Identification and functional characterization of lncRNAs acting as ceRNA involved in the malignant progression of glioblastoma multiforme

  • Authors:
    • Kun Zhang
    • Qi Li
    • Xixiong Kang
    • Yajie Wang
    • Shuo Wang
  • View Affiliations

  • Published online on: September 5, 2016     https://doi.org/10.3892/or.2016.5070
  • Pages: 2911-2925
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Abstract

Glioblastoma multiforme (GBM) is the most common brain malignancy. Long non-coding RNAs (lncRNAs) are aber­rantly expressed in many cancers and involved in pathogenesis, progression and metastasis of tumors. In particular, lncRNAs can function as competing endogenous RNAs (ceRNAs). The functional roles of lncRNA associated-ceRNAs in GBM are not fully understood. Human Exon 1.0 Microarray (Affymetrix) and Human MicroRNA Microarray (Agilent) were used to detect the expression of 955 microRNAs (miRNAs), 33,125 lncRNAs, and 17,453 mRNAs in 8 GBM and 8 normal brain samples. The function of differential mRNA was determined by Gene Ontology (GO) and pathway analysis. The distinctly expressed miRNAs, lncRNAs and mRNAs were subjected to construct miRNA-lncRNA-mRNA interaction network. The expression of miRNAs, lncRNAs and mRNAs in GBM tissues vs. normal brain tissues was examined by quantitative real-time RT-PCR. A total of 41 miRNAs, 398 lncRNAs and 1,995 mRNAs were found to be differentially expressed between the GBM and normal brain groups. GO and pathway analyses had proven that the functions of differentially expressed mRNAs in GBM related closely with many processes important in the cancer pathogenesis. Fifty-five lncRNAs acting as ceRNAs were identified based on the miRNA-lncRNA-mRNA interaction network. The potential roles of the 39 ceRNAs were revealed, which participated in 23 diverse cancer biological pathways, including proliferation, cell apoptosis, adhesion, angiogenesis and metastasis. The identified sets of miRNAs, lncRNAs and mRNAs specific to GBM were verified by qRT-PCR experiment in GBM samples. Our study predicts the biological functions of a multitude of lncRNA associated-ceRNAs in GBM. Moreover, our study provides a road map for the identification and analysis of lncRNA acting as ceRNA in tumors.

Introduction

Mammalian genomes generate thousands of regulatory RNAs that are either long non-coding RNAs (lncRNAs) or microRNAs (miRNAs) (1,2). lncRNAs are more than 200 nucleotides, and synthesized by RNA polymerase II, spliced and sometimes polyadenylated (3). They are pervasively transcribed, and exhibit spatially and temporally regulated expression patterns (4). Unlike small ncRNAs, lncRNAs can fold into complex secondary and higher order structures to provide greater potential and versatility for both protein and target recognition (5). lncRNAs have been found to play crucial regulatory roles in a diverse range of cellular processes and biological pathways, including genomic imprinting, chromosome inactivation, differentiation and development of many human diseases (6). lncRNAs are emerging as new players in the cancer biology paradigms and their dysfunction are correlated with tumorigenesis and malignancy transformation in various types of cancers (7,8).

miRNAs, the most well characterized ncRNAs, are short endogenous molecules, approximately 22 nucleotides in length, that are processed by the RNase III enzymes Drosha and Dcr. miRNAs post-transcriptionally regulate the gene expression through interaction between the 5′ end and the 3′ untranslated region (3′UTR) of mRNA. miRNA can guide the RNA-induced silencing complex (RISC) to miRNA response element (MRE) on target transcript, usually resulting in degradation of the transcript or inhibition of its translation (9). Dysregulation of miRNA expression is involved in various diseases (10). Accumulating evidence highlights the role of miRNA-mediated regulation in cell growth, differentiation, proliferation and apoptosis. Alterations in the miRNA balance in the cell can lead to dysregulation of tumor suppressor genes and/or oncogenes regulated by aberrantly expressed miRNAs, leading to cancer (11,12).

Recent studies have described a complicated interplay among diverse RNA species, including coding and non-coding RNAs. These RNAs inclusive of mRNA, pseudogene, lncRNA or circular RNA, interact and co-regulate with each other by acting as competing endogenous RNAs (ceRNAs). ceRNAs have MRE, and serve as miRNA sponges to control miRNAs available to their target RNAs. ceRNA can sequester miRNAs, thereby protecting their target RNAs from repression (13). Understanding this novel RNA interaction will lead to significant insight into gene regulatory networks in human development and disease. Although lacking 3′UTRs, lncRNAs have been reported to be downregulated by miRNAs and work as ceRNAs. The experimental evidence is already emerging of lncRNAs as competitive platforms for both miRNAs and mRNAs (14,15).

Glioblastoma multiforme (GBM) is the most common and malignant brain tumor with poor prognosis. According to the 2007 World Health Organization classification, gliomas are classified into 4 histopathological grades based on malignancy degree, and GBM is the highest-grade glioma (grade IV) (16). Patients with newly diagnosed GBM exhibit a median survival of approximately 15 months (17). Despite maximal surgical, radiological and chemotherapeutic interventions, these figures have changed little in the past two decades (18). New therapeutic strategies will likely evolve from a better understanding of GBM biology.

