Decreased expression of lncRNA VPS9D1-AS1 in gastric cancer and its clinical significance

Abstract

BACKGROUND:

A recent study has demonstrated that the long non-coding RNA VPS9D1-AS1 is highly expressed in colorectal cancer and predicts poor prognosis. However, roles of VPS9D1-AS1 in gastric cancer remained poorly understood.

OBJECTIVE:

The aim of this study is to decipher the expression of VPS9D1-AS1 in gastric cancer (GC) patients, so as to assess whether or not it could be used as a novel biomarker for prognosis in gastric cancer patients.

METHODS:

The expression of VPS9D1-AS1 was examined in cancer tissues and paired adjacent non-tumorous tissues from 126 gastric cancer patients using qRT-PCR. Correlations between the expression of VPS9D1-AS1 and clinicopathological parameters and patients’ survival were analyzed.

RESULTS:

VPS9D1-AS1 expression was downregulated in gastric cancer tissues than that in adjacent non-tumorous tissues ( $P<$ 0.001). VPS9D1-AS1 expression level was markedly correlated with tumor size and TNM stage in gastric cancer. Kaplan-Meier analysis showed low expression of VPS9D1-AS1 were correlated with poor overall and disease free survival. On multivariate analysis, the hazard ratio of VPS9D1-AS expression was 0.30 (95% CI $=$ 0.13–0.66, $P=$ 0.003) for overall survival.

CONCLUSIONS:

Overall, our data suggest that downregulated VPS9D1-AS1 may be used as a novel prognosis predictor of gastric cancer.

Keywords

Long non-coding RNA VPS9D1-AS1 gastric cancer downregulation

1. Introduction

Gastric cancer (GC) remains one of the most common malignancies and the third leading cause of cancer-related death worldwide [1, 2]. Although numerous advances in diagnostic interventions and therapeutic alternatives have been made in the past decades, gastric cancer still constitutes a major challenge because more than half of patients have stepped into a progressive stage at diagnosis [3, 4]. The 5-year overall survival rate of GC patients is estimated to be less than 30% [1, 4]. It is thus urgent to find novel molecular biomarkers for diagnosis and treatment of GC.

Recently, development in sequencing technology and bioinformatics has contributed to the findings that the vast majority of human genomes are actively transcribed into non-coding RNAs without protein coding potentials [5, 6], which are classified into long non-coding RNAs (lncRNAs) and small non-coding RNAs such as microRNAs according to their lengths [7]. Previous studies have revealed that microRNAs play important roles in cancer development [8]. Increasing evidences are emerging to show that dysregulated expression of lncRNA may be associated with a variety of biological processes including cell growth, differentiation, senescence, tumorigenesis and treatment response [6, 7, 9]. For example, the lncRNA XIST, which was essential for X chromosome inactivation during female development [10], was found to be overexpressed in GC and promote malignant phenotype of GC cell lines in vitro and in vivo [11]. HOTAIR acted as a scaffold to recruit proteins required for chromatin remodeling [12] and high expression of HOTAIR predicted poor prognosis of several human cancers [13, 14]. The lncRNA VPS9D1-AS1 was recently found to be upregulated in human colorectal cancer tissues and considered to be a potential prognosis biomarker via re-mining the Affymetrix array data [15]. However, roles of VPS9D1-AS1 in GC remain poorly understood.

In this study, the expression of VPS9D1-AS1 was investigated in GC cancer tissues and corresponding adjacent non-tumorous tissues. The association between expression of VPS9D1-AS1 and clinicopathological parameters was investigated. This could highlight the possibility whether VPS9D1-AS1 could be used as a novel diagnosis biomarker or treatment target of GC.

2. Materials and methods

2.1 Clinical samples

A total of 126 primary GC cancer tissues and matched adjacent non-tumorous tissues were obtained from patients who underwent gastrectomy in the Sun Yan-sen University Cancer Center between April 2007 and September 2010. No patients enrolled into our study received radiotherapy or chemotherapy prior to surgery. Informed consent of all the patients was obtained for using the tissue samples for research purpose. Our study about the human specimens was approved from the Ethics Committee of the Sun Yan-sen University Cancer Center and in accordance with the principles set out in the Declaration of Helsinki 1964 as modified by subsequent revisions. The samples were immediately snap-frozen in liquid nitrogen and stored in $-$ 80 ${{}^{\circ}}$ C until use. The clinicopathological characteristics were obtained from the medical record.

2.2 Cell lines and cell culture

The GC cell lines and one normal gastric epithelial cell line (GES1) were obtained from the Shanghai Cell Bank of the Chinese Academy of Sciences (Shanghai, China). The gastric cancer cells were cultured in RPMI1640 (MGC803, BGC823 and MKN45) or in DMEM (SGC7901 and AGS) supplemented with 10% fetal bovine serum (FBS), 100 U/ml penicillin, and 100 mg/ml streptomycin (Invitrogen, Carlsbad, CA, USA) at 37 ${}^{\circ}$ C in 5% CO ${}_{2}$ .

