 miR-329 miR-383 miR-7

XYLT1 xylosyltransferase I miR-124 miR-129-3p miR-181a miR-323-3p

ZNF148 zinc finger protein 148 miR-124 miR-129-3p miR-323-3p miR-7

ZNF395 zinc finger protein 395 miR-129-5p miR-323-3p miR-490-3p miR-7

Table 5: Predicted Targetscan protein targets of microRNAs overexpressed in pilocytic

astrocytoma

Target

gene

Gene name Representative miRNA

ABL2

v-abl Abelson murine

leukemia viral oncogene

homolog 2 (arg, Abelson-

related gene) miR-1274a miR-142-3p miR-143 miR-23a

CBL

Cas-Br-M (murine) ecotropic

retroviral transforming

sequence miR-1288 miR-143 miR-155 miR-650

GATAD2B

GATA zinc finger domain

containing 2B miR-1260 miR-155 miR-21 miR-650

KCNA1

potassium voltage-gated

channel, shaker-related

subfamily, member 1

(episodic ataxia with

myokymia) miR-1274a miR-155 miR-21 miR-650

MLL2

myeloid/lymphoid or mixed-

lineage leukemia 2 miR-1260 miR-143 miR-146b-5p miR-23a

NFIX

nuclear factor I/X (CCAAT-

binding transcription factor) miR-10b miR-1260 miR-23a miR-886-3p

NOVA1

neuro-oncological ventral

antigen 1 miR-1274a miR-143 miR-146b-5p miR-155

PURB

purine-rich element binding

protein B let-7c miR-142-3p miR-21 miR-23a

SLC39A10

solute carrier family 39 (zinc

transporter), member 10 miR-142-3p miR-143 miR-155 miR-23a

TRPS1

trichorhinophalangeal

syndrome I miR-1274a miR-143 miR-155 miR-23a

Table 6: Predicted Target scan protein targets of microRNAs overexpressed in NF1-PA vs.

PA with BRAF alterations

Target

gene Gene name Representative miRNA

ANP32E

acidic (leucine-rich) nuclear

phosphoprotein 32 family, member E miR-1276 miR-650

CTNND1

catenin (cadherin-associated protein),

delta 1 miR-1276 miR-650

KPNA6 karyopherin alpha 6 (importin alpha 7) miR-1276 miR-650

NFASC neurofascin homolog (chicken) miR-1276 miR-650

NUDT10

nudix (nucleoside diphosphate linked

moiety X)-type motif 10 miR-1276 miR-650

ONECUT2 one cut homeobox 2 miR-1276 miR-650

RYBP RING1 and YY1 binding protein miR-1276 miR-650

Table 7: Differential expression of putative microRNA targets in pilocytic astrocytoma

(PA) identified by immunohistochemistry

Protein

target

Staining

pattern

Sporadic

PA

NF1-PA Anaplastic

PA

Diffuse

Glioma

Non-

neoplastic

brain

PBX3a

Nuclear 57/99 (58%) 11/16

(69%)

7/12 (58%) 5/79 (6%) 0/16

METAP2a

Cytoplasmic 25/102

(25%)

12/15

(80%)

6/13 (46%) 6/79 (4%) 0/17

NFIBa

Nuclear 30/95 (35%) 4/15 (27%) 5/12 (42%) 0/79 0/15

aNumber of tumors with strong diffuse expression (3+ scores)/total number (percentage)

representing the median estimate from two independent observers.