Efforts have been made to study the relationship between the lncRNA expression and the GBM pathogenesis (8,1921), but many more lncRNAs playing crucial roles in GBM remain to be determined. The aberrant miRNA expression has features of GBM (22). Nevertheless, the miRNA-lncRNA-mRNA regulation networks in the GBM, as well as the potential roles of ceRNAs in the biogenesis and development of GBM have not been explored.

In this study, we aimed at profiling the miRNA, lncRNA and mRNA expression signature, and constructing miRNA-lncRNA-mRNA crosstalk by analyzing a cohort of sample-matched exon and miRNA expression microarrays from the Cancer Genome Atlas (TCGA), and predicting the functions of lncRNAs acting as ceRNAs in GBM. The identified sets of lncRNA, miRNA and mRNA specific to GBM were subsequently confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) in GBM samples.

Materials and methods

Data-set characteristics

The sample matched whole-transcript and miRNA expression profiling upon GBM were obtained from the TCGA database (https://tcga-data.nci.nih.gov/tcga/). To compare the miRNA, lncRNA and mRNA expression signatures in GBM, we selected 16 data-sets that included 8 GBM and 8 non-tumoral brain samples. Two panels of data-sets were included in our study: Affymetrix Human Exon 1.0 array and Agilent Human MicroRNA array 8×15K.

Data analysis

Two-class differential was used to determine the differentially expressed miRNA, lncRNA and mRNA between the normal and GBM groups. The random variance model (RVM) t-test was applied to filter the differentially expressed genes for it can effectively increase the degrees of freedom in cases of small samples. The false discovery rate (FDR) was calculated to correct the P-value. We selected the differentially expressed miRNAs, lncRNAs and mRNAs according to the P-value and FDR. P-values <0.05 and FDR <0.05 were considered significant.

The differentially expressed probe sets were imported into Cluster and TreeView (Stanford University) to perform hierarchical cluster analysis (HCA) (23).

Gene Ontology (GO) and pathway analysis

A GO analysis was applied to analyze the main functions of the differentially expressed mRNAs (24). Specifically, a two-sided Fisher's exact test and a χ2 test were used to classify the GO category. We computed P-values of the GO for each differential gene. Enrichment provides a measure for the significant function: As the enrichment increases, the corresponding function is more specific. Within the significant category, the enrichment Re was given as follows: Re = (nf/n)/(Nf/N), where nf is the number of flagged genes within the particular category, n is the total number of genes within the same category, Nf is the number of flagged genes in the entire microarray, and N is the total number of genes in the microarray.

Pathway analysis was used to identify the significant pathway of the differential mRNAs according to KEGG, BioCarta and Reactome. We used Fisher's exact test and the χ2 test to select the significant pathway, and the threshold of significance was defined by P-value and FDR. The enrichment Re was calculated as described above (25).

Construction of lncRNA-mRNA co-expression network

The lncRNA-mRNA networks were built according to the normalized signal intensity of specific mRNA and lncRNA expression in microarray. For each pair of mRNA-lncRNA, mRNA-mRNA or lncRNA-lncRNA, we calculated the Pearson correlation and chose the significant correlation pairs to construct the network (26). In a network analysis, degree is the most important measure of an mRNA or lncRNA centrality within a network. Degree centrality is defined as the link numbers one node has to the other (27). The clustering coefficient represents the density of each gene with the adjacent gene, and the larger the clustering coefficient, the greater importance the gene has in regulating the network.

Patient samples

GBM specimens were derived from patients with GBM who underwent surgical treatment at Beijing Tian Tan Hospital. All histologic diagnoses were made on formalin fixed, paraffin-embedded H&E sections and were reviewed blinded to the original diagnosis according to the 2007 World Health Organization classification. Normal brain tissues were obtained from severe head trauma patients for whom partial resection of normal brain was required during surgery at Beijing Tian Tan Hospital. Samples were collected immediately after surgical resection, snap-frozen and stored in liquid nitrogen. The study was approved by the institutional review board of Beijing Tian Tan Hospital.

RNA preparation and qRT-PCR

Total RNA from tissue specimens was extracted using the TRIzol reagent (Invitrogen Life Technologies, Carlsbad, CA, USA). RNA integrity was analyzed on a 1.2% agarose gel. RNA quantity was determined using a NanoDrop 2000c Spectrophotometer (Thermo Fisher Scientific, Inc., Waltham, MA, USA). RNA (1 μg) was reverse transcribed with a PrimeScript™ RT reagent kit (Takara Biotechnology Co., Ltd., Dalian, China) for cDNA synthesis and genomic DNA removal. For miRNA detection, total RNA was reverse transcribed using miRNA specific primers. qPCRs were performed according to the instructions of the SYBR Premix Ex Taq™ II kit (Takara Biotechnology Co., Ltd.) and carried out in the Takara real-time PCR system. Gene-specific primers were designed using online primer designing tools primer-blast (http://www.ncbi.nlm.nih.gov/tools/primer-blast/). The primer sequences are listed in Table I. The lengths of amplifications are between 100 and 250 bp. Standard deviations were calculated from three PCR replicates. The specificity of amplification was assessed by dissociation curve analysis and the relative abundance of genes was determined using the comparative ΔΔCt method.

