- Open Access
HOXD-AS1 is a novel lncRNA encoded in HOXD cluster and a marker of neuroblastoma progression revealed via integrative analysis of noncoding transcriptome
- Aliaksandr A Yarmishyn†1,
- Arsen O Batagov†1,
- Jovina Z Tan1,
- Gopinath M Sundaram2,
- Prabha Sampath2, 3, 4,
- Vladimir A Kuznetsov1, 5Email author and
- Igor V Kurochkin1Email author
© Yarmishyn et al.; licensee BioMed Central Ltd. 2014
- Published: 8 December 2014
Long noncoding RNAs (lncRNAs) constitute a major, but poorly characterized part of human transcriptome. Recent evidence indicates that many lncRNAs are involved in cancer and can be used as predictive and prognostic biomarkers. Significant fraction of lncRNAs is represented on widely used microarray platforms, however they have usually been ignored in cancer studies.
We developed a computational pipeline to annotate lncRNAs on popular Affymetrix U133 microarrays, creating a resource allowing measurement of expression of 1581 lncRNAs. This resource can be utilized to interrogate existing microarray datasets for various lncRNA studies. We found that these lncRNAs fall into three distinct classes according to their statistical distribution by length. Remarkably, these three classes of lncRNAs were co-localized with protein coding genes exhibiting distinct gene ontology groups. This annotation was applied to microarray analysis which identified a 159 lncRNA signature that discriminates between localized and metastatic stages of neuroblastoma. Analysis of an independent patient cohort revealed that this signature differentiates also relapsing from non-relapsing primary tumors. This is the first example of the signature developed via the analysis of expression of lncRNAs solely. One of these lncRNAs, termed HOXD-AS1, is encoded in HOXD cluster. HOXD-AS1 is evolutionary conserved among hominids and has all bona fide features of a gene. Studying retinoid acid (RA) response of SH-SY5Y cell line, a model of human metastatic neuroblastoma, we found that HOXD-AS1 is a subject to morphogenic regulation, is activated by PI3K/Akt pathway and itself is involved in control of RA-induced cell differentiation. Knock-down experiments revealed that HOXD-AS1 controls expression levels of clinically significant protein-coding genes involved in angiogenesis and inflammation, the hallmarks of metastatic cancer.
Our findings greatly extend the number of noncoding RNAs functionally implicated in tumor development and patient treatment and highlight their role as potential prognostic biomarkers of neuroblastomas.
- long noncoding RNA
- retinoic acid
Whereas the coding sequences constitute only 1.5% of human genome, the noncoding constituent of the genome has recently come into light as functionally important. The most well studied part of noncoding transcriptome is represented by microRNAs (miRNAs), which affect expression of protein coding genes by modulating mRNA stability and translation. miRNAs regulate various biological processes and have well established roles in cancer . However, long noncoding RNAs (lncRNAs) represent the largest fraction of transcriptional output in the mammalian genome. Unlike extensively studied protein coding transcripts or miRNAs, the majority of lncRNAs still remain "the dark matter of the genome" with their functionality being debatable. Several lncRNAs have been shown in recent years to be directly involved in oncogenesis . Reprogramming of chromatin state mediated by lncRNA HOTAIR is linked to metastasis and poor prognosis in breast cancer . MALAT-1 has originally been shown to be associated with metastasis in lung carcinomas  and regulate invasive potential of tumor cells .
Significant fraction of lncRNAs is represented on widely used microarray platforms, however they have traditionally been ignored in cancer studies. Thousands of microarray datasets accumulated over years in public databases provide an immense resource for analysis of lncRNA expression. In this study we set an example of reanalysis of this rich data source by annotating the noncoding probe sets of the popular Affymetrix U133 microarray and applied this annotation to identify lncRNAs associated with aggressive and nonaggressive types of neuroblastoma (NB).
NB is a common pediatric malignancy characterized by tremendous clinical heterogeneity, in which some tumors are extremely aggressive and drug resistant while others can spontaneously differentiate into benign forms and are sensitive to differentiation stimuli . According to International NB Staging System (INSS), based on tumor characteristics and age, NB is classified into 3 localized stages (stages 1, 2a and 2b), advanced locoregional (stage 3) and metastatic (stages 4 and 4s) . About one-half of NB patients have metastatic disease at diagnosis.