2.3 Quantitative real-time PCR (qRT-PCR) analysis

Total RNA from tissue samples or cells was extracted with TRIzol Reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. Reverse transcription from 1 $\mu$ g of total RNA was performed in a volume of 20 $\mu$ l mix containing random and oligo dT primers according to instructions of the PrimeScript RT Kit (TaKaRa, Dalian, China). For qRT-PCR analysis, SYBR mix (Promega) was used to determine the expression levels of VPS9D1-AS1. The primer sequences were used as follows:

VPS9D1-AS1 forward, 5’-CTCAGCAGTAAGTA ACAGTGGTAGA-3’ VPS9D1-AS1 reverse, 5’-CTTCAGCATCTTGGA GGTGTC-3’ GAPDH forward, 5’-GGAGCGAGATCCCTCCA AAAT-3’ GAPDH reverse, 5’-GGCTGTTGTCATACTTCT CATGG-3’

The average value from three biological replicates with three technical replicates was used to calculate the relative amount of VPS9D1-AS1. The ABI PRISM Cycler software (ABI, Foster City, CA, USA) was used to calculate the threshold cycle number (Ct) value for GAPDH and VPS9D1-AS1 during the log phase of each cycle. VPS9D1-AS1 levels were normalized to the expression level of GAPDH ( $\Delta$ Ct $=$ Ct ${}_{\text{VPS9D1-AS1}}$ $-$ Ct ${}_{\text{GAPDH}}$ ). These values were compared with values obtained from GES1 cells according to the formula: 2 ${}^{-\Delta\Delta\text{Ct}}$ , where $\Delta\Delta$ Ct $=$ $\Delta$ Ct ${}_{\text{unknown}}-\Delta$ Ct ${}_{\text{GES1}}$ .

2.4 Statistical analysis

All statistical analyses were performed using SPSS 18.0 (IBM, Chicago, IL, USA) and GraphPad Prism 5.0 (GraphPad Software, La Jolla, CA). All data were presented as mean $\pm$ standard deviation (SD) from three independent experiments. Student’s t test, $\chi$ 2 test, or Wilcoxon’s test were used to analyze the significance of the differences between groups. The Kaplan-Meier method was used to estimate the survival probability in subgroups determined by expression level of VPS9D1-AS1. Two-sided P values were calculated and less than 0.05 was considered significant.

3. Results

3.1 VPS9D1-AS1 is downregulated in GC cancer tissues and cell lines

Expression level of VPS9D1-AS1 in 126 paired GC cancer and adjacent non-tumorous tissues was detected by qRT-PCR assays. As shown in Fig. 1A, expression of VPS9D1-AS1 was significantly downregulated in cancer tissues compared with that in non-tumorous tissues. Underexpression of VPS9D1-AS1 (fold change (Tumor/Normal) $<$ 0) was found in 74.60% of GC patients in our cohort (Fig. 1B). Moreover, we examined expression of VPS9D1-AS1 in a panel of GC cell lines including SGC7901, MKN45, BGC823, MGC803, AGS and one normal gastric epithelial cell GES1. Our data revealed that VPS9D1-AS1 was downregulated in cancer cells compared with that in GES1 cell (Fig. 1C).

Figure 1.

VPS9D1-AS1 is downregulated in gastric cancer tissues and cell lines. (A) Expression level of VPS9D1-AS1 in gastric tumor tissues are significantly lower than that in non-tumorous tissues. Results were presented as cycle threshold (Ct) of Ct (VPS9D1-AS1) relative to that of Ct (GAPDH). (B) Expression of VPS9D1-AS1 in 126 GC samples were shown as log2 (Ct (tumor)/Ct (normal)). (C) Expression of VPS9D1-AS1 in GC cancer cell lines including SGC7901, MKN45, BGC823, MGC803 and AGS and one gastric epithelial cell GES1 was examined. Values are from three biological replicates with three technical replicates and presented as mean $\pm$ SD. * $P<$ 0.05.