Table I

The miRNA, lncRNA and mRNA primers for qRT-PCR.

Table I

The miRNA, lncRNA and mRNA primers for qRT-PCR.

PrimersSequences (5′-3′)
miR-21RT: CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGATCAACATG
F: CTCAACTGGTGTCGTGGAGT
R: ACACTCCAGCTGGGTAGCTTATCAGACTG
miR-27aRT: CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGCGGAACTT
F: CTCAACTGGTGTCGTGGAGT
R: ACACTCCAGCTGGGTTCACAGTGGCTAAG
miR-210RT: CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGATCAGCCGC
F: CTCAACTGGTGTCGTGGAGT
R: CACTCCAGCTGGGCTGTGCGTGTGACAG
miR-23aRT: CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGGAAATCCC
F: CTCAACTGGTGTCGTGGAGT
R: CACTCCAGCTGGGATCACATTGCCAGGG
miR-155RT: CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAACCCCTATC
F: CTCAACTGGTGTCGTGGAGT
R: ACACTCCAGCTGGGTTAATGCTAATCGTG
miR-139RT: CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAACTGGAGA
F: CTCAACTGGTGTCGTGGAGT
R: CACTCCAGCTGGGTCTACAGTGCACGTG
hsa-miR-338RT: CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGACAACAAAAT
F: CTCAACTGGTGTCGTGGAGT
R: ACACTCCAGCTGGGTATTGCACTCGTCC
miR-137RT: CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGACTACGCGTA
F: CTCAACTGGTGTCGTGGAGT
R: ACACTCCAGCTGGGTTATTGCTTAAGAAT
miR-7RT: CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAACAACAAA
F: CTCAACTGGTGTCGTGGAGT
R: CACTCCAGCTGGGTGGAAGACTAGTGAT
miR-124aRT: TCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGGCATTCAC
F: CTCAACTGGTGTCGTGGAGT
R: CACTCCAGCTGGGTAAGGCACGCGGTGA
miR-15aRT: CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGACACAAACCA
F: CTCAACTGGTGTCGTGGAGT
R: CACTCCAGCTGGGTAGCAGCACATAATG
miR-29bRT: CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAAACACTGAT
F: CTCAACTGGTGTCGTGGAGT
R: CACTCCAGCTGGGTAGCACCATTTGAAA
miR-29cRT: CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGATAACCGATT
F: CTCAACTGGTGTCGTGGAGT
R: CACTCCAGCTGGGTAGCACCATTTGAAA
ENST00000520186F: GTTGGACCTTACTGAGGCCG
R: GGAGACACCATGGCTGGAAC
ENST00000559981F: AGAGTGAAATTTTGTATAAGCACCA
R: GCCTGGAGACATACTGAGATGG
ENST00000547415F: TGCCATCTGCAGAGTGAAACT
R: GGCTTTCCAGTCTAGGGCAG
ENST00000518554F: TGGCATTTTGTCAGTTTTCCCG
R: GCAAATGCACACACCACTCC
GAPDHF: GCACCGTCAAGGCTGAGAAC
R: TGGTGAAGACGCCAGTGGA
RNU6F: CTCGCTTCGGCAGCACA
R: AACGCTTCACGAATTTGCGT
AKT3F: TTGCTTTCAGGGCTCTTGAT
R: CATAATTTCTTTGCATCATCTGG
PPP3CAF: TGTGATATCCTGTGGTCAGA
R: CTGACTGTGTTGTGAGTGAA
LAMC1F: TGGGCATTCTTCTGTCTGTACAA
R: GCCACCCATCCTCATCAATC
TNFRSF1AF: TGCCTACCCCAGATTGAGAA
R: ATTTCCCACAAACAATGGAGTAG

[i] miRNA, microRNA; lncRNA, long non-coding RNA.

Results

GBM demonstrates significantly altered miRNA, lncRNA and mRNA expression patterns comparing with that of the normal brain

In terms of the Sanger miRBase database, 866 human and 89 human viral miRNAs were authenticated on the Agilent Human MicroRNA array 8×15K. Based on the NetAffx annotation of the probe sets, the Ensemble, NOCODE3.0, and UCSC annotations of lncRNAs, and the RefSeq, Ensemble and GenBank annotations of mRNAs, we identified 33,125 lncRNAs (corresponding to 44,482 probe sets) and 17,453 mRNAs (corresponding to 22,011 probe sets) represented on the Affymetrix Human Exon 1.0 array (data not shown).

The expression patterns of miRNAs, lncRNAs and mRNAs were detected in 8 GBMs and 8 normal brain samples. We identified 41 miRNAs, 398 lncRNAs and 1,995 mRNAs that had significant differential expression in the GBM group comparing with the normal brain group (fold change ≥2.0 or ≤0.5 and P-value <0.05, data not shown).

The hierarchical clustering analysis showed that with the differential expression of these miRNAs, lncRNAs and mRNAs, samples were non-random partitioned, they were divided into 2 groups, the first group containing 8 normal brain samples and the second group containing 8 GBM samples (Fig. 1). Thus, the miRNA, lncRNA and mRNA expression signatures identified here were likely to be representative.