One of the most widely used prognostic genomic markers is amplification of MYCN locus, which occurs in 20% of NB cases. It is associated with poor survival and is considered a promising therapeutic target in NB [8, 9]. All metastatic tumors with amplified MYCN gene are aggressive, whereas tumors with non-amplified MYCN gene have variable clinical behavior that could be driven by clinical (stage, histology, age at diagnosis etc) and biological factors (mutations, chromosome rearrangements, gene expression profiles).
A large number of clinical genomics and experimental model studies have described protein- coding gene expression profiles that can differentiate NBs with favorable and unfavorable outcomes [10–14]. The protein-coding gene signatures of NB aggressiveness are characterized by over-expression of genes involved in MYC and β-catenin pathways, cell cycle
and chromosome segregation, and on the other hand by low expression of genes involved in neural differentiation [15–19]. Unfortunately, reliability and consistency between the gene signatures reported in these and other publications are very poor (V.A. Kuznetsov, unpublished). It suggests high plasticity of gene expression patterns in NB tumors, a large number of correlations between potential biomarkers and high dimensionality of their space. Due to poor understanding of molecular basis of NB and high disease-caused mortality of the NB patients there is an urgent need for discovery of novel molecular drivers of NB progression and its differentiation, as well as high-confidence and reliable clinical biomarkers specifying NB tumors, prognosis of clinical outcome and improvement of NB patients' therapy.
The degree of NB differentiation correlates with stage of NB and its progression. Retinoic acid (RA) is a differentiation agent that is the first choice drug for NB therapy . However, in many cases NB acquires resistance to RA treatment thus leading to relapses. Much effort has been devoted to identify biological subtypes and critical regulators of RA-induced NB differentiation. RA-induced signalling pathway in NB initiates expression of many hundreds of genes including MYCN and other proto-oncogenes . It has also been shown to alter drug resistance mechanisms . Identification of new molecular drivers, as well as unbiased, more complete and clinically relevant mechanisms of RA-induced differentiation of NB is still a great challenge.
Gain of chromosome arm 17q is one of the most common genetic abnormalities encountered in NB, which is associated with poor prognosis . An example of lncRNA biomarker for NB is ncRAN gene mapped to 17q region . Its over-expression was shown to be associated with aggressiveness of primary NB  and confer a set of oncogenic properties, such as acceleration of cell growth and increasing invasiveness [24, 25]. One of the classes of highly conserved lncRNAs, known as transcribed ultraconserved regions (T-UCR) has been shown to be implicated in cancer . Indeed, differential expression of T-UCRs was shown to correlate with such oncogenomic parameters as MYCN amplification status and be a consequence of DNA copy number changes associated with NB . Another study revealed association of different sets of T-UCRs with poor and long-term survival in the metastatic stage of NB patients . In spite of this evidence for involvement of lncRNAs in NB, systematic and unbiased genome-wide analysis has not been performed.
In this study, we performed systematic genome-wide analysis to identify biomarkers for aggressive stages of NB. For these purposes we took advantage of the extensive coverage of a considerable part of noncoding transcriptome by Affymetrix U133 Plus 2.0 microarray probe sets. These probe sets have traditionally been neglected in most studies aimed to identify tumor biomarkers. Here, we developed a computational pipeline to annotate noncoding probe sets on Affymetrix U133 chips, representing ESTs, non-annotated RNAs, and unknown ncRNAs and identified lncRNA transcripts that differentiate favorable from aggressive stages of NB, as well as relapsing and non-relapsing primary tumors. We found that one of the identified in this study lncRNA transcript, termed HOXD-AS1, is induced by RA, regulated by PI3K/Akt pathway and controls genes implicated in RA-mediated cell differentiation and also angiogenesis and inflammation, the hallmarks of metastasized cancer .
