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Abstract

BACKGROUND:

Many studies have demonstrated that the long non-coding RNA (lncRNA), NEAT1_1, plays critical roles in various human tumor entities and is related to the survival of patients with malignancies. However, the role of NEAT1_1 in diffuse large B cell lymphoma (DLBCL) remains unclear. The aim of this study was to investigate the role of NEAT1_1 in DLBCL.

METHODS:

The expression of NEAT1_1 was evaluated in paraffin-embedded tissues from 64 DLBCL patients and 15 lymphnoditis patients using the ISH method. The correlations between the expression levels of NEAT1_1 and clinical-pathological features and patients’ survival were also analyzed. After knocking down NEAT1_1 using shRNA in the DLBCL cell lines OCI-Ly1 and SUDHL-4, cell viability, apoptosis and migration were assessed by performing CCK8 assays, annexin V-FITC/PI double staining assays and migration filter assays, respectively.

RESULTS:

NEAT1_1 expression was increased in DLBCL tissue compared to lymphnoditis tissue samples ( $P<$ 0.001). The NEAT1_1 level was positively related to stage ( $P=$ 0.031), IPI ( $P=$ 0.017), extranodal site involvement ( $P=$ 0.042) and drug response ( $P=$ 0.040). Kaplan-Meier analysis showed that high expression levels of NEAT1_1 were correlated with a poor prognosis in DLBCL patients. After shRNA-NEAT1_1 was transfected into OCI-Ly1 and SUDHL-4 for 24 h, the NEAT1_1 level decreased to approximately one-third the level of the control. Moreover, the viability and migration ability of the DLBCL cell lines were significantly suppressed. shRNA-NEAT1_1 induced apoptosis in both DLBCL cell lines.

CONCLUSIONS:

Our results showed that NEAT1_1 plays an oncogenic role in DLBCL. NEAT1_1 expression may serve as a predictive marker for DLBCL patients.

Keywords

Diffuse large B cell lymphoma long non-coding RNA NEAT1_1

1. Introduction

Diffuse proliferation of large neoplastic B lymphoid cells with a nuclear size equal to or exceeding that of normal macrophage nuclei is defined as diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin lymphoma (NHL), accounting for 30%–40% of adult NHLs. In Europe and the USA, the current annual incidence of NHL is estimated at 15–20 cases/100,000, comprising approximately 5% of all malignancies in western countries [1]. The incidence of DLBCL in Asia is approximately 3.26/100,000 [2]. The pathological morphology of DLBCL is complex and volatile. During the last decade, the International Prognostic Index (IPI) has become the most frequently used prognostic factor in DLBCL [3]. The IPI scoring system includes age, performance status, serum lactate dehydrogenase (LDH) level, stage and extranodal involvement. Although IPI scoring systems have been useful for risk stratification for several decades, it is unreliable for predicting outcomes in individual patients because of the biological heterogeneity of DLBCL. New prognostic tools and molecular methods have led to the identification of a novel basis for subtype/subset-specific index divisions, demonstrating molecular heterogeneity within morphologically similar tumors and linking gene expression profiles (GEP) to prognosis. DLBCL is usually aggressive, and 80% of aggressive lymphomas are DLBCL. Approximately 60% of DLBCL patients’ disease progresses into advanced stage lymphoma (usually stage III or IV) [4]. Different treatment paradigms should be performed at an early date to improve the survival of these patients. Exploring a novel specific and sensitive biomarker is indispensable for predicting the clinical course of DLBCL.

Non-coding RNAs recognized as a large group of endogenous RNA molecules without protein coding capacity that have specialized cellular and molecular functions. Molecules of 200 nt in size are standard, and those that are greater than or equal to 200 nucleotides are named long non-coding RNAs (lncRNAs), serving as a molecular signal, decoy, guide and scaffold [5]. Verma performed a systematic analysis of novel lncRNAs from the poly-adenylated transcriptomes of 116 primary DLBCL samples, and they identified 2,642 novel, multiexonic lncRNAs expressed in more than one tumor, two-thirds of which are not expressed in normal B cells [6]. Increasing evidence has shown that lncRNAs work as a biomarkers in various human cancers, such as lung cancer, breast cancer, gastric cancer, to reflect disease progression [7, 8, 9]. Aberrantly expressed lncRNAs exhibit unique profiles in different tumors. The specific expression of lncRNAs may correlate with DLBCL biological characteristics, such as treatment response and prognosis. The NEAT1 gene is transcribed as two major isoforms that overlap completely at their 5’ ends: the shorter, polyadenylated NEAT1_1 and the longer NEAT1_2 isoform (also called MEN $\beta$ , 23 kb in humans). Many studies have shown that the shorter isoform, NEAT1_1, promotes tumor growth and metastasis [10]. Whether NEAT1_1 plays an important role in lymphoma remains unclear. In this study, we investigated the correlation between NEAT1_1 expression and the clinicopathological features and survival outcomes of DLBCL patients. Moreover, we silenced NEAT1_1 in DLBCL cell lines and found that NEAT1_1 participated in both the proliferation and migration processes.