Construction of miRNA-lncRNA-mRNA interaction network and identification lncRNAs acting as ceRNAs

The miRNA-lncRNA-mRNA network was constructed according to the study flow summarized in Fig. 2.

First, the target mRNAs of the differentially expressed miRNA were analyzed by TargetScan and miRanda method, termed as target 1 mRNAs (6,737 mRNAs, data not shown). The intersection of the target 1 mRNAs and differentially expressed mRNAs in GBM was picked and obtained target 2 mRNAs (1,034 mRNAs, data not shown). Of the target 2 mRNAs, the mRNAs were selected with expression levels negatively correlated with miRNA expression, and were termed the N&T mRNAs (749 mRNAs, data not shown).

Then, GO and pathway analysis were applied to analyze the significant function and pathway of the N&T mRNAs. GO analysis results showed that upregulated and downregulated mRNAs respectively were involved in 156 and 240 items with significant functions (P-value <0.01, data not shown). The pathway analysis revealed that there were 65 and 24 significant pathways corresponding to the up and downregulated mRNAs respectively (P-value <0.01, data not shown).

The third step, the mRNAs that contained both the significant function and pathway were termed G&P mRNAs (248 mRNAs, data not shown). The G&P mRNAs and differently expressed lncRNAs were used to build the lncRNA-mRNA co-expression network, respectively, in the normal and GBM group (data not shown).

The TargetScan method was used to analysis the target lncRNAs of differentially expressed miRNA and obtained the 55 miRNA targeted lncRNAs. These 55 lncRNAs were identified ceRNAs. Based on the interaction network of miRNA-mRNA, miRNA-lncRNA and lncRNA-mRNA, we obtained 224 feed-forward loop networks and constructed general miRNA-lncRNA-mRNA feed-forward loop network (data not shown). All of miRNAs, lncRNAs and mRNAs and their relations in this network are listed in Table II.

Table II

The 224 feed-forward loops including miRNAs, lncRNAs and mRNAs.

Table II

The 224 feed-forward loops including miRNAs, lncRNAs and mRNAs.

