The authors have declared that no competing interests exist.
Conceived and designed the experiments: FZ XWX YLY. Performed the experiments: FZ QW LX. Analyzed the data: FZ NYS DND. Contributed reagents/materials/analysis tools: RW SNW. Wrote the paper: FZ YLY.
Long noncoding RNAs (lncRNAs) are pervasively transcribed and play a key role in tumorigenesis. The aim of the study was to determine the lncRNA expression profile in astrocytomas and to assess its potential clinical value. We performed a three-step analysis to establish the lncRNA profile for astrocytoma: a) the lncRNA expression was examined on 3 astrocytomas as well as 3 NATs (normal adjacent tissues) using the lncRNA microarray; b) the top-hits were validated in 40 astrocytomas (WHO grade II-IV) by quantitative real time-PCR (qRT-PCR); c) the hits with significant differences were re-evaluated using qRT-PCR in 90 astrocytomas. Finally, 7 lncRNAs were found to have a significantly different expression profile in astrocytoma samples compared to the NAT samples. Unsupervised clustering analysis further revealed the potential of the 7-lncRNA profile to differentiate between tumors and NAT samples. The upregulation of ENST00000545440 and NR_002809 was associated with advanced clinical stages of astrocytoma. Using Kaplan-Meier survival analysis, we showed that the low expression of BC002811 or XLOC_010967, or the high expression of NR_002809 was significantly associated with poor patient survival. Moreover, Cox proportional hazard regression analysis revealed that this prognostic impact was independent of other clinicopathological factors. Our results indicate that the lncRNA profile may be a potential prognostic biomarker for the prediction of post-surgical outcomes.
Astrocytomas are the most common primary malignant brain tumor in the central nervous system [
Recently, long non-coding RNAs (lncRNAs) have attracted increasing scientific interest. LncRNAs are transcripts longer than 200 nucleotides that are not translated into proteins and are found in sense or antisense orientation to protein-coding genes, within introns of protein-coding genes or in intergenic regions of the genome [
Although the clinical stage of cancer is the primary predictor of survival for patients who have undergone surgery for most solid tumors, including astrocytoma, the predictions are not very accurate. Patients of the same stage with similar treatment may have very different clinical outcomes. Unique patterns of altered lncRNA expression may serve as novel molecular biomarkers for astrocytoma. Han et al. found that the lncRNA profile in GBM was significantly altered and may be involved in GBM pathogenesis [
In the present study, we investigated the lncRNA expression profile of human astrocytoma by comparing the expression levels of lncRNAs in WHO grade II-IV astrocytoma samples with that from normal adjacent tissue (NAT) samples using a high-throughput lncRNA microarray followed by quantitative real-time PCR (qRT-PCR). We observed a widespread variation in the levels of lncRNA expression during astrocytic tumorigenesis. Notably, the profile of seven specific lncRNAs exhibited great potential to differentiate astrocytomas from NAT samples. The upregulation of ENST00000545440 and NR_002809 was associated with advanced clinical stages of astrocytoma. Using the Kaplan-Meier survival analysis and univariate/multivariable statistical models, we showed that low expression levels of BC002811 or XLOC_010967 and high expression levels of NR_002809 were significantly associated with poor survival in astrocytoma patients. These results indicate that NR_002809, BC002811 and XLOC_010967 have the potential to serve as novel prognostic indicators of astrocytoma.
The present study included 130 patients who underwent surgical treatment for treat astrocytomas at the Third Affiliated Hospital of Soochow University between 2005 and 2013. The study was approved by the Research Ethics Board of the Third Affiliated Hospital of Soochow University, and written informed consent was obtained from each participant.