Affymetrix U133 series microarrays contain 1,586 probe sets measuring expression of potential lncRNAs, including 549 high-confidence lncRNA transcripts
Length distribution of the ncRNA candidate transcripts reveals three lncRNA classes
Genes encoding lncRNA-2 and lncRNA-3 classes are localized next to transcription factor genes implicated in development
Since the vast majority of the studied lncRNAs have never been functionally classified, we analyzed the functions of their neighboring genes (within +/-10 kb distance). Due to their proximity, they may be co-regulated with the lncRNA genes and thus may suggest biological processes associated with these genomic regions. For 89% (1400/1581) of lncRNAs such neighbors were found. For the classes lncRNA-1, lncRNA-2 and lncRNA-3 this fraction was 89% (62/70), 89% (1218/1372) and 93% (129/138), respectively (Additional files 1 and 3). Gene ontology (GO) analysis of all lncRNAs showed significant enrichment for 13 ontologies, including 'Wnt pathway', 'nucleic acids metabolic processes', 'mitochondria' and 'transcription factors' (Additional file 4). Out of four GO terms associated with lncRNA-1, three ('lysosomal transport', 'hydrolase activity of N-glycosyl compounds', 'phosphodiesterase') were common with all lncRNAs. In contrast, class lncRNA-2 was associated with 10 GO terms common with all lncRNAs. 'Homeobox transcription factors' and 'gut mesoderm development' categories were absent in lncRNA-2 class GO terms and were unique for lncRNA-3 class. Class lncRNA-3 was significantly associated with 15 GO terms, i.e. more than for all lncRNAs. GO terms 'reproduction', 'embryonic development' and 'KRAB box transcription factors' were associated only with lncRNA-3 class. It is remarkable that among the neighbors of lncRNA-3 class genes 'gut mesoderm development' category was represented by 11 genes from 3 HOX clusters: HOXA, HOXB, and HOXD. These transcription factors constitute 88% of all 18 lncRNAs annotated with this GO term. Two other homeobox genes, not belonging to HOX were EMX2 and MNX1. Fifteen other transcription factor genes belong to the zinc finger class and the remaining 8 genes to other classes.
159 lncRNAs are associated with NB progression
Importantly, this list of lncRNA candidates included 13 genes (including four high-confidence lncRNAs) whose neighboring protein-coding genes were annotated as involved in embryonic development (Figure 3). Studying them could give a clue for the mechanisms of lncRNA involvement in neuroblastoma progression. Therefore, we tested the expression of each of the 13 lncRNAs in response to the treatment of neuroblastoma cells to RA. RA is a morphogen, which can induce neuronal differentiation in embryo and cell culture and is widely used for therapy of neuroblastoma.
HOXD-AS1 is up-regulated by retinoic acid in SH-SY5Y neuroblastoma cell line
Characterization of HOXD-AS1 locus and its transcripts
Evolutionary analysis of HOXD-AS1gene
Tissue expression pattern of HOXD-AS1 correlates with HOXD1 and HOXD3genes expression
Differentially expressed genes identified after knock-down of HOXD-AS1 in SH-SY5Y cells
Expression of genes controlled by HOXD-AS1 significantly correlates with its expression in clinical neuroblastoma samples
In non-MNA tumors we analyzed pair-wise correlation between expression of HOXD-AS1 and of its targets revealed in the knock-down experiments. Among the genes regulated by HOXD-AS1 in the cell line, 53 were represented on the Affymetrix chip by 98 probe sets. The expression of 21 of them significantly (P < 0.05) correlated with HOXD-AS1expression in the NB samples (Additional file 12). Sixteen genes correlated positively and 5 negatively. Expression of 20 HOXD-AS1 targets was significantly different in primary tumors of 102 patients with relapse (P < 0.05), seven of which at the same time correlated with NB stage (Additional file 12). Importantly, MAGEA9B gene expression was also a significant (P = 0.014) marker of 5-year survival of neuroblastoma patients (GSE16237). Additionally, MAGEA9B, along with two other targets of HOXD-AS1 - TNF and CNR1, were recently found to be a part of neuroblastoma signature identified in several datasets . Sixteen of HOXD-AS1 target genes were predictive markers of NB relapse (Additional file 13). Remarkably, 12 out of these 16 genes were previously reported to respond to RA treatment (Additional file 14A). Among these genes SNN, TMEM86A, VIPR1, CREM were down-regulated and TSPAN2, CNR1, CREBL1, PTGS1 were up-regulated by RA. Gene signature consisting of four HOXD-AS1 targets, ADAMTS3, AMDMD2, ANG, ASNA1 could predict NB relapse with 76-80% accuracy (Additional files 14B and 14C). Thus, HOXD-AS1, in response to RA treatment, controls expression of genes involved in NB progression. Some of them can serve as predictive markers of NB relapse and are associated with patient survival.