Analysis of functional annotation for miR-124 targets

Gene Set Collection: pathwayCommons.cell-map

Gene Set Name Value

EGFR1 0.00129

Gene Set Collection: pathwayCommons.nci-nature

Gene Set Name Value

VEGFR3 signaling in lymphatic endothelium 7.00E-06

RXR and RAR heterodimerization with other nuclear receptor 0.00041

IL6-mediated signaling events 0.0005

FGF signaling pathway 0.0005

Gene Set Collection: pathwayCommons.reactome

Gene Set Name Value

SHC-mediated cascade 1.13E-10

Downstream signaling of activated FGFR 2.10E-09

Axon guidance 2.55E-07

Signalling to ERKs 3.33E-07

Shc events in EGFR signaling 8.63E-07

SHC-related events 8.63E-07

PI-3K cascade 1.02E-06

Semaphorin interactions 1.11E-06

Activation myristolyation of BID and translocation to mitochondria 1.97E-06

FRS2-mediated cascade 1.97E-06

FGFR2c ligand binding and activation 1.97E-06

Signalling to RAS 2.51E-06

Fatty acid triacylglycerol and ketone body metabolism 4.11E-06

SHC-mediated signalling 4.82E-06

Tie2 Signaling 6.38E-06

Insulin receptor signalling cascade 9.97E-06

Signaling by Insulin receptor 1.15E-05

Signaling by FGFR 1.09E-05

Signaling by BMP 1.48E-05

ADP signalling through P2Y purinoceptor 12 2.31E-05

Signaling by EGFR 2.10E-05

Regulation of signaling by CBL 2.31E-05

IRS-mediated signalling 2.10E-05

FGFR4 ligand binding and activation 2.31E-05

FGFR3 ligand binding and activation 2.31E-05


FGFR3b ligand binding and activation 2.31E-05

Supplementary table 1


Signaling by PDGF 3.48E-05

Inositol transporters 3.41E-05

Beta oxidation of palmitoyl-CoA to myristoyl-CoA 3.41E-05

ERKs are inactivated 3.41E-05

Grb2 events in EGFR signaling 3.81E-05

SOS-mediated signalling 3.81E-05

IRS-related events 4.14E-05

NGF signalling via TRKA from the plasma membrane 4.42E-05

Sema4D in semaphorin signaling 6.53E-05

Caspase-mediated cleavage of cytoskeletal proteins 8.60E-05

Metabolism of lipids and lipoproteins 9.75E-05

Down-stream signal transduction 0.00011

Sema4D induced cell migration and growth-cone collapse 0.00012

G alpha (12/13) signalling events 0.00012

Neurofascin interactions 0.00013

FGFR1c and Klotho ligand binding and activation 0.00013

Sphingolipid metabolism 0.00017

Signalling by NGF 0.00017

5-Phosphoribose 1-diphosphate biosynthesis 0.0002

Opioid Signalling 0.00039

Adenylate cyclase inhibitory pathway 0.00053

Interleukin-6 signaling 0.00051

Inhibition of adenylate cyclase pathway 0.00053

mitochondrial fatty acid beta-oxidation of saturated fatty acids 0.00051

Notch receptor binds with a ligand 0.00051

ERK/MAPK targets 0.00053

Activation of PKB 0.00055

Frs2-mediated activation 0.00064

Apoptotic cleavage of cellular proteins 0.00077

Transmembrane transport of small molecules 0.00086

Prolonged ERK activation events 0.00108

Membrane Trafficking 0.00124

Signaling by TGF beta 0.00143

Transport of inorganic cations/anions and amino acids/oligopeptides 0.00141

Sodium/Proton exchangers 0.00143

Receptor-ligand binding initiates the second proteolytic cleavage of Notch receptor 0.00143