The astrocytoma cases were individuals with newly diagnosed, histologically confirmed primary astrocytomas. Histological subtypes were defined according to WHO criteria. There were no age, gender or cancer-grade restrictions on recruitment. The following inclusion criteria were used: (i) the absence of previous cancers or recurrent tumors, (ii) the absence of previous chemo- or radiotherapeutic treatment and (iii) the absence of synchronous multiple cancers. Sixty NAT samples were also analyzed and served as controls. The NAT samples were the same as those described in our previous work [
A multiphase, case-control study was designed to identify lncRNAs as potential markers for astrocytomas. In the initial biomarker screening stage, an lncRNA microarray was performed on 3 astrocytoma samples (1 WHO grade II, 1 WHO grade III, and 1 WHO grade IV) and 3 NAT samples to identify lncRNA differences between astrocytomas and controls. Subsequently, sequential validation was performed using qRT-PCR to refine the number of lncRNAs included in the astrocytoma signature. All 130 samples included in the confirmation stage were randomly separated into training (40 astrocytoma samples and 20 NAT samples) and validation (90 astrocytoma samples and 40 NAT samples) sets prior to analysis. The demographic and clinical features of the patients are listed in
Training Set | Validation set | ||||||
---|---|---|---|---|---|---|---|
Variable | Astrocytoma (n = 40) | NAT (n = 20) | Astrocytoma (n = 90) | NAT (n = 40) | |||
45.3 ± 13.3 | 44.1 ± 11.8 | 0.535 |
46.2 ± 15.0 | 44.8 ± 12.6 | 0.427 |
0.372 |
|
0.927 |
0.977 |
0.571 |
|||||
≤ 46 | 18 | 10 | 47 | 21 | |||
> 46 | 22 | 10 | 43 | 19 | |||
0.854 |
0.860 |
0.837 |
|||||
Male | 23 | 11 | 48 | 22 | |||
Female | 17 | 9 | 42 | 18 | |||
Diffuse astrocytoma (WHO grade II) | 15 | 29 | |||||
Anaplastic astrocytoma (WHO grade III) | 13 | 31 | |||||
Glioblastoma multiforme (GBM, WHO grade IV) | 12 | 30 | |||||
Alive | 30 | 57 | 0.270 |
||||
Dead | 10 | 33 | |||||
Mean survival time (months) | 42.3 ± 17.3 | 45.7 ± 8.6 |
a Astrocytoma samples from training set versus control samples from training set.
b Astrocytoma samples from validation set versus control samples from validation set.
c Astrocytoma samples from training set versus astrocytoma samples from validation set.
d Student's t-test.
e Two-sided λ2 test.
Total RNA was first extracted from 3 tumor samples and 3 NAT samples using Trizol reagent (Invitrogen) according to manufacturer’s protocol. The Agilent human lncRNA + mRNA Array v2.0 was used in this study. The microarray experiment and data analysis were performed by CapitalBio, Beijing, PR China. A detailed version of the procedure is included in the
Two algorithms, geNorm [
For qRT-PCR, the reverse transcription reactions were carried out with Reverse Transcriptase (SuperScript III, Invitrogen) according to the manufacturer’s instructions. Approximately 2μg total RNA was added to each reaction. The TaqMan gene expression assay (Invitrogen) was performed on an ABI 7500 system in a 20μl reaction. All the primers and probes were designed and produced by Invitrogen. The reactions were incubated at 95°C for 5 min, followed by 40 cycles of 95°C for 15 s, and 60° for 60 s. All quantitative PCR reactions were performed in triplicate. Each lncRNA in each sample was repeated by qRT-PCR for at least 3 times. The Ct value of each candidate lncRNA was then normalized to the expression value of GAPDH. Relative expression levels of the lncRNAs were calculated using the 2-△Ct method.