HOXD-AS1 is evenly distributed between nucleus and cytoplasm
So far most of the characterized lncRNAs that effect gene expression are known to be localized to the nucleus. Therefore, we analyzed the subcellular localization of HOXD-AS1. qRT-PCR analysis of subcellular fractions of SH-SY5Y cells revealed that HOXD-AS1 is equally distributed between the nucleus and the cytoplasm (Additional file 15A). This pattern was largely confirmed by RNA fluorescence in situ hybridization. HOXD-AS1-specific probe sets detected individual transcripts both in the nucleus and the cytoplasm (Additional file 15B).
HOXD-AS1 expression is regulated by PI3K/Akt signaling pathway
Evidence for involvement of different lncRNAs in oncogenesis and for their potential use as biomarkers of cancer is growing [2, 38]. Affymetrix U133 series are among the most widely used microarray platforms with thousands of datasets deposited in the public databases. Given the fact that many lncRNAs were fortuitously represented on this platform these datasets provide a great potential for their reanalysis, with lncRNAs taken into account. Here we annotated the noncoding fraction of Affymetrix probe sets and created a resource for this kind of studies.
Discrimination of coding and non-coding transcripts is not straightforward, and different laboratories are using different methods. Therefore, in our work we used two different methods that discard the possibility of any transcript encoding a protein: Megablast, to eliminate the possible known protein homologs; followed by CRITICA to eliminate the possibility that any of our transcripts may encode a protein, yet to be discovered [31, 32]. All 1,586 transcripts studied in our work satisfied both criteria. In addition to that, we applied the criterion of transcript length distribution among proteins to judge about our dataset as a whole. To maximize the reliability of prediction, we performed an additional analysis and mapped the probe sequences to the lncRNA transcript sequences predicted by Gencode consortium. It allowed us to identify 549 reliable probesets corresponding to lncRNAs out of 1,586 initially predicted. We provided classification of these 1,586 lncRNAs by the length of their transcripts and discriminating three classes among the lncRNA genes longer than 200 nt. Our classification system is vindicated by the fact that the proposed lncRNA classes have distinct gene ontologies.
However, lncRNA prediction strategy utilized in this study does not take into account relative position of candidate lncRNA gene in relation to protein coding gene. It has been a prevalent belief for some time that intronic parts of mRNAs cannot be considered as lncRNAs because they merely represent pre-mRNAs on their way to splicing or spliced-out introns on their way to degradation. The lower expression of the intronic regions than those in exons seemingly was consistent with this notion. However, several recent studies have focused on this important issue and concluded that intronic lncRNAs have a number of features that would be expected from functional, stand-alone RNA species. For example, St Laurent et al. have shown that there are thousands of intronic RNAs in the mouse genome changing throughout the inflammation time course that are likely to be functional . The levels of intronic RNAs were independent of those of exons or other introns and some of the introns encoded relatively abundant RNA species. Another study by Tahira et al. described intronic lncRNAs differentially expressed in primary and metastatic pancreatic cancer . The authors suggested that intronic lncRNAs are generated from independent transcriptional units. In support of this notion they have shown enrichment of H3K4me3 marks close to the known boundaries for the set of intronic transcripts, the presence of conserved DNA elements and stable predicted secondary structures in intronic lncRNAs, evidence for the evolutionary selection, and significant enrichment of CAGE tags proximal to known start sites of intronic lncRNAs .