p38MAPK events 0.00143

Interleukin-3 5 and GM-CSF signaling 0.00185

GABA B receptor activation 0.00185

Activation of GABAB receptors 0.00185

Triglyceride Biosynthesis 0.00185

Signal amplification 0.00203

Signaling by Notch 0.00203

Activation of BMF and translocation to mitochondria 0.00298

AMPK inhibits chREBP transcriptional activation activity 0.00298

Beta oxidation of lauroyl-CoA to decanoyl-CoA-CoA 0.00298

Beta oxidation of decanoyl-CoA to octanoyl-CoA-CoA 0.00298

Beta oxidation of octanoyl-CoA to hexanoyl-CoA 0.00298

Beta oxidation of hexanoyl-CoA to butanoyl-CoA 0.00298

Axonal growth stimulation 0.00298

G alpha (z) signalling events 0.00269

G beta:gamma signalling through PLC beta 0.00298

Gab1 signalosome 0.00329

SLC-mediated transmembrane transport 0.00316

FGFR2b ligand binding and activation 0.00329

FGFR2 ligand binding and activation 0.00329

FGFR1b ligand binding and activation 0.00329

Other semaphorin interactions 0.00348

Synthesis and interconversion of nucleotide di- and triphosphates 0.00348

Nuclear Events (kinase and transcription factor activation) 0.00348

Gene Set SizeCorrected ValueRanking

137 0.01293 1


24 0.0013 1

26 0.0234 2

48 0.0234 2

48 0.0234 2


3 1.26E-07 1

12 1.17E-06 2

225 9.30E-05 3

30 9.30E-05 3

16 0.00016 5

16 0.00016 5

8 0.00016 5

64 0.00016 5

5 0.0002 9

5 0.0002 9

5 0.0002 9

22 0.00023 12

83 0.00035 13

13 0.00038 14

18 0.00048 15

56 0.0007 16

79 0.00071 17

24 0.00071 17

19 0.00087 19

6 0.00092 20

51 0.00092 20

6 0.00092 20

51 0.00092 20

6 0.00092 20

6 0.00092 20

6 0.00092 20

6 0.00092 20

6 0.00092 20

32 0.00122 29

3 0.00122 29

3 0.00122 29

3 0.00122 29

15 0.00125 33

15 0.00125 33

53 0.00132 35

110 0.00137 36

27 0.00197 37

11 0.00253 38

252 0.00279 39

28 0.00304 40

22 0.00308 41

22 0.00308 41

7 0.00343 43

7 0.00343 43

29 0.00424 45

146 0.00424 45

1 0.00469 47

53 0.00904 48

13 0.01102 49

8 0.01102 49

13 0.01102 49

8 0.01102 49

8 0.01102 49

13 0.01102 49

4 0.01118 55

19 0.01276 56

40 0.01503 57

367 0.01657 58

20 0.02052 59

84 0.02304 60

9 0.02462 61

94 0.02462 61

9 0.02462 61

9 0.02462 61

9 0.02462 61

28 0.02992 66

28 0.02992 66

28 0.02992 66

28 0.02992 66

15 0.03187 70

15 0.03187 70

5 0.04154 72

5 0.04154 72

5 0.04154 72

5 0.04154 72

5 0.04154 72

5 0.04154 72

5 0.04154 72

22 0.04154 72

5 0.04154 72

10 0.04317 81

248 0.04317 81

10 0.04317 81

10 0.04317 81

10 0.04317 81

16 0.04422 86

16 0.04422 86

16 0.04422 86

Description

CELL_MAP - EGFR1 gene set

Description

NCI_NATURE - VEGFR3 signaling in lymphatic endothelium gene set

NCI_NATURE - RXR and RAR heterodimerization with other nuclear receptor gene set

NCI_NATURE - IL6-mediated signaling events gene set

NCI_NATURE - FGF signaling pathway gene set

Description

REACTOME - SHC-mediated cascade gene set

REACTOME - Downstream signaling of activated FGFR gene set

REACTOME - Axon guidance gene set

REACTOME - Signalling to ERKs gene set

REACTOME - Shc events in EGFR signaling gene set

REACTOME - SHC-related events gene set

REACTOME - PI-3K cascade gene set

REACTOME - Semaphorin interactions gene set

REACTOME - Activation myristolyation of BID and translocation to mitochondria gene set