No.miRNAlncRNAmRNA
1miR-15an339339PAK7
2miR-15an339339CACNA1E
3miR-15a ENST00000520186PAK7
4miR-15a ENST00000533229PAK7
5miR-15a ENST00000566630PAK7
6miR-15an341995PAK7
7miR-15an346032CACNA1E
8miR-15an346032NMNAT2
9miR-15a ENST00000520186MAPK9
10miR-15a ENST00000520186AKT3
11miR-15a ENST00000559981VAMP1
12miR-15a ENST00000559981SYNJ1
13miR-15a ENST00000596580VAMP1
14miR-15a ENST00000596580AKT3
15miR-15a ENST00000596580SYNJ1
16miR-15a ENST00000596580SLC9A6
17miR-15a ENST00000569946VAMP1
18miR-15a ENST00000532691CACNA1E
19miR-15a ENST00000566942HTR2A
20miR-15an338128PTPRR
21miR-15a ENST00000566630CACNA1E
22miR-15a ENST00000524501CACNA1E
23miR-15a ENST00000578746CACNA1E
24miR-15an341995CACNA1E
25miR-15an410578SYNJ1
26miR-15a ENST00000524501NMNAT2
27miR-15a ENST00000578746NMNAT2
28miR-15an411142AKT3
29miR-15a ENST00000492667AKT3
30miR-15a ENST00000434383SGCD
31miR-15an411142SYNJ1
32miR-15a ENST00000492667SYNJ1
33miR-15bn339339PAK7
34miR-15bn339339CACNA1E
35miR-15b ENST00000520186PAK7
36miR-15b ENST00000533229PAK7
37miR-15b ENST00000566630PAK7
38miR-15bn341995PAK7
39miR-15bn406352PAK7
40miR-15bn346032CACNA1E
41miR-15bn346032NMNAT2
42miR-15b ENST00000520186MAPK9
43miR-15b ENST00000520186AKT3
44miR-15b ENST00000559981VAMP1
45miR-15b ENST00000559981SYNJ1
46miR-15b ENST00000596580VAMP1
47miR-15b ENST00000596580AKT3
48miR-15b ENST00000596580SYNJ1
49miR-15b ENST00000596580SLC9A6
50miR-15b ENST00000569946VAMP1
51miR-15b ENST00000532691CACNA1E
52miR-15b ENST00000566942HTR2A
53miR-15bn338128PTPRR
54miR-15b ENST00000566630CACNA1E
55miR-15b ENST00000524501CACNA1E
56miR-15bn341995CACNA1E
57miR-15bn406352CACNA1E
58miR-15bn410578SYNJ1
59miR-15b ENST00000524501NMNAT2
60miR-15bn411142AKT3
61miR-15b ENST00000492667AKT3
62miR-15b ENST00000434383SGCD
63miR-15bn411142SYNJ1
64miR-15b ENST00000492667SYNJ1
65miR-23an339339SLIT1
66miR-23an339339RXRG
67miR-23an339339SLC1A1
68miR-23a ENST00000562191GABRB2
69miR-23a ENST00000562191NEFL
70miR-23a ENST00000562191MEF2C
71miR-23an346032GABRB3
72miR-23an346032PCLO
73miR-23a ENST00000522102SRGAP3
74miR-23an385835GABRA4
75miR-23an383510FUT9
76miR-23an383510CADM3
77miR-23a ENST00000559981ATP6V1C1
78miR-23a ENST00000559981SYNJ1
79miR-23a ENST00000559981TLN2
80miR-23a ENST00000555811GABRB3
81miR-23a ENST00000566630GABRB3
82miR-23a ENST00000524501GABRB3
83miR-23a ENST00000555811FUT9
84miR-23a ENST00000555811PCLO
85miR-23an411142ATP6V1C1
86miR-23an373066FUT9
87miR-23a ENST00000524501PCLO
88miR-23a ENST00000524501NRXN3
89miR-23a ENST00000434383RXRG
90miR-23a ENST00000434383SGCD
91miR-23a ENST00000502752RAB11FIP2
92miR-23an384012TLN2
93miR-23an411142SYNJ1
94miR-30an346032GRM5
95miR-30a ENST00000522102SRGAP3
96miR-30an373066CAMK4
97miR-30a ENST00000520186PSD3
98miR-30a ENST00000473866GNAO1
99miR-30a ENST00000532691GRM5
100miR-30a ENST00000569946GRM3
101miR-30a ENST00000569946CACNA1C
102miR-30an339481CACNA1C
103miR-30a ENST00000532691B4GALT6
104miR-30a ENST00000569946NEFL
105miR-30a ENST00000566630B4GALT6
106miR-30an374560GDA
107miR-30a ENST00000471736GRIA2
108miR-30a ENST00000569946NEFM
109miR-30a ENST00000530447B4GALT6
110miR-27an339339IQSEC1
111miR-27an339339PDE3B