Applying the new probe set and ncRNAs annotation system for biomarker discovery in public datasets, we identified a gene expression signature consisting of 159 lncRNAs, which can discriminate NB tumors by their aggressiveness and clinical stages. To our knowledge, this is the first example of a gene signature based on expression of lncRNAs only. Functional studies of lncRNAs are not common and the biological effects of this class of molecules have not been studied well. The primary structures of lncRNAs are much less conservative in comparison with protein structures. Therefore, functional characterization of human lncRNAs is particularly difficult. Characterization of lncRNAs via gene signatures specific to certain diseases reported in the present study can be used as a tool to solve this methodological conundrum. It is important that our syndrome-centered scan for potential candidates was successfully validated and complemented with mechanistic studies on a model cell line. Regarding the clinical potential of gene expression signatures based only on lncRNAs, it is important to note that such signatures can be particularly useful as diagnostic and prognostic tools in combinations with existing protein-based signatures, since lncRNAs and proteins belong to different regulatory realms and thus naturally increase each other's power as patient classifiers.
Among the 159 lncRNAs we selected HOXD-AS1 for functional characterization as the most potent candidate for playing a role in oncogenesis. The gene encoding HOXD-AS1 is located in HOXD cluster. HOX genes are the key developmental regulators and their aberrant expression is often associated with malignancy . We obtained evidence of a two-step evolution of the unspliced HOXD-AS1 sequence. The first step, characterized with initial two-fold increase of HOXD-AS1 length, presumably occurred around the time of primates branching from other placental animals between 115  and 80 million years ago . The second step, when the maximal-length HOXD-AS1 gene in hominids (H. sapiens, P. paniscus, P. troglodytes and P. abelii) was established, might have occurred 15-19 million years ago, when hominid diverged from hylobatids (N. leucogenys) .
Remarkably, HOXD3 gene, adjacent to HOXD-AS1 locus, has established role in promoting metastatic potential of lung cancer cells . HOXD1, which is also adjacent to HOXD-AS1, has been linked with locus rs2072590 associated with an increased risk of ovarian cancer, more significantly for the serous subtype . This locus lies in a non-coding region, ~5 kb distal to HOXD3 and ~10 kb proximal to HOXD1 . Using higher resolution map, we found that SNP rs2072590 is localized in HOXD-AS1 and includes the last intron and last exon regions boundary.
Interestingly, differential expression of HOXD-AS1 lncRNA was detected by microarray analysis in a number of cancer studies. It was among the 17 genes up-regulated in EGFR-mutated adenocarcinomas, which are characterized by poor post-operative prognosis . In breast cancer, over-expression of breast cancer metastasis suppressor gene (BRMS) leads to down-regulation of HOXD-AS1 , thus further pointing to its potential pro-oncogenic role. In contrast, HOXD-AS1 was shown to be one of the most drastically down-regulated genes in hepatocellular carcinoma . We also found that HOXD-AS1 is strongly suppressed in colorectal adenoma discriminating these precancerous lesions from the surround normal mycosa tissue . Here we show that HOXD-AS1 is highly induced by RA treatment of NB SH-SY5Y cell line. RA causes growth arrest and differentiation of NB cells, therefore it is widely used for therapeutic purpose. The elevation of HOXD-AS1 levels in metastatic tumors is in a seeming contradiction with its up- regulation during tumor cell differentiation. One possible explanation is that HOXD-AS1 elevation in metastatic tumors is a compensatory reaction to a loss of its differentiation-promoting function that actually leads to aberrant tumor-promoting effect. This could happen because of the overall loss of homeostasis when inactivation of one pathway leads to a compensatory activation of another pathway.