Table 1

The correlation between clinicopathological parameters and VPS9D1-AS1 expression

	VPS9D1-AS1 expression		$P$
	Low, $n$ (%)	High, $n$ (%)
Age
$<$ 60	51 (54.3)	20 (62.5)	0.536
$\geqslant$ 60	43 (45.7)	12 (37.5)
Gender
Male	59 (62.8)	22 (68.8)	0.670
Female	35 (37.2)	10 (31.2)
Alcohol consumption
Ever and current	18 (19.1)	4 (12.5)	0.590
Never	76 (80.9)	28 (87.5)
Smoking status
Ever and current	31 (33.0)	11 (34.4)	1.000
Never	63 (67.0)	21 (65.6)
Differentiation status
Well or moderate	29 (30.9)	13 (40.6)	0.386
Poor	65 (69.1)	19 (59.4)
Lymph node metastasis
Present	75 (79.8)	23 (71.9)	0.460
Absent	19 (20.2)	9 (28.1)

Table 2

Univariate and multivariate analyses of various potential prognostic factors in GC patients

	Univariate analysis		Multivariate analysis
	HR (95% CI)	$P$	HR (95% CI)	$P$
Age ( $<$ 60 vs $\geqslant$ 60)	0.83 (0.48–1.44)	0.505	–	–
Gender (Male vs Female)	1.16 (0.66–2.03)	0.606	–	–
Tumor size ( $<$ 5 cm vs $\geqslant$ 5 cm)	0.94 (0.54–1.62)	0.815	–	–
Differentiation (Moderate/Poor, Well)	1.61 (0.87–2.98)	0.127	–	–
Lymph node metastasis (Present vs Absent)	2.70 (1.15–6.33)	0.022	1.06 (0.41–2.80)	0.899
TNM Stage (III/IV vs I/II)	4.56 (2.05–10.1)	0.000 ${}^{*}$	4.27 (1.72–10.6)	0.002 ${}^{*}$
VPS9D1-AS1 (High vs Low)	0.28 (0.13–0.63)	0.000 ${}^{*}$	0.30 (0.13–0.66)	0.003 ${}^{*}$

HR: hazard ratio; CI: confidence interval; ${}^{*}$ $P<$ 0.05.

Figure 2.

VPS9D1-AS1 correlated with tumor size and TNM stage. (A) Relative expression level of VPS9D1-AS1 in GC cancer tissues with tumor size larger than 5 cm and smaller than 5 cm compared with tumor size smaller than 5 cm. $P=$ 0.003. (B) Relative expression level of VPS9D1-AS1 in GC cancer tissues in different TNM stages. $P=$ 0.026.

3.2 Downregulation of VPS9D1-AS1 correlated with clinicopathological parameters

To analyze correlations between VPS9D1-AS1 expression and the clinicopathological features of 126 GC patients, our patient cohort was divided into high expression arm (expression of VPS9D1-AS1 above the cutoff, $n=$ 32) and low expression arm (expression of VPS9D1-AS1 below the cutoff, $n=$ 94) (Fig. 1B) with the 75% quantitle expression level (0.05) as the cutoff value based on the expression pattern. The correlations between expression of VPS9D1-AS1 and the clinical pathological parameters of GC patients are summarized in Table 1. Lower VPS9D1-AS1 expression level was observed in tissues with tumor size larger than 5 cm than tissues with tumor size smaller than 5 cm (Fig. 2A, $P=$ 0.003). In addition, VPS9D1-AS1 expression was markedly downregulated in stage III/IV compared with that in stage I/II (Fig. 2B, $P=$ 0.026). However, correlations of VPS9D1-AS1expression with other parameters including age, gender, alcohol consumption, and smoking status were not observed (Table 1).

3.3 Decreased VPS9D1-AS1 expression is an unfavorable prognostic factor in GC patients

To further explore whether VPS9D1-AS1 expression predicted prognosis of GC patients, we used Kaplan-Meier methods with log-rank tests. As shown in Fig. 3A, the median overall survival of patients with low VPS9D1-AS1 expression was significantly shorter than that of patients with high VPS9D1-AS1 expression ( $P<$ 0.001). Moreover, expression of VPS9D1-AS1 correlated with disease free survival of GC patients ( $P=$ 0.003, Fig. 3B). Univariate and multivariate survival analyses showed that TNM stage ( $P=$ 0.002) and VPS9D1-AS1 expression ( $P=$ 0.003) were independent prognosis indicators of GC patients (Table 2).

4. Discussion

Figure 3.

VPS9D1-AS1 predicts prognosis of GC patients. Kaplan-Meier overall survival (A) and disease-free survival (B) curves according to VPS9D1-AS1 expression level. Patients with low VPS9D1-AS1 expression ( $n=$ 94, blue column) showed reduced survival times compared with patients with high VPS9D1-AS1 expression ( $n=$ 32, red column) ( $P<$ 0.001 and 0.003, log-rank test).