REACTOME - FRS2-mediated cascade gene set

REACTOME - FGFR2c ligand binding and activation gene set

REACTOME - Signalling to RAS gene set

REACTOME - Fatty acid triacylglycerol and ketone body metabolism gene set

REACTOME - SHC-mediated signalling gene set

REACTOME - Tie2 Signaling gene set

REACTOME - Insulin receptor signalling cascade gene set

REACTOME - Signaling by Insulin receptor gene set

REACTOME - Signaling by FGFR gene set

REACTOME - Signaling by BMP gene set

REACTOME - ADP signalling through P2Y purinoceptor 12 gene set

REACTOME - Signaling by EGFR gene set

REACTOME - Regulation of signaling by CBL gene set

REACTOME - IRS-mediated signalling gene set

REACTOME - FGFR4 ligand binding and activation gene set



REACTOME - FGFR3b ligand binding and activation gene set


REACTOME - Signaling by PDGF gene set

REACTOME - Inositol transporters gene set

REACTOME - Beta oxidation of palmitoyl-CoA to myristoyl-CoA gene set

REACTOME - ERKs are inactivated gene set

REACTOME - Grb2 events in EGFR signaling gene set

REACTOME - SOS-mediated signalling gene set

REACTOME - IRS-related events gene set

REACTOME - NGF signalling via TRKA from the plasma membrane gene set

REACTOME - Sema4D in semaphorin signaling gene set

REACTOME - Caspase-mediated cleavage of cytoskeletal proteins gene set

REACTOME - Metabolism of lipids and lipoproteins gene set

REACTOME - Down-stream signal transduction gene set

REACTOME - Sema4D induced cell migration and growth-cone collapse gene set

REACTOME - G alpha (12/13) signalling events gene set

REACTOME - Neurofascin interactions gene set

REACTOME - FGFR1c and Klotho ligand binding and activation gene set

REACTOME - Sphingolipid metabolism gene set

REACTOME - Signalling by NGF gene set

REACTOME - 5-Phosphoribose 1-diphosphate biosynthesis gene set

REACTOME - Opioid Signalling gene set

REACTOME - Adenylate cyclase inhibitory pathway gene set

REACTOME - Interleukin-6 signaling gene set

REACTOME - Inhibition of adenylate cyclase pathway gene set

REACTOME - mitochondrial fatty acid beta-oxidation of saturated fatty acids gene set

REACTOME - Notch receptor binds with a ligand gene set

REACTOME - ERK/MAPK targets gene set

REACTOME - Activation of PKB gene set

REACTOME - Frs2-mediated activation gene set

REACTOME - Apoptotic cleavage of cellular proteins gene set

REACTOME - Transmembrane transport of small molecules gene set

REACTOME - Prolonged ERK activation events gene set

REACTOME - Membrane Trafficking gene set

REACTOME - Signaling by TGF beta gene set

REACTOME - Transport of inorganic cations/anions and amino acids/oligopeptides gene set

REACTOME - Sodium/Proton exchangers gene set

REACTOME - Receptor-ligand binding initiates the second proteolytic cleavage of Notch receptor gene set

REACTOME - p38MAPK events gene set

REACTOME - Interleukin-3 5 and GM-CSF signaling gene set

REACTOME - GABA B receptor activation gene set

REACTOME - Activation of GABAB receptors gene set

REACTOME - Triglyceride Biosynthesis gene set

REACTOME - Signal amplification gene set

REACTOME - Signaling by Notch gene set

REACTOME - Activation of BMF and translocation to mitochondria gene set

REACTOME - AMPK inhibits chREBP transcriptional activation activity gene set

REACTOME - Beta oxidation of lauroyl-CoA to decanoyl-CoA-CoA gene set

REACTOME - Beta oxidation of decanoyl-CoA to octanoyl-CoA-CoA gene set

REACTOME - Beta oxidation of octanoyl-CoA to hexanoyl-CoA gene set

REACTOME - Beta oxidation of hexanoyl-CoA to butanoyl-CoA gene set

REACTOME - Axonal growth stimulation gene set

REACTOME - G alpha (z) signalling events gene set

REACTOME - G beta:gamma signalling through PLC beta gene set

REACTOME - Gab1 signalosome gene set

REACTOME - SLC-mediated transmembrane transport gene set




REACTOME - Other semaphorin interactions gene set

REACTOME - Synthesis and interconversion of nucleotide di- and triphosphates gene set