In this study, we investigate regulation of HOXD-AS1 expression within a framework of PI3K and MAPK signaling pathways, both of which are necessary for RA mediated differentiation and survival of SH-SY5Y cells . Here we demonstrate that chemical inhibitor of PI3K, but not inhibitors of components of MAPK pathway MEK1 and p38 significantly reduce RA-mediated induction of HOXD-AS1 (Figure 9). Both PI3K and MAPK pathways are induced in SH-SY5Y cells by ligation of a family of growth factors, known as neurotrophins to their respective receptors belonging to a class of receptor tyrosine kinases. Different types of neurotrophin receptors are known to control differentiation and survival of neuroblastoma cells include TrkA, TrkB, TrkC . Whereas TrkA and TrkC are not regulated, or regulated marginally by RA in SH-SY5Y, TrkB is efficiently induced by it (Additional file 16panel A). Interestingly, activation of PI3K pathway by ligation of TrkB with its specific ligand BDNF leads to both neuritogenesis and survival. On the other hand, activation of MAPK pathway contributes only to neuritogenesis, but not survival . Indeed, PI3K pathway is known to promote oncogenesis via inhibition of apoptosis as one of the mechanisms . Both activation of PI3K pathway and over-expression of TrkB and BDNF are associated with poor prognosis in NB [13, 53, 54]. In contrast, patients with increased levels of TrkA and TrkC demonstrate better prognosis [55, 56]. Thus our results imply that HOXD-AS1 may be regulated via PI3K pathway induced by oncogenic BDNF/TrkB axis. This is further supported by our observation that HOXD-AS1 transcript positively correlates with TrkB and negatively correlates with TrkA and TrkC (Additional file 16panel B).
In agreement with the proposed pro-oncogenic role of HOXD-AS1, we observed that its siRNA- mediated knock-down resulted in differential expression of genes implicated in angiogenesis and inflammation, two highly interdependent processes considered as the hallmarks of cancer . Both of these processes are manifested in a crosstalk between tumor cells and their microenvironment that includes endothelial and immune inflammatory cells. In agreement with this, a number of HOXD-AS1 target genes encode secreted proteins that provide communication between tumor and its microenvironment. Among them are cytokines, such as CX3CL1, CCL20, TNF, GDF15 - key regulators of angiogenesis and lymphangiogenesis ANG and PROX1 - genes involved in remodeling of extracellular matrix LOX and ADAMTS3.
In conclusion, the computational pipeline developed in this study was successfully applied to greatly extend the number of novel NB cancer biomarkers using published Affymetrix U133 chip datasets and to identify candidate lncRNAs functionally implicated in classification of the NB subtypes. We found that one of these lncRNAs, named HOXD-AS1, is induced by RA, could be regulated via PI3K/Akt pathway, controls genes involved in RA signaling, angiogenesis and inflammation.
SH-SY5Y cell line was purchased from American Type Culture Collection (ATCC). SH-SY5Y cells were cultured in Hams F12/ DMEM supplemented with 10% FBS in a humidified incubator at 370C with 5% CO2. For cell differentiation experiment SH-SY5Y cells were plated onto laminin coated dishes and on the next day were induced to differentiate by addition of 10 µM all-trans-retinoic acid (RA, Sigma). For pathway inhibition study SH-SY5Y were induced to differentiate by 10 µM in the absence or presence of 10 µM of LY294002, PD98059 and SB203580 (Sigma).
Total RNA was purified using Trizol. For microarray experiment RNA was additionally purified using RNeasy Mini Kit (Qiagen) according to manufacturer's instructions.
The probe to detect HOXD-AS1 was designed to be of 373 nucleotides, mapping to coordinates chr2:177,040,872-177,041,244 in GRCh37/hg19 Human Assembly of UCSC browser. The probe was synthesized (using cDNA derived from SH-SY5Y total RNA as a template) and Northern blot procedures were performed using DIG Northern Starter Kit according to manufacturer's instructions.
Quantitative PCR (qRT-PCR)
Total RNA was used as a template for reverse transcription using QuantiTect Reverse Transcription Kit (Qiagen) using random hexamer primers. The transcript levels were analyzed by qRT-PCR run on Rotor-Gene Q machine using Rotor-Gene SYBR Green PCR Kit (Qiagen). The primers used throughout this study are listed in Additional file 17.
Knock-down experiment using small interfering RNA (siRNA)
siRNA duplexes and control ON-TARGETplus non-targeting pool (Dharmacon) were introduced into SH-SY5Y cells using AmaxaNucleofector System (Lonza AG) according to manufacturer"s instructions. Cells were plated onto laminin coated dishes and 24 hours later were induced to differentiate with 10 µM RA. 48 hours later cells were harvested for RNA analysis. The following siRNA target sequences (Dharmacon) were used to knock-down HOXD-AS1: GAAAGAAGGACCAAAGTAA, GCACAAAGGAACAAGGAAA.