Statistical comparison of the demographic features between the astrocytoma and NAT samples, or between the astrocytoma samples from the training and validation sets, was performed by Student’s t-test or two-sided λ2 test. The differences were considered statistically significant when p < 0.05. We constructed the receiver operating characteristic (ROC) curve and calculated the area under the ROC curve (AUC) to evaluate the potential power of the lncRNA signature for astrocytoma. Risk score analysis was performed to evaluate the associations between the expression levels of the lncRNAs and astrocytoma. The risk score of each lncRNA, denoted as
A total of 130 histologically confirmed astrocytoma patients from the Third Affiliated Hospital of Soochow University, ranging from WHO grade II to grade IV, were enrolled in the present study. Additionally, 60 NAT samples were analyzed and served as controls. These samples were randomly assigned to a training set (40 astrocytoma samples and 20 NAT samples) or to a validation set (90 astrocytoma samples and 40 NAT samples). The demographic and clinical features of the astrocytoma and NAT samples are listed in
To select candidate lncRNA biomarkers for astrocytomas, we first performed an initial lncRNA screening of 3 astrocytoma samples and 3 NAT samples by lncRNA microarray. The results revealed that the lncRNA expression profiles varied between the astrocytomas and the NAT samples. Among the lncRNAs detected, 3806 lncRNAs with a fold change > 2 or < 0.5 and a q value < 0.05 were found to have significantly different expression levels in astrocytoma samples compared to NAT samples. Of those, 1196 were downregulated and 2610 were upregulated. These lncRNAs were listed in
Proper normalization is a critical aspect of quantitative gene expression analysis. An algorithm known as geNorm was used to assess the expression stability of 4 putative normalizer genes (GAPDH, β-actin, 18s rRNA and 28s rRNA). The geNorm analysis clearly showed that GAPDH was highly consistent in their expression levels across 40 astrocytoma tissue samples and 20 NAT samples in the training set (Fig A in
The 59 candidate lncRNAs were individually assayed by qRT-PCR in the 130 astrocytoma samples and 60 NAT samples, including the samples used in the microarray, to validate their differential expression. Only the lncRNAs with a mean fold change > 2 or < 0.5 and a p-value < 0.05 were selected from the training set for further validation. LncRNAs were excluded from further analysis when their expression levels were not significantly altered, the assays were not linear, the detection rates were <50%, or the Ct values were higher than 35 in the qRT-PCR assay. Based on these parameters, our analysis generated a total of 9 lncRNAs that were differentially expressed between astrocytomas and NAT samples (
LncRNA | Training set | Validation set | Training + Validation | Result | |||
---|---|---|---|---|---|---|---|
Mean fold (tumor/NAT) | p-value | Mean fold (tumor/NAT) | p-value | Mean fold (tumor/NAT) | p-value | ||
ENST00000244906 | 5.05 | 3.61 × 10−4 | 3.19 | 4.74 × 10−2 | 3.399 | 4.06 × 10−3 | significant |
ENST00000545440 | 2.45 | 6.82 × 10−3 | 3.72 | 1.64 × 10−6 | 2.610 | 2.61 × 10−6 | significant |
NR_002809 | 2.21 | 1.30 × 10−2 | 2.47 | 3.44 × 10−2 | 2.201 | 1.24 × 10−2 | significant |
ENST00000436616 | 2.74 | 4.71 × 10−2 | 2.35 | 1.31 × 10−2 | 2.050 | 1.00 × 10−2 | significant |
XLOC_010967 | 0.40 | 3.62 × 10−2 | 0.42 | 5.18 × 10−4 | 0.474 | 7.81 × 10−4 | significant |
BC002811 | 0.31 | 5.37 × 10−6 | 0.48 | 2.39 × 10−4 | 0.429 | 7.11 × 10−8 | significant |
ASO1937 | 0.19 | 1.50 × 10−3 | 0.34 | 4.88 × 10−3 | 0.290 | 3.63 × 10−5 | significant |
ENST00000424474 | 2.09 | 4.87 × 10−3 | 1.25 | 3.64 × 10−1 | not significant | ||
uc002hng.1 | 0.48 | 1.47 × 10−5 | 0.88 | 6.74 × 10−2 | not significant |
The differential expression of lncRNAs between astrocytoma samples and NAT samples was further characterized by an unsupervised clustering analysis that was blind to the clinical annotations. The dendrogram generated by the cluster analysis showed a clear separation of the astrocytoma samples from the NAT samples based on their respective lncRNA profiles (
The lncRNA expression levels, as measured by qRT-PCR, were normalized, mean-centered, clustered, and plotted as a heat map for the training set (A), the validation set (B), and all samples (C). The dendrogram generated by the cluster analysis shows a clear separation of the astrocytoma and the NAT samples based on the 9 or 7 lncRNA profiles.