REACTOME - Nuclear Events (kinase and transcription factor activation) gene set

URL

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URL

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Analysis of Functional Annotation of miR-129-5p targets

Gene Set Name

glutamine biosynthesis I

spermine biosynthesis

spermidine biosynthesis

ceramide biosynthesis

pentose phosphate pathway (non-oxidative branch)

sphingomyelin metabolism

Gene Set Collection: pathwayCommons.nci-nature

Gene Set Name

S1P4 pathway

FGF signaling pathway

S1P5 pathway

VEGFR1 specific signals

Class IB PI3K non-lipid kinase events

Angiopoietin receptor Tie2-mediated signaling

Neurotrophic factor-mediated Trk receptor signaling

Signaling events mediated by VEGFR1 and VEGFR2

N-cadherin signaling events

Gene Set Collection: pathwayCommons.reactome

Arguments: refPop=ann, featureType=featureNames, colname=chisq.test, absolute=FALSE, testfunction=function

Gene Set Name

Phospholipase C-mediated cascade

Activation of CaMK IV

FRS2-mediated cascade

CaMK IV-mediated phosphorylation of CREB

MAP kinase cascade

Frs2-mediated activation

Prolonged ERK activation events

Downstream signaling of activated FGFR

CREB phosphorylation through the activation of CaMKK

ERK activation

SHC-mediated signalling

Grb2 events in EGFR signaling

SOS-mediated signalling

Astrocytic Glutamate-Glutamine Uptake And Metabolism

Neurotransmitter uptake and Metabolism In Glial Cells

phospho-PLA2 pathway

Entry of Influenza Virion into Host Cell via Endocytosis

Supplementary table 2

ERK2 activation

Shc events in EGFR signaling

SHC-related events

Axon guidance

NGF signalling via TRKA from the plasma membrane

Down-stream signal transduction

PI-3K cascade

Signalling to p38 via RIT and RIN

NCAM signaling for neurite out-growth

Signalling to ERKs

Destabilization of mRNA by Tristetraprolin (TTP)

PLC-gamma1 signalling

PECAM1 interactions

ARMS-mediated activation

Signaling by PDGF

Role of second messengers in netrin-1 signaling

Signaling by EGFR

Signalling to RAS

Collagen adhesion via Gp IV

Arachidonate production from DAG

Breakdown of the nuclear lamina

EGFR interacts with phospholipase C-gamma

ERKs are inactivated

Progressive trimming of alpha-1 2-linked mannose residues from Man9/8/7GlcNAc2 to produce Man5GlcNAc2

NCAM1 interactions

Signalling by NGF

PLC beta mediated events

Signaling by FGFR

G-protein mediated events

Advanced glycosylation endproduct receptor signaling

Ca-dependent events

Netrin-1 signaling

Collagen adhesion via alpha 2 beta 1 glycoprotein

Transcriptional Regulation of White Adipocyte Differentiation

Class C/3 (Metabotropic glutamate/pheromone receptors)

IRS-mediated signalling

Other semaphorin interactions

CRMPs in Sema3A signaling

Opioid Signalling

IRS-related events

Post NMDA receptor activation events

Activation of NMDA receptor upon glutamate binding and postsynaptic events

Beta-catenin phosphorylation cascade

CTLA4 inhibitory signaling

RNA Polymerase III Transcription Termination

S6K1 signalling

FGFR2c ligand binding and activation

RAF activation

N-glycan trimming and elongation in the cis-Golgi

Destabilization of mRNA by Butyrate Response Factor 1 (BRF1)

Value Gene Set SizeCorrected ValueRanking

3.60E-24 1 9.37E-22 1

1.19E-12 2 1.03E-10 2

1.19E-12 2 1.03E-10 2

1.24E-05 5 0.0008 4

7.74E-05 6 0.00402 5

0.00029 7 0.01255 6


1.60E-09 14 2.98E-07 1

1.93E-05 48 0.00179 2

8.59E-05 8 0.00533 3

0.00026 30 0.0122 4

0.00097 3 0.03026 5

0.00114 50 0.03026 5

0.00101 103 0.03026 5

0.0016 69 0.03307 8

0.00153 36 0.03307 8

Arguments: refPop=ann, featureType=featureNames, colname=chisq.test, absolute=FALSE, testfunction=function