2. Materials and methods

2.1 Patients and tissue samples

Paraffin sections were obtained from 64 patients who were diagnosed with DLBCL enrolled at the department of Hematology at the time of diagnosis between January 2007 and June 2010. The diagnosis of DLBCL was confirmed based on the 2008 WHO classification criteria. All of these patients underwent molecular and phenotypic classification with the available clinical data. Paraffin sections from 15 lymphnoditis patients were also from these two hospitals. Data were obtained by outpatient chart review and contacting the patients by telephone or mail. The study was approved by the Ethics Committee these two Hospitals and was performed in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments. The ethical permission and informed consent were obtained from all participants. All study subjects provided written informed consent for the use of their tissue samples in the present study. We retrospectively collected the patients’ clinicopathological characteristics, including sex, age, stage, pathological type, IPI score, LDH level, and survival information. The follow-up was 60 months.

2.2 In situ hybridization

Hematoxylin and eosin-stained slides from each tumor block were reviewed, and representative areas with the highest percentage of tumor cells were selected for TMA construction. ISH was performed on 3- $\mu$ m TMA sections using a NEAT1_1 probe(designed with exiqon Web 5’-AACGCACAAGAAGGCAGGC AA-3’). Briefly, the tissue slides were prehybridized in a hybridization solution (Exiqon Company, Denmark) at 59 ${}^{\circ}$ C for 2 hours. Ten picomoles of 5’ 6-FAM-labelled miRCURYTM locked nucleic acid (LNA) detection probes (Exiqon, Denmark) complementary to NEAT1_1 or scrambled microRNA were added and hybridized for 16 hours at a temperature of 21 ${}^{\circ}$ C below the calculated melting temperature of the LNA probe. The slides were counterstained with nuclear fast red to visualize the nuclei and then mounted in aqueous mounting medium (Maixin Biotechnology Company, China). Then, tissue sections were scored manually semiquantitatively for cytoplasmic staining. The dominant staining intensity in tumor cells was scored as 0 $=$ negative; 1 $=$ weak (0 $\sim$ 25%); 2 $=$ intermediate (26 $\sim$ 50%); 3 $=$ strong (51 $\sim$ 75%); and 4 $=$ extra strong (76 $\sim$ 100%). We calculated the five fields and added the scores together.

2.3 Cell culture

OCI-Ly1 and SUDHL-4 cells were maintained in RPMI-1640 medium (Gibco, Carlsbad, CA, USA) containing 10% heat-inactivated fetal bovine serum albumin (Logan, USA) in a 95% humidified incubator with 5% CO ${}_{2}$ at 37 ${}^{\circ}$ C.

2.4 shRNA plasmid construction and transduction

The shRNAs of the human NEAT1_1 construct lentiviral vector were constructed by Jierui BioCom, with a sequences of 5’-GUGAGAAGUUGCUUAGAA ACUUUCC-3’. The empty vector pLKO.1 is control. First, OCI-Ly1 and SUDHL-4 cells were starved in RPMI-1640 medium without fetal bovine serum for 24 h, then transduced with the shRNA-NEAT1_1 using Lipofectamine3000. After 8 hours, we replaced the medium with fresh RPMI-1640 medium with 10%FBS and G418.