112miR-27a ENST00000564076CACNB2
113miR-27a ENST00000562191SNAP25
114miR-27an346032GABRB3
115miR-27an346032SYT1
116miR-27an346032PCLO
117miR-27a ENST00000522102SRGAP3
118miR-27a ENST00000566630GABRB3
119miR-27an406352GABRB3
120miR-27an371475CDS1
121miR-27an338494CACNB2
122miR-27an345100SYT1
123miR-27an345100SNAP25
124miR-27an373066FUT9
125miR-27an339978SYT1
126miR-27an406352PDE3B
127miR-27an406352ATP6V1A
128miR-27a ENST00000492667ATP2B1
129miR-34an339339CACNA1E
130miR-34an346032CACNA1E
131miR-34an346032GABRA3
132miR-34an346032SYT1
133miR-34an383510FUT9
134miR-34a ENST00000549205PSD3
135miR-34a ENST00000549205PRKCE
136miR-34a ENST00000596580SYNJ1
137miR-34a ENST00000555811CACNA1E
138miR-34a ENST00000555811FUT9
139miR-34a ENST00000555811PCLO
140miR-34an384244CNTN2
141miR-34a ENST00000566630CACNA1E
142miR-34an406352CACNA1E
143miR-34an410578SYNJ1
144miR-34a ENST00000569946CPLX2
145miR-34an339978SYT1
146miR-34an406352WASF1
147miR-34an406352PRKCE
148miR-34a ENST00000492667SYNJ1
149miR-106b ENST00000440363PDE3B
150miR-106b ENST00000522102SRGAP3
151miR-106b ENST00000596580PIP4K2A
152miR-106b ENST00000596580AKT3
153miR-106bn345100MAPK9
154miR-106bn337874PIP4K2A
155miR-106bn337874GABBR1
156miR-106b ENST00000555811RIMS2
157miR-106b ENST00000555811B4GALT6
158miR-106bn345100RIMS2
159miR-106bn337874B4GALT6
160miR-106bn345100SCN1A
161miR-106bn411142AKT3
162miR-106b ENST00000434383SGCD
163miR-25 ENST00000493303PRKCE
164miR-25 ENST00000549205MAP2K4
165miR-25 ENST00000555811ST6GAL2
166miR-25n337874ST6GAL2
167miR-25 ENST00000566630ST6GAL2
168miR-25 ENST00000530447ST6GAL2
169miR-25 ENST00000549205PRKCE
170miR-25n345100CACNA1C
171miR-25n345100NEFL
172miR-25 ENST00000555811RIMS2
173miR-25n345100RIMS2
174miR-25n345100SYT1
175miR-2n410578SYNJ1
176miR-25n406352PRKCE
177miR-25n411142SYNJ1
178miR-25 ENST00000492667SYNJ1
179miR-223 ENST00000493303PRKCE
180miR-223n371741ATP2B1
181miR-223n406352PRKCE
182miR-223 ENST00000492667ATP2B1
183miR-155n345100GABRA1
184miR-155n346032DYNC1I1
185miR-155n341006RAB11FIP2
186miR-155 ENST00000596580ATP6V1C1
187miR-155n345100CACNA1C
188miR-155 ENST00000535764ATP6V1C1
189miR-155n411142ATP6V1C1
190miR-155n345100SCN1A
191miR-155n345100DYNC1I1
192miR-155 ENST00000578746DYNC1I1
193miR-2 ENST00000559981PPP3CA
194miR-21n411142PPP3CA
195miR-21 ENST00000492667PPP3CA
196miR-92b ENST00000555811ST6GAL2
197miR-92bn337874ST6GAL2
198miR-92b ENST00000530447ST6GAL2
199miR-92b ENST00000596580SYNJ1
200miR-92bn345100CACNA1C
201miR-92bn345100NEFL
202miR-92b ENST00000555811RIMS2
203miR-92bn345100RIMS2
204miR-92bn345100SYT1
205miR-92bn410578SYNJ1
206miR-92bn411142SYNJ1
207miR-92b ENST00000492667SYNJ1
208miR-339n383510CADM3
209miR-339 ENST00000555811DAAM2
210miR-339 ENST00000555811PCLO
211miR-339 ENST00000523571DAAM2
212miR-339 ENST00000481854SGCD
213miR-10b ENST00000221169TIAM1
214miR-10b ENST00000555811RIMS2
215miR-10b ENST00000524501RIMS2
216miR-29b ENST00000481203TNFRSF1A
217miR-29b ENST00000518554TNFRSF1A
218miR-29b ENST00000547415LAMC1
219miR-29b ENST00000559148MYBL2
220miR-377n338229LAMC1
221miR-124n376998EDNRB
222miR-29c ENST00000547415LAMC1
223miR-29c ENST00000559148MYBL2
224miR-29c ENST00000518554TNFRSF1A