Total RNA was amplified using IlluminaTotalPrep RNA Amplification Kit (Ambion) and amplified cRNA was hybridized to HumanHT-12 v4 BeadChip according to manufacturer"s instructions. Briefly, 500 ng of total RNA was converted to double stranded cDNA using T7-oligo(dT) primers followed by IVT reaction to produce biotinylatedcRNA. The chips were scanned on IlluminaBeadArray Reader. The microarray data were deposited to NCBI GSE40680.
Analysis of microarray data from knock-down experiments
Expression values from four technical replicates were analyzed for each of the three biological samples (two siRNA knock-down experiments and one non-matching siRNA control). After RMA normalization, microarray signal distributions were examined. Multiple statistical criteria were used to obtain robust results of differential expression analysis. A differentially expressed probe simultaneously satisfied each of the following criteria: 1) Student's t-test P(sample vs. control) <0.05, 2) fold change ratio of median sample value to median control value > 1.3 or >1.4, 3) the number of sample replicas which signal value is higher than median control value is not less than 3. Based on these criteria 98 differentially expressed probes were selected.
Computational pipeline to define and classify probe sets for lncRNAs on Affymetrix U133- Plus-2.0
Noncoding status of the probe sets was confirmed by assuring that their sequences share no homology with existing protein-coding genes with NCBI BLAST . To further confirm that the selected transcripts were not protein-coding by the base composistion statistical criteria, CRITICA software was used as an additional filter . The overlap length distribution of the probe sets and the noncoding transcripts was calculated and parametrized with a 3-component mixture model, a sum of 3 extended power-law distribution functions by least squares method . To generate a high-confidence subset of lncRNA genes used in the study, the all Affymetrix U133-Plus-2.0 probes were matched against Gencode v.19 database of lncRNA transcripts using NCBI BLAST tool with the following options:-F F -g F -a 4 -K 1 -e 0.001.
Computational analysis of clinical microarray data
For identification of lncRNAs differentially expressed in neuroblastoma, GSE12460  AffymetrixU133-Plus-2.0 data from GEO database were used. This dataset contains 64 neuroblastoma (stages 1 to 4) tumor samples, in the genomes of 14 of which MYCN gene was amplified. For clinical analysis, lncRNA longer than 200 bp were selected. Mann-Whitney U test with null-hypothesis that the expression of a given lncRNA in the samples of neuroblastoma patients diagnosed at stages I or II is distributed equivalently to the patients diagnosed at stages III or IV and Student's t-test with null hypothesis that the population means are equal were applied with FDR control . For every lncRNA Kendall's tau correlation was calculated across the patients sorted by their diagnosed neuroblastoma stages (I to IV). To study relapsing neuroblastoma tumors 102 patients of GSE3446 dataset  was used. To study the effect of RNA gene expression on 5-years patient survival 44 patients of GSE16237 dataset  was used. Mann-Whitney U test for assessing gene differential expression and PAM analysis  for gene signature identification were used for these two datasets.
RefSeq genes located in the vicinity of +/-10 kb from each studied lncRNAs were defined as neighboring. For each neighboring gene, the best matching Affymetrix probe set was found, according to APMA annotation (30). For each such probe set, the best matching mRNA was identified. For all such mRNAs found for all neighboring genes Entrez IDs were found according to UniProt annotation. For each Entrez ID, the corresponding GO were found from Panther database (http://www.pantherdb.org/). Enrichment was considered significant for ontologies with P < 0.01 and enrichment value not less than 2.
Fluorescence in situ hybridization
RNA visualization was performed using QuantiGeneViewRNA technology (Panomics). RNA staining was performed according to manufacturer's protocol. The cells were counterstained with DAPI to label nuclei and images were acquired on Zeiss LSM 5 DUO confocal microscope.
The authors declare that they have no competing interests.
Publication of this article was funded by Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore.
This article has been published as part of BMC Genomics Volume 15 Supplement 9, 2014: Thirteenth International Conference on Bioinformatics (InCoB2014): Computational Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcgenomics/supplements/15/S9.
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