2.5 Quantitative real-time PCR analysis

Total RNAs from untreated cells and those transduced with NC or shRNA-NEAT1_1 cells were extracted using TRIzol (Invitrogen, USA) according to the manufacturer’s instructions. The cDNA was synthesized using the MLV transcriptase Kit (Invitrogen, USA). The quantitative analysis of the NEAT1_1 mRNA level was evaluated using qRT-PCR, and $\beta$ -actin was used as an endogenous control. The primers for NEAT1_1 were forward, 5’-CTTCCTCCCTTTAA CTTATCCATTCAC-3’, and reverse, 5’-CTCTTCCTC CACCATTACCAACAATAC-3’. The primers for $\beta$ -actin were forward, 5’-TGAAGTGTGACGTGGACA TC-3’ and reverse, 5’-GGAGGAGCAATGATCTTG AT-3’. The fold changes were calculated by relative quantification with the 2 ${}^{-\Delta\Delta\text{Ct}}$ method. All of the reactions were performed in a 20- $\mu$ l reaction volume in triplicate.

2.6 Cell viability assay

The cells of three groups were seeded into 96-well culture plates at a density of 1.5 $\times$ 10 ${}^{4}$ cells/well in 100 $\mu$ l medium for 24 h. Viable cells were evaluated with the CCK-8 Assay according to the manufacturer’s instructions. The optical density of each well was read at 450 nm using a microplate reader (ELX800, Bio-Tek, USA). The viability was assessed with the following equation: Viability $=$ Experimental/Untreated Control.

2.7 Cell apoptosis assay

Apoptotic and dead cells from the three groups were determined using FITC-labelled Annexin V and propidium iodide (PI) staining (BD Bioscience, USA), followed by flow cytometry. The cells were collected and resuspended in binding buffer at a concentration of 3 $\times$ 10 ${}^{6}$ /mL. Then, 100 $\mu$ l of cell suspension was added to 5 $\mu$ l of Annexin V-FITC and 10 $\mu$ l of PI and mixed for 15 min in the dark at room temperature. Next, 400 $\mu$ l of PBS was added to the solution. A FACScan instrument (Becton Dickinson, USA) was used to count the cells (1 $\times$ 10 ${}^{3}$ ) at an excitation wavelength of 490 nm. CellQuest software was used for data collection and processing.

Figure 1.

NEAT1_1 was highly expressed in DLBCL. Representative images of in situ hybridization for NEAT1_1 in DLBCL tissues and lymphnoditis tissues. For positive NEAT1_1 staining, purple-blue circumnuclear grains were observed. For the strong positive group, navy blue grains were widely distributed in the cell, particularly in the nucleus. A) and D) are negative in lymphnoditis tissue; B) and E) are weak positive of NEAT1_1 in DLBCL tissue; C) and F) are extra strong positive of NEAT1_1 in DLBCL tissue. A, B and C are at 200 $\times$ magnification; D, E and F are at 400 $\times$ magnification. Very weakly positive staining scattered throughout the cell was observed in the lymphnoditis samples.

2.8 Migration assay

The migration of OCI-Ly1 and SUDHL-4 cells was assayed using a Boyden chamber (Haimen, Jiangsu, China). Cells from the three groups were plated at 1 $\times$ 10 ${}^{6}$ /mL in the upper chamber. The 10% FCS RPMI-1640 medium was added to the lower chamber. After incubating for 24 h, non-migrating cells were removed from the top well, whereas the bottom cells were collected and counted by trypan blue exclusion assay in triplicate.

2.9 Statistical analysis

Comparisons of NEAT1_1 expression between DLBCL and lymphnoditis tissues were assessed using an unpaired t-test. One-way ANOVA was used to identify the impact of clinicopathological factors and NEAT1_1 expression in patients with DLBCL. Patient survival rates were calculated using the Kaplan-Meier method, and statistically significant differences in survival were identified using the log-rank test. All $P$ values were two-tailed. Values of $P<$ 0.05 were considered significant. All analyses were performed using SPSS software version 19.0.

3. Results

3.1 NEAT1_1 upregulation within DLBCL tissue

We compared the NEAT1_1 expression levels between the DLBCL and lymphnoditis tissues. NEAT1_1 expression in the DLBCL tissues was significantly increased compared to that in the lymphnoditis tissues (10.20 $\pm$ 5.05 vs. 1.87 $\pm$ 0.88; $P<$ 0.000). As shown in Fig. 1, in the DLBCL tissues, there were dark blue positive particles in the plasma. For the lymphnoditis tissues, there were some light blue fine-grained particles. A and D indicate negative lymphnoditis tissues, B and E indicate weak positive DLBCL tissues, and C and F indicate extra strong positive tissues. A, B and C were under a 20-power binocular microscope. D, E and F were under a 40-power binocular microscope. These data suggested that NEAT1_1 was upregulated in DLBCL.