[i] lncRNAs, long non-coding RNAs; miRNAs, microRNAs.

Biological prediction of lncRNA function as ceRNAs in the GBM

The functions of 55 lncRNAs acting as ceRNAs were predicted through pathway analysis of 67 mRNAs in the miRNA-lncRNA-mRNA interaction network. The results indicated that 30 mRNAs participated in 7 upregulated and 16 downregulated pathways which involved in diverse biological processes of cancer, including proliferation, cell apoptosis, adhesion, angiogenesis and metastasis (Fig. 3A and B). As a consequence, we predicted the important roles of the 39 ceRNAs in GBM pathogenesis. The miRNAs, lncRNAs, mRNAs, and their participated pathways are listed in Table III.

Table III

Functional prediction of the lncRNA ceRNAs based on pathway analysis of mRNAs that location together in the miRNA-lncRNA-mRNA feed-forward loop in GBM.

Table III

Functional prediction of the lncRNA ceRNAs based on pathway analysis of mRNAs that location together in the miRNA-lncRNA-mRNA feed-forward loop in GBM.

miRNAlncRNAmRNAPathway
hsa-miR-15a-5pn339339PAK7ErbB signaling
ENST00000520186
ENST00000533229
ENST00000566630
n341995
hsa-miR-15a-5pn339339CACNA1ECalcium, MAPK signaling
n346032
ENST00000532691
ENST00000566630
ENST00000524501
ENST00000578746
n341995
hsa-miR-15a-5p ENST00000520186MAPK9MAPK, ErbB, Wnt signaling, focal adhesion, pathways in cancer
hsa-miR-15a-5p ENST00000520186AKT3MAPK, ErbB, PI3K-Akt, VEGF, mTOR signaling, glioma, apoptosis, focal adhesion, pathways in cancer
ENST00000596580
n411142
ENST00000492667
hsa-miR-15a-5p ENST00000559981SYNJ1 Phosphatidylinositol signaling
ENST00000596580
n410578
n411142
ENST00000492667
hsa-miR-15a-5p ENST00000566942HTR2ACalcium signaling
hsa-miR-15a-5pn338128PTPRRMAPK signaling
hsa-miR-15b-5pn339339PAK7ErbB signaling
ENST00000520186
ENST00000533229
ENST00000566630
n341995
n406352
hsa-miR-15b-5pn339339CACNA1ECalcium, MAPK signaling
n346032
ENST00000532691
ENST00000566630
ENST00000524501
n341995
n406352
hsa-miR-15b-5p ENST00000520186AKT3MAPK, ErbB, PI3K-Akt, VEGF, mTOR signaling, glioma, apoptosis, focal adhesion, pathways in cancer
ENST00000596580
n411142
ENST00000492667
hsa-miR-15b-5p ENST00000559981SYNJ1 Phosphatidylinositol signaling
ENST00000596580
n410578
n411142
ENST00000492667
hsa-miR-15b-5p ENST00000520186MAPK9MAPK, ErbB, Wnt signaling, focal adhesion, pathways in cancer
hsa-miR-15b-5p ENST00000566942HTR2ACalcium signaling
hsa-miR-15b-5pn338128PTPRRMAPK signaling
hsa-miR-23a-3pn339339SLIT1 Phosphatidylinositol signaling
hsa-miR-23a-3pn339339RXRGPathways in cancer
ENST00000434383
hsa-miR-23a-3p ENST00000559981SYNJ1 Phosphatidylinositol signaling
n411142
hsa-miR-23a-3p ENST00000562191MEF2CMAPK signaling
hsa-miR-23a-3pn383510CADM3Cell adhesion molecules
hsa-miR-23a-3p ENST00000559981TLN2Focal adhesion
hsa-miR-23a-3p ENST00000524501NRXN3Cell adhesion molecules
hsa-miR-30a-5pn346032GRM5Calcium signaling
ENST00000532691
hsa-miR-30a-5p ENST00000569946CACNA1CCalcium signaling, MAPK signaling
n339481
hsa-miR-30a-5pn373066CAMK4Calcium signaling
hsa-miR-27a-3pn339339PDE3BProteoglycans in cancer
hsa-miR-27a-3p ENST00000564076CACNB2MAPK signaling
n338494
hsa-miR-27a-3pn371475CDS1 Phosphatidylinositol signaling
hsa-miR-27a-3p ENST00000492667ATP2B1Calcium signaling
hsa-miR-34a-5pn339339CACNA1ECalcium, MAPK signaling
n346032
ENST00000555811
ENST00000566630
n406352
hsa-miR-34a-5p ENST00000596580SYNJ1 Phosphatidylinositol signaling
n410578
ENST00000492667
hsa-miR-34a-5p ENST00000549205PRKCETight junction
n406352
hsa-miR-34a-5pn384244CNTN2Cell adhesion molecules
hsa-miR-34a-5pn406352WASF1Regulation of actin cytoskeleton,
Adherens junction
hsa-miR-106b-5p ENST00000596580PIP4K2A Phosphatidylinositol signaling, regulation of actin cytoskeleton
n337874
hsa-miR-106b-5p ENST00000596580AKT3MAPK, ErbB, PI3K-Akt, VEGF,
n411142mTOR signaling, glioma, apoptosis, focal adhesion, pathways in cancer
hsa-miR-106b-5pn345100MAPK9MAPK, ErbB, Wnt signaling, focal adhesion, pathways in cancer
hsa-miR-25-3p ENST00000493303PRKCETight junction
ENST00000549205
n406352
hsa-miR-25-3pn410578SYNJ1 Phosphatidylinositol signaling
n411142
ENST00000492667
hsa-miR-25-3p ENST00000549205MAP2K4MAPK, ErbB signaling
hsa-miR-223-3p ENST00000493303PRKCETight junction
n406352
hsa-miR-223-3pn371741ATP2B1Calcium signaling
ENST00000492667
hsa-miR-21-5p ENST00000559981PPP3CACalcium, MAPK, Wnt, VEGF signaling
n411142
ENST00000492667
hsa-miR-92b-3p ENST00000596580SYNJ1 Phosphatidylinositol signaling
n410578
n411142
ENST00000492667
hsa-miR-339-5p ENST00000555811DAAM2Wnt signaling
ENST00000523571
hsa-miR-339-5pn383510CADM3Cell adhesion molecules
hsa-miR-10b-5p ENST00000221169TIAM1Regulation of actin cytoskeleton, proteoglycans in cancer
hsa-miR-29b-3p ENST00000481203TNFRSF1AMAPK signaling, cytokine-cytokine receptor interaction, apoptosis
ENST00000518554
hsa-miR-29b-3p ENST00000547415LAMC1PI3K-Akt signaling, focal adhesion, ECM-receptor interaction, pathways in cancer
hsa-miR-377-3pn338229LAMC1PI3K-Akt signaling, focal adhesion, ECM-receptor interaction, pathways in cancer
hsa-miR-29c-3p ENST00000547415LAMC1PI3K-Akt signaling, focal adhesion, ECM-receptor interaction, pathways in cancer
hsa-miR-29c-3p ENST00000518554TNFRSF1AMAPK signaling, cytokine-cytokine receptor interaction, apoptosis

[i] lncRNA, long non-coding RNA; ceRNAs, competing endogenous RNAs; miRNA, microRNA; GBM, glioblastoma multiforme.

Quantitative real-time RT-PCR analysis of the distinctive expression of lncRNAs, miRNAs and mRNAs in GBM samples

To validate the conclusions of microarray analysis, we selected 10 miRNAs with larger fold change from the microarray results and analyzed their expression levels by qRT-PCR in 20 normal brain and 30 GBM samples. Our results confirmed the findings of the miRNA microarray dataset (Fig. 4A and B).

Based on the analysis of 224 miRNA-lncRNA-mRNA feed-forward loops in Table II, we evaluated the expression levels of 4 miRNA, 4 lncRNA and 4 mRNA that, respectively, located in 4 feed-forward loops. The average expression levels of miR-15a and miR-21 were significantly increased, while miR-29b and miR-29c were reduced in GBM compared with normal brain tissues. Analysis showed relatively high expression of miRNA and low expression of lncRNA and mRNA, and low expression of miRNA and high expression of lncRNA and mRNA (Fig. 4C and D). The 4 feed-forward loops detection by qRT-PCR are presented in Fig. 4E.

Discussion

In recent years, the emerging significance of ceRNAs in cancers has drawn attention of researchers. ceRNA activity is determined by factors such as miRNA/ceRNA abundance, ceRNA binding affinity to miRNAs and RNA-binding proteins. The alteration of any of these factors may lead to ceRNA network imbalance and thus contribute to cancer (28). ceRNA study processes generally include: ceRNA prediction, ceRNA validation and ceRNA functional investigation.