Among the mRNAs detected, 3547 mRNAs with a fold change > 2 or < 0.5 and a p value < 0.05 were found to have significantly different expression levels in astrocytoma samples compared to NAT samples. Of those, 1959 were upregulated and 1588 were downregulated. Among the mRNAs with the raw gProcessed Signal > 500, two of the 5 most upregulated mRNAs (Tenascin-C, Aquaporin-1) and two of the 5 most downregulated mRNAs (HAPLN4, PPP2R2C) were validated in the training set (40 astrocytomas and 20 NAT samples). As shown in
To assess the power of the lncRNA signature, we used a risk score formula to calculate the risk score for patient samples and control samples in the training set. The samples were ranked according to their risk score and then divided into a high-risk group, which represented the predicted astrocytoma cases, and a low-risk group, which represented the predicted control individuals. The ROC (receiver operating characteristic) curve is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The frequency table and the ROC curve were then used to evaluate the power of the 9-lncRNA panel. The AUC for the combined 9 lncRNAs was 0.9992 (95% CI, 0.9990 to 1.0003) for the astrocytomas and controls (
We subsequently investigated whether lncRNA expression levels represented specific molecular signatures for subsets of astrocytomas. The expression levels of the 7 lncRNAs in the astrocytoma samples were stratified using 3 types of clinicopathological parameters (sex, age, and WHO grade). We assessed the relationship between these clinical features and the lncRNA expression levels. No lncRNAs were found to be differentially expressed when the astrocytoma samples were stratified by age or sex. However, 2 lncRNAs were found to be differentially expressed when the samples were stratified according to tumor grade. As shown in
The relative expression levels of ENST00000545440 and NR_002809 in each group are shown.
We next investigated the correlation between the lncRNA expression profiles and patient survival using the prospective follow-up data collected from the 130 astrocytoma patients. Due to the observation that 7 lncRNAs were differentially expressed between the astrocytoma patients and the controls, these lncRNAs were used for the survival analysis. The expression levels of these 7 lncRNAs in the astrocytoma samples were first stratified by the median value; then, the survival of the patients with high lncRNA expression levels (≥ median) was compared with the outcomes for patients with low lncRNA expression levels (< median), as determined by Kaplan-Meier survival analysis. We observed a marginally significant poorer survival rate in astrocytoma patients who expressed high levels of NR_002809 (p = 0.049,
(A) Kaplan-Meier survival analysis of the astrocytoma patients stratified according to the expression level of NR_002809. (B) Kaplan-Meier survival analysis of the astrocytoma patients stratified according to the expression level of BC002811. (C) Kaplan-Meier survival analysis of the astrocytoma patients stratified according to the expression level of XLOC_010967. (D) Kaplan-Meier survival analysis of the astrocytoma patients according to the 3-lncRNA signature stratified by risk score.
Subsequently, a univariate Cox proportional hazard regression model was performed to determine the influence of lncRNA expression, as well as clinicopathological characteristics (gender, age and WHO grade), on patient survival. WHO grade II was designated as the low pathological grade, and WHO III-IV was designated as the high pathological grade. This univariate analysis indicated that age, WHO grade and the expression levels of NR_002809, BC002811, and XLOC_010967 were significantly related to survival (hazard ratio >2 and p-value <0.05 were considered to be statistically significant), whereas gender was not (
Variable | Subset | Hazard ratio (95% CI) | p-value |
---|---|---|---|
Univariate analysis | |||
Gender | Female/Male | 1.032 (0.563–1.894) | 0.918 |
Age | Age≥46/Age<46 | 5.088 (2.420–10.696) | < 0.0001 |
WHO | High grade/Low grade | 3.167 (1.404–7.470) | 0.005 |
NR_002809 | High/Low | 2.962 (1.523–5.869) | 0.042 |
BC002811 | Low/High | 2.061 (1.041–3.965) | 0.037 |
XLOC_010967 | Low/High | 2.086 (1.097–3.968) | 0.025 |
Multivariate analysis | |||
Gender | Female/Male | 0.749 (0.395–1.422) | 0.377 |
Age | Age≥46/Age<46 | 6.163 (2.722–13.702) | < 0.0001 |
WHO | High grade/Low grade | 2.328 (0.966–5.442) | 0.051 |
NR_002809 | High/Low | 3.022 (1.129–8.087) | 0.028 |
BC002811 | Low/High | 4.573 (1.863–11.228) | 0.001 |
XLOC_010967 | Low/High | 2.782 (1.122–6.893) | 0.027 |
In the present study, we examined the lncRNA profiles of astrocytoma samples and NAT samples and identified a unique astrocytoma signature composed of 7 differentially expressed lncRNAs. Unsupervised clustering analysis revealed a clear separation of astrocytoma samples from NAT samples, indicating that these 7 lncRNAs may represent an astrocytoma lncRNA ‘‘fingerprint.” The upregulation of ENST00000545440 and NR_002809 was associated with advanced clinical stages of astrocytoma. Moreover, the low expression of BC002811 and XLOC_010967, or high expression of NR_002809 was significantly associated with poor patient survival.