3.2 Correlation between NEAT1_1 expression and clinicopathological characteristics

We correlated NEAT1_1 expression levels with the clinicopathological characteristics of the patients with DLBCL (Table 1). A total of 64 DLBCL patients were enrolled in this study. Among them, 36 (56.25%) were male, 33 (51.56%) were $<$ 60 years old, and 35 (54.68%) had stage I–II disease. The NEAT1_1 level was positively related to stage ( $P=$ 0.031), IPI ( $P=$ 0.017), extranodal site involvement ( $P=$ 0.042) and drug response ( $P=$ 0.040). However, NEAT1_1 expression was not related to other characteristics such as patient age, sex, B symptom, or pathological type.

Table 1
Clinicopathological characteristics of the 64 DLBCL patients

Characteristic	N (%)		P value
	Low	High
Sex			0.617
Male	20 (25.97)	12 (46.15)
Female	18 (74.03)	14 (53.85)
Age (y)			0.316
$\leqslant$ 60	21 (55.26)	11 (42.31)
$ï ¼ ž$ 60	17 (44.74)	15 (57.69)
Ann Arbor stages			0.031
I–II	25 (65.79)	10 (38.46)
III–IV	13 (34.21)	16 (61.54)
B system			0.767
Negative	19 (50)	12 (46.15)
Positive	19 (50)	14 (53.85)
Pathological type			0.425
GCB	20 (52.63)	11 (42.31)
ABC	18 (47.37)	15 (57.69)
Extranodal site involvement			0.042
No	23 (60.52)	9
Yes	15 (39.48)	17
ECOG			0.272
0–1	24 (55.26)	16
$\geqslant$ 2	14 (44.73)	10
IPI score			0.017
0–2	26 (68.42)	10 (38.46)
3–5	12 (31.58)	16 (61.54)
Drug response			0.040
CR and PR	35 (92.11)	19 (73.08)
NR	3 (7.89)	7 (26.93)
LDH			0.190
Normal	18 (47.37)	8 (30.77)
High	20 (52.63)	18 (69.23)
CRP			0.541
Normal	22 (66.23)	13
High	16 (33.77)	13

GCB is germinal center B cell-like DLBCL; ABC is activated B-cell-like DLBCL.

3.3 Correlation between high NEAT1_1 expression levels and poor prognosis in patients with DLBCL

We evaluated the NEAT1_1 level using the ISH method. A total score equal to or greater than 10 was used as a cutoff value to divide all 64 patients into two groups: the low expression group ( $n=$ 38) and the high expression group ( $n=$ 26). To further evaluate whether NEAT1_1 upregulation was linked to the prognosis of patients who underwent radical resection of DLBCL, we analyzed the prognostic value of NEAT1_1 expression levels in 64 patients with DLBCL using Kaplan-Meier analysis. The follow-up period for the studied patients was 60 months. Among the 64 patients, 9 of 38 (23.7%) with low NEAT1_1 expression levels died. In the high NEAT1_1 expression group, 12 of 26 (46.2%) died ( $P=$ 0.032; Fig. 2B).

Figure 2.

NEAT1_1 is a factor associated with a poor prognosis of DLBCL. A) The expression of NEAT1_1 was detected in two groups using the ISH method. B) Kaplan-Meier survival analysis of 64 patients with DLBCL comparing high and low NEAT1_1 expression ( $P=$ 0.032).

3.4 The effect of NEAT1_1 on viability, apoptosis and migration in DLBCL cell

NEAT1_1 plays an oncogenic role in various cancers. Here, to determine the function of NEAT1_1 in DLBCL, we showed that shRNA-NEAT1_1 significantly suppressed NEAT1_1 expression in OCI-Ly1 and SUDHL-4 cells (Fig. 3A). For the cell viability of both cell lines, there was a significant reduction in cell proliferation in the shRNA-NEAT1_1 group (Fig. 3B). To determine the function of NEAT1_1 in migration, we used the Boyden chamber assay to assess OCI-Ly1 and SUDHL-4 cells. As shown in Fig. 3C, we found that there were significantly less migration through the basement membrane in the shRNA-NEAT1_1 group. Apoptosis experiments were also performed (Fig. 3D). The death and apoptosis rates were much higher in the shRNA-NEAT1_1 group than in the other two control groups.

Figure 3.

