Identification and functional characterization of lncRNAs acting as ceRNA involved in the malignant progression of glioblastoma multiforme
- Authors:
- Published online on: September 5, 2016 https://doi.org/10.3892/or.2016.5070
- Pages: 2911-2925
Abstract
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 (8,19–21), 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.
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.
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 IIIFunctional prediction of the lncRNA ceRNAs based on pathway analysis of mRNAs that location together in the miRNA-lncRNA-mRNA feed-forward loop in GBM. |
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 (29–33). 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 (34–42). 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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