It has been recognized that in addition to protein-coding genes, non-coding genes exist primarily in human genomes [6, 9], of which lncRNA was recently identified. Generally, lncRNAs play oncogenic or suppressive roles in cancer biology, although the underlying mechanisms were diverse [9, 16, 17]. One of the well-characterized models is that lncRNAs act as endogenous competitive RNA for microRNA leading to release of downstream target genes. For instance, lncRNA PVT1 promotes gastric cancer progression via regulation of microRNA-186 [18]. LncRNAs such as HOTAIR or XIST may function as a scaffold to recruit proteins involved in chromatin remodeling or histone modification [12, 19, 20]. In addition, the lncRNA NBR2 directly binds to the metabolic master AMPK and regulates tumor cell metabolism under glucose deprivation [21]. Through interactions with other RNA, DNA or proteins, lncRNAs can regulate cell function and are proposed to be diagnostic markers or therapeutic targets of human cancer [22, 23, 24].

Over the past decades, the possible molecular alterations underlying gastric carcinogenesis have been thoroughly explored [25, 26]. However, particular pathogenesis of GC is still largely vague. Multitudinous reports have demonstrated dysregulated lncRNAs in GC. For example, the lncRNA GClnc1 was found to be overexpressed in GC and acted as a modular scaffold for WDR5 and KAT2A complexes [27]. Li et al. reported that MALAT1 promoted malignant transformation of gastric cancer by regulating vasculogenic mimicry and angiogenesis [28]. Yang et al. found higher VPS9D1-AS1 expression in the colorectal cancer tissues and CRC cell lines than in normal tissues and cell lines. Upregulated VPS9D1-AS1 indicated shortened OS time [15]. In this study, we found downregulated expression of VPS9D1-AS1 in GC tissues and cancer cell lines compared with that in adjacent non-tumorous tissues and gastric epithelial cells. Moreover, downregulation of VPS9D1-AS1 predicted poor overall and disease free survival of GC patients. These results showed that VPS9D1-AS1 may play different functions in colorectal cancer and GC. However, exact mechanisms underlying VPS9D1-AS1 in GC progression warrant further investigation.

In conclusion, we found for the first time that VPS9D1-AS1 was underexpressed in GC. Our data suggest that VPS9D1-AS1 may be used as a novel prognosis predictor of GC.

Footnotes

Acknowledgments

This work was supported by Guizhou Provincial People’s Hospital Youth Funding (No. GZSQN [2016] 20).

Conflict of interest

The authors declare that they have no conflict of interest.

References

Chen

Zheng

Baade

P.D.

Zhang

Zeng

Bray

Jemal

X.Q.

and He

, Cancer statistics in China, 2015, CA Cancer J Clin 66 (2016), 115–132.

Siegel

Naishadham

and Jemal

, Cancer statistics, 2013, CA Cancer J Clin 63 (2013), 11–30.

Lasithiotakis

Antoniou

S.A.

Antoniou

G.A.

Kaklamanos

and Zoras

, Gastrectomy for stage IV gastric a systematic review and meta-analysis, Anticancer Res 34 (2014), 2079–2085.

Sugano

, Screening of gastric cancer in Asia, Best Pract Res Clin Gastroenterol 29 (2015), 895–905.

Consortium

E.P.

Birney

Stamatoyannopoulos

J.A.

Dutta

Guigo

Gingeras

T.R.

Margulies

E.H.

Weng

Snyder

Dermitzakis

E.T.

Thurman

R.E.

Kuehn

M.S.

Taylor

C.M.

Neph

Koch

C.M.

Asthana

Malhotra

Adzhubei

Greenbaum

J.A.

Andrews

R.M.

Flicek

Boyle

P.J.

Cao

Carter

N.P.

Clelland

G.K.

Davis

Day

Dhami

Dillon

S.C.

Dorschner

M.O.

Fiegler

Giresi

P.G.

Goldy

Hawrylycz

Haydock

Humbert

James

K.D.

Johnson

B.E.

Johnson

E.M.

Frum

T.T.

Rosenzweig

E.R.

Karnani

Lee

Lefebvre

G.C.

Navas

P.A.

Neri

Parker

S.C.

Sabo

P.J.

Sandstrom

Shafer

Vetrie

Weaver

Wilcox

Collins

F.S.

Dekker

Lieb

J.D.

Tullius

T.D.

Crawford

G.E.

Sunyaev

Noble

W.S.

Dunham

Denoeud

Reymond

Kapranov

Rozowsky

Zheng

Castelo

Frankish

Harrow

Ghosh

Sandelin

Hofacker

I.L.

Baertsch

Keefe

Dike

Cheng

Hirsch

H.A.

Sekinger

E.A.

Lagarde

Abril

J.F.

Shahab

Flamm

Fried

Hackermuller

Hertel

Lindemeyer

Missal

Tanzer

Washietl

Korbel

Emanuelsson

Pedersen

J.S.

Holroyd

Taylor

Swarbreck

Matthews

Dickson

M.C.

Thomas

D.J.