Knocking down NEAT1_1 inhibited DLBCL proliferation and migration in vivo. (A) After transducing the siRNA-NEAT1_1 into OCI-Ly1 and SUDHL-4 cells for 24 h, we detected NEAT1_1 using the QRT-PCR method. The siRNA effectively inhibited NEAT1_1expression in DLBCL cells ( ${}^{*}P<$ 0.001). (B) After transducing shRNA-NEAT1_1 for 24 h, the proliferation of siRNA-UCA1 groups in both DLBCL cell lines was significant reduced compared to the other two control groups ( ${}^{*}P<$ 0.01). (C) Assessment and quantification of cell migration changes in OCI-Ly1 and SUDHL-4 cells by transwell assays. The migrating OCI-Ly1 and SUDHL-4 cells were significantly fewer in the shRNA-NEAT1_1 group than in controls ( ${}^{*}P<$ 0.01). (D) The Annexin V and PI stain show that the apoptosis ratio is much higher in the shRNA-NEAT1_1 group than in controls ( ${}^{*}P<$ 0.05).

4. Discussion

Nuclear enriched abundant transcripts are located less than 70 kb apart on human chromosome 11. There are no more than three transcripts, including XIST, and two unique noncoding nuclear enriched abundant transcripts (NEAT) RNAs. Although NEAT1 and NEAT2 (also known as MALAT-1) share no significant homology with each other, both of them are conserved within the mammalian lineage. MALAT-1, as a metastasis driver, is widely credited, but NEAT1 has not been studied thoroughly as MALAT-1. NEAT1 comprises of two isoforms produced by alternative 3’-end processing (NEAT1_1 and NEAT1_2). NEAT1_1 is a 4-kb, unspliced, polyadenylated, nuclear restricted, noncoding transcript with two small regions of high conservation across several species, indicating its potentially important function. Clemson found that the large, tightly packaged structural aggregates of NEAT1_1 RNA, containing many self-complementary sequences and complex intra-molecular structures, forms quickly at the transcription site, which suggests that NEAT1_1 RNA may self-associate. Except for mitosis, NEAT1_1 RNA is tightly localized in paraspeckles, consistently overlapping with these structured concentrations of both PSP1 and p54. Overexpressed NEAT1_1 can increase the number of paraspeckles, which suggests that it is the functional isoform for paraspeckle formation. p54nrb, PSF, and PSP1 are characteristically enriched in paraspeckles, and NEAT1_1 is required for their localization. NEAT1_1 affects the formation and maintenance of these discrete nuclear domains [11]. In 2007, Hutchinson [12] first identified NEAT1_1 and reported that it was enriched by 25.6-fold in the nuclei of lymphoblasts, compared to 2.1-fold in fibroblasts. Blume [13] showed that the landscape of p53-dependent microRNA/non-coding RNA was induced in response to DNA damage in chronic lymphocytic leukemia (CLL). NEAT1_1 and long intergenic non-coding RNA p21 (lincRNA-p21) are induced in response to DNA damage in the presence of functional p53 but not in the presence of p53 mutations. This p53 dependence of NEAT1_1 and the lincRNA-p21 model were also verified in a lymphoma cell line. NEAT1_1 is highly expressed in lung cancer [14], colorectal cancer [15] and glioma [16]. NEAT1_1 expression patterns and the clinicopathological features of lymphoma are not well described thus far. Approximately 31% of all NHLs and 80% of aggressive lymphomas are DLBCLs. The WHO criteria classify DLBCL into several subtypes and a few distinct disease entities. Many molecular and immunophenotypic subgroups were described as prognostic indicators and provide insights into therapy. However, a large number of DLBCLs remain biologically heterogeneous without clear and acceptable criteria. A number of investigators have attempted to identify several highly specific and sensitive indices to be used for diagnosis or predicted prognosis. Here, our results showed that the NEAT1_1 expression level was increased in DLBCL tissue compared with acute lymphadenitis tissue ( $P<$ 0.001). Analysis of the relationship between the NEAT1_1 level in tumor tissues and clinicopathological characteristics in patients with DLBCL revealed that the NEAT1_1 level was positively related to stage, IPI, extranodal site involvement and drug response. However, we failed to identify a relationship between the NEAT1_1 level and patient age, sex, B symptoms, or pathological type. Kaplan-Meier analysis showed that higher NEAT1_1 expression levels were correlated with a poor prognosis in patients with DLBCL. Patients with a higher NEAT1_1 expression level had shorter DFS and OS than those with a lower NEAT1_1 expression level ( $P=$ 0.04 and 0.038, respectively). Furthermore, we verified the biological function of NEAT1_1 in DLBCL cell lines. Our data showed that shRNA-mediated knockdown of NEAT1_1 led to significant inhibition of cell viability and migration, and silencing NEAT1_1 in DLCBL cell lines significantly induced apoptosis.