An increasing number of studies have suggested deregulation of lncRNAs in various cancers. In the present study, we provide a ''proof-of-principle'' approach to identify a particular disease-specific lncRNA profile. This approach included microarray analysis as an initial screening followed by multiple qRT-PCR validation sets at the individual level. Employing this approach, we identified a unique expression profile for astrocytoma. In the present study, we identified a unique lncRNA signature of astrocytoma comprising 7 differentially expressed lncRNAs. However, these lncRNAs were different from those found in previous studies. Indeed, we investigate the lncRNA expression profiles in a sample set including 40 NAT samples and 130 astrocytoma samples across grades II-IV, while the lncRNA signatures established by other studies were only from a limited patient cohort, or were only specific to GBM, or were not associated with prognosis, or were just from statistical analysis by literature screen. Though no single lncRNA was found in common, our study on astrocytoma lncRNAs was more comprehensive and more systematic. The reason for limited overlap between our study and other previous studies may be due to the differences in study design, race, sample size and methodology.
Like protein-coding genes and microRNAs, lncRNAs can function as oncogenes or tumor suppressors during cancer progression. The mechanisms through which lncRNAs contribute to the regulatory networks that underpin cancer development are diverse. The functions of lncRNAs are intimately linked to their gene structures. Thus, understanding the gene structure for these molecules is essential to determining how they function. Though many investigators have suggested the presence and importance of structural elements within lncRNAs, lncRNA structure remains poorly understood. LncRNAs act through a variety of mechanisms such as remodeling of chromatin, transcriptional co-activation or co-repression, protein inhibition, and posttranscriptional modifiers or decoy elements [
Seeking novel molecular biomarkers of malignancy is important and helpful for clinical diagnosis and management. The discovery that lncRNAs are key regulators in cancer transformation and progression leads to intriguing possibilities of application for diagnostics and therapeutics. The use of noncoding RNAs in diagnostics has intrinsic advantages over protein-coding RNAs. Although lncRNAs may require post-transcriptional modifications or protein interactions to function, because the mature product is the functional end-product, measurement of its expression directly represents the levels of the active molecule. Many lncRNAs are expressed in a tissue- and cancer-type restricted manner and have already been shown to be useful as prognostic markers. HOTAIR was strongly increased in primary tumors and metastases of breast cancer patients, with expression levels positively correlated with a poor survival rate [
In conclusion, our study identified an lncRNA signature of astrocytoma and presents the first assessment of the impact of lncRNAs on the survival of astrocytoma patients. Further validation studies in prospective cohorts and in cohorts from different institutions are needed to test the prognostic power of the signature before it is applied in a clinical setting. This observation should initiate further research to elucidate the functional effects of these lncRNAs, which will improve our knowledge regarding the role that these novel biomarkers play in carcinogenesis and will elucidate their potential as therapeutic agents.
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The expression levels of 4 selected candidates were measured using qRT-PCR from astrocytoma (n = 40) and NAT (n = 20) samples. The CT values were averaged, and the standard deviation was calculated. A) Identification of the optimal number of reference genes for accurate normalization using geNorm. B) Identification of the most stable reference genes using NormFinder.
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For comparison, the expression levels of these 4 genes in NAT samples were arbitrarily set at 1.
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