3.72E-13 1 4.16E-10 1

2.68E-10 5 7.47E-08 2

2.68E-10 5 7.47E-08 2

2.68E-10 5 7.47E-08 2

4.18E-10 11 9.35E-08 5

5.42E-10 19 1.01E-07 6

1.75E-09 20 2.80E-07 7

2.82E-09 12 3.93E-07 8

1.13E-08 6 1.26E-06 9

1.13E-08 6 1.26E-06 9

1.42E-08 13 1.44E-06 11

1.89E-07 15 1.63E-05 12

1.89E-07 15 1.63E-05 12

4.64E-07 2 2.88E-05 14

4.64E-07 2 2.88E-05 14

4.64E-07 2 2.88E-05 14

4.64E-07 2 2.88E-05 14

4.64E-07 2 2.88E-05 14

5.43E-07 16 3.03E-05 19

5.43E-07 16 3.03E-05 19

6.45E-07 225 3.35E-05 21

6.60E-07 110 3.35E-05 21

1.04E-06 28 5.07E-05 23

1.24E-06 8 5.77E-05 24

1.38E-06 17 6.16E-05 25

2.59E-06 60 0.00011 26

3.04E-06 30 0.00013 27

3.16E-06 18 0.00013 28

4.92E-06 31 0.00019 29

6.00E-06 9 0.00022 30

6.65E-06 19 0.00024 31

7.74E-06 32 0.00027 32

2.13E-05 10 0.00072 33

2.39E-05 51 0.00079 34

4.14E-05 22 0.00132 35

5.42E-05 3 0.00148 36

5.42E-05 3 0.00148 36

5.42E-05 3 0.00148 36

5.42E-05 3 0.00148 36

5.42E-05 3 0.00148 36

5.42E-05 3 0.00148 36

6.86E-05 23 0.00183 42

8.93E-05 146 0.00232 43

9.64E-05 39 0.00245 44

0.00011 24 0.00271 45

0.00013 40 0.00313 46

0.00014 12 0.00341 47

0.00036 27 0.00832 48

0.00056 46 0.01275 49

0.00061 4 0.01362 50

0.00084 69 0.01839 51

0.00098 15 0.02113 52

0.00146 51 0.03081 53

0.0016 16 0.03244 54

0.0016 16 0.03244 54

0.00204 53 0.04 56

0.00204 53 0.04 56

0.00255 34 0.04438 58

0.00255 34 0.04438 58

0.00266 5 0.04438 58

0.00266 5 0.04438 58

0.00245 17 0.04438 58

0.00266 5 0.04438 58

0.00266 5 0.04438 58

0.00266 5 0.04438 58

0.00266 5 0.04438 58

Description

HUMANCYC - glutamine biosynthesis I gene set

HUMANCYC - spermine biosynthesis gene set

HUMANCYC - spermidine biosynthesis gene set

HUMANCYC - ceramide biosynthesis gene set

HUMANCYC - pentose phosphate pathway (non-oxidative branch) gene set

HUMANCYC - sphingomyelin metabolism gene set

Description

NCI_NATURE - S1P4 pathway gene set

NCI_NATURE - FGF signaling pathway gene set

NCI_NATURE - S1P5 pathway gene set

NCI_NATURE - VEGFR1 specific signals gene set

NCI_NATURE - Class IB PI3K non-lipid kinase events gene set

NCI_NATURE - Angiopoietin receptor Tie2-mediated signaling gene set

NCI_NATURE - Neurotrophic factor-mediated Trk receptor signaling gene set

NCI_NATURE - Signaling events mediated by VEGFR1 and VEGFR2 gene set

NCI_NATURE - N-cadherin signaling events gene set

Description

REACTOME - Phospholipase C-mediated cascade gene set

REACTOME - Activation of CaMK IV gene set

REACTOME - FRS2-mediated cascade gene set

REACTOME - CaMK IV-mediated phosphorylation of CREB gene set

REACTOME - MAP kinase cascade gene set

REACTOME - Frs2-mediated activation gene set

REACTOME - Prolonged ERK activation events gene set

REACTOME - Downstream signaling of activated FGFR gene set

REACTOME - CREB phosphorylation through the activation of CaMKK gene set

REACTOME - ERK activation gene set

REACTOME - SHC-mediated signalling gene set

REACTOME - Grb2 events in EGFR signaling gene set

REACTOME - SOS-mediated signalling gene set