In conclusion, our analysis showed that NEAT1_1 expression was increased in DLBCL tissues and positively related to a poor prognosis and shorter OS than patients with weak NEAT1_1 expression. Accordingly, NEAT1_1 expression may serve as a risk marker for patients with DLBCL. Furthermore, in vitro assay results also indicate that NEAT1_1 plays an oncogenic role in DLCBL. However, future studies are warranted to investigate the mechanism of NEAT1_1 in DLBCL.

Footnotes

Acknowledgments

This work was supported by grants from the Natural Science Foundation of Guangdong Pr (No. S20120100 08748 and S2013010014715) and National Natural Science Foundatio of China (No. 81400103).

References

Sehn

L.H.

Scott

D.W.

Chhanabhai

et al., Impact of concordant and discordant bone marrow involvement on outcome in diffuse large B-cell lymphoma treated with R-CHOP, J Clin Oncol 29 (2011), 1452–1457.

Bassig

B.A.

W.Y.

Mang

et al., Subtype-specific incidence rates of lymphoid malignancies in Hong Kong compared to the United States, 2001–2010, Cancer Epidemiol 15(42) (Mar. 2016), 15–23.

A predictive model for aggressive non-Hodgkin’s lymphoma. The International Non-Hodgkin’s Lymphoma Prognostic Factor Project, N Engl J Med 329 (1993), 987–994.

Vaque

J.P.

Martinez

and Batlle-Lopez

, B-cell lymphoma mutations: improving diagnostics and enabling targeted therapies, Haematologica 99 (2014), 222–31.

Yarmishyn

A.A.

and Kurochkin

I.V.

, Long noncoding RNAs: a potential novel class of cancer biomarkers, Front Genet 6 (23 Apr 2015), 145.

Verma

Jiang

et al., Transcriptome sequencing reveals thousands of novel long non-coding RNAs in B cell lymphoma, Genome Med 7(1) (1 Nov 2015), 110.

Chen

Wang

Zhang

et al., Long non-coding RNAs in non-small cell lung cancer as biomarkers and therapeutic targets, J Cell Mol Med 18(12) (Dec 2014), 2425–36.

Wang

Quan

Z.F.

et al., Functional roles of long non-coding RNA in human breast cancer, Asian Pac J Cancer Prev 15(15) (2014), 5993–5997.

Wan

Ding

Chen

et al., The functional sites of miRNAs and lncRNAs in gastric carcinogenesis, Tumour Biol 36(2) (Feb 2015), 521–532.

10.

Chakravarty

Sboner

Nair

S.S.

et al., The oestrogen receptor alpha-regulated lncRNA NEAT1 is a critical modulator of prostate cancer, Nat Commun 5 (2014), 5383.

11.

Clemson

C.M.

Hutchinson

J.N.

Sara

S.A.

et al., An architectural role for a nuclear noncoding RNA: NEAT1 RNA is essential for the structure of paraspeckles, Mol Cell 33(6) (27 Mar 2009), 717–726.

12.

Hutchinson

J.N.

Ensminger

A.W.

Clemson

C.M.

et al., A screen for nuclear transcripts identifies two linked noncoding RNAs associated with SC35 splicing domains, BMC Genomics 8 (1 Feb 2007), 39.

13.

Blume

C.J.

Hotz-Wagenblatt

Hüllein

et al., p53-dependent non-coding RNA networks in chronic lymphocytic leukemia, Leukemia 29(10) (Oct 2015), 2015–2023,

14.

Pan

L.J.

Zhong

T.F.

Tang

R.X.

et al., Upregulation and clinicopathological significance of long non-coding NEAT1 RNA in NSCLC tissues, Asian Pac J Cancer Prev 16(7) (2015), 2851–2855.

15.

Yang

Zhao

et al., Nuclear-enriched abundant transcript 1 as a diagnostic and prognostic biomarker in colorectal cancer, Mol Cancer 14(1) (9 Nov 2015), 191.

16.