Recently, several studies have confirmed the dysregulation of lncRNAs by acting as ceRNAs have profound implications for tumor initiation, maintenance or progression. lncRNAs acting as ceRNAs are involvd in the pathogenesis of several common cancers such as thyroid cancer, gastric cancer and hepatocellular cancer (2933). The ceRNA activity of lncRNAs has also been shown to have an oncogenic effect: The lncRNA HOTAIR was shown to display ceRNA activity in gastric cancer cells, in which it was found to specifically bind the tumor suppressor miR-331-3p, modulating HER2 derepression (31). The other example of lncRNA-mediated ceRNA regulation involves the tumor suppressor gene BARD1. The lncRNA BARD1 9′L is transcribed by an alternative intronic promoter of the BARD1 gene and share both miR-203 and miR-101 MREs with BARD1 mRNA in their homologous 3′UTRs. BARD1 mRNAs were downregulated by miR-203 and miR-101, and BARD1 9′L counteracted the effect of these miRNAs. These data support a role for BARD1 9′L as a tumor suppressor transcript through its ceRNA activity (33). These findings provide important clues for understanding the key roles of lncRNA-miRNA functional network in cancers. Exploring the interplay of lncRNA function as a ceRNA in cancer provides new insight into cancer pathogenesis and opportunities for therapy exploration.

Understanding the novel miRNA-lncRNA-mRNA crosstalk will lead to significant insight into gene regulatory networks in cancers. In this study, we investigated the miRNA, lncRNA and mRNA expression signatures in GBM, constructed the miRNA-lncRNA-mRNA regulation network, on this basis, identified the lncRNA acting as ceRNAs and predicted the possible biology functions of these ceRNAs.

We re-annotated the Affymetrix Human Exon 1.0 probe sets and identified the lncRNAs and mRNAs on this array. The sample matched miRNA expression profiling of the Agilent Human MicroRNA array 8×15K was analyzed to determine differently expressed miRNAs in GBM. We identified a set of 41 miRNAs, 398 lncRNAs and 1,995 mRNAs with differentiated expression between GBM and normal brain tissues. Such differentiation signified their potential roles in tumorigenesis.

The complexity and diversity of potential ceRNA interactions have been described with the identification of abundant lncRNAs. We discussed the effect of miRNA competition on the regulation of both lncRNAs and mRNAs, as well as the implications of lncRNA function as ceRNA for the development of GBM. To our knowledge, this is the first study to show the roles of lncRNA acting as ceRNAs in GBM. Understanding the key roles of 'miRNA-lncRNA' module will lead to the identification of new therapeutic targets for treating GBM.

Our qRT-PCR expression analysis confirmed there are a series of miRNAs, lncRNAs and mRNAs aberrantly expressed in GBM tissues, which indicated that the differently expressed non-coding and coding RNAs may be one of characters of GBM. The aberrant miR-21, miR-27a, miR-210, miR-23a, miR-155, miR-139, miR-338, miR-137, miR-7, miR-124a, miR-15a, miR-29b and miR-29c expression levels in GBM were detected, our results were in concordance with the previous findings, and these deregulated miRNAs have been reported to be aberrantly expressed in GBM (3442). In our expression profiling analysis, the lncRNA ENST00000520186, ENST00000559981, ENST00000547415 and ENST00000518554 were separately considered as the ceRNA of miR-15a, miR-21, miR-29b and miR-29c in GBM. So far, these ceRNAs have not been reported implicated in GBM. Four mRNAs may be regulated by these miRNAs and lncRNAs, the PPP3CA have been reported to be aberrantly expressed in other tumors, but have not been studied in GBM; in addition, AKT3, TNFRSF1A and LAMC1 have been studied to different expression in GBM (36,43,44).

Overall, our study identified and analyzed lncRNA function as ceRNA in GBM and showed they may play crucial biological roles during GBM formation and development, and provide important theory and experimental foundations for future study of drug target and treatment for GBM.

Acknowledgments

This study was supported by the Natural Science Foun dation of China (nos. 81572474 and 81303268), the Natural Science Foundation of Beijing City (no. 7152098), the Excellence Talents Training Projects of Beijing City (no. 2013D009008000006) and the Science and Technology Development Fund Project of Traditional Chinese Medicine of Beijing (JJ2015-14). The authors would like to thank the Genminix Company (Shanghai, China) for assistance with bioinformatics analysis.

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November-2016
Volume 36 Issue 5

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Zhang K, Li Q, Kang X, Wang Y and Wang S: Identification and functional characterization of lncRNAs acting as ceRNA involved in the malignant progression of glioblastoma multiforme. Oncol Rep 36: 2911-2925, 2016
APA
Zhang, K., Li, Q., Kang, X., Wang, Y., & Wang, S. (2016). Identification and functional characterization of lncRNAs acting as ceRNA involved in the malignant progression of glioblastoma multiforme. Oncology Reports, 36, 2911-2925. https://doi.org/10.3892/or.2016.5070
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Zhang, K., Li, Q., Kang, X., Wang, Y., Wang, S."Identification and functional characterization of lncRNAs acting as ceRNA involved in the malignant progression of glioblastoma multiforme". Oncology Reports 36.5 (2016): 2911-2925.
Chicago
Zhang, K., Li, Q., Kang, X., Wang, Y., Wang, S."Identification and functional characterization of lncRNAs acting as ceRNA involved in the malignant progression of glioblastoma multiforme". Oncology Reports 36, no. 5 (2016): 2911-2925. https://doi.org/10.3892/or.2016.5070