REACTOME - Astrocytic Glutamate-Glutamine Uptake And Metabolism gene set

REACTOME - Neurotransmitter uptake and Metabolism In Glial Cells gene set

REACTOME - phospho-PLA2 pathway gene set

REACTOME - Entry of Influenza Virion into Host Cell via Endocytosis gene set

REACTOME - ERK2 activation gene set

REACTOME - Shc events in EGFR signaling gene set

REACTOME - SHC-related events gene set

REACTOME - Axon guidance gene set

REACTOME - NGF signalling via TRKA from the plasma membrane gene set

REACTOME - Down-stream signal transduction gene set

REACTOME - PI-3K cascade gene set

REACTOME - Signalling to p38 via RIT and RIN gene set

REACTOME - NCAM signaling for neurite out-growth gene set

REACTOME - Signalling to ERKs gene set

REACTOME - Destabilization of mRNA by Tristetraprolin (TTP) gene set

REACTOME - PLC-gamma1 signalling gene set

REACTOME - PECAM1 interactions gene set

REACTOME - ARMS-mediated activation gene set

REACTOME - Signaling by PDGF gene set

REACTOME - Role of second messengers in netrin-1 signaling gene set

REACTOME - Signaling by EGFR gene set

REACTOME - Signalling to RAS gene set

REACTOME - Collagen adhesion via Gp IV gene set

REACTOME - Arachidonate production from DAG gene set

REACTOME - Breakdown of the nuclear lamina gene set

REACTOME - EGFR interacts with phospholipase C-gamma gene set

REACTOME - ERKs are inactivated gene set

REACTOME - Progressive trimming of alpha-1 2-linked mannose residues from Man9/8/7GlcNAc2 to produce Man5GlcNAc2 gene set

REACTOME - NCAM1 interactions gene set

REACTOME - Signalling by NGF gene set

REACTOME - PLC beta mediated events gene set

REACTOME - Signaling by FGFR gene set

REACTOME - G-protein mediated events gene set

REACTOME - Advanced glycosylation endproduct receptor signaling gene set

REACTOME - Ca-dependent events gene set

REACTOME - Netrin-1 signaling gene set

REACTOME - Collagen adhesion via alpha 2 beta 1 glycoprotein gene set

REACTOME - Transcriptional Regulation of White Adipocyte Differentiation gene set

REACTOME - Class C/3 (Metabotropic glutamate/pheromone receptors) gene set

REACTOME - IRS-mediated signalling gene set

REACTOME - Other semaphorin interactions gene set

REACTOME - CRMPs in Sema3A signaling gene set

REACTOME - Opioid Signalling gene set

REACTOME - IRS-related events gene set

REACTOME - Post NMDA receptor activation events gene set

REACTOME - Activation of NMDA receptor upon glutamate binding and postsynaptic events gene set

REACTOME - Beta-catenin phosphorylation cascade gene set

REACTOME - CTLA4 inhibitory signaling gene set

REACTOME - RNA Polymerase III Transcription Termination gene set

REACTOME - S6K1 signalling gene set


REACTOME - RAF activation gene set

REACTOME - N-glycan trimming and elongation in the cis-Golgi gene set

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MicroRNA profiling in pediatric pilocytic astrocytoma reveals biologically relevant targets, including PBX3, NFIB, and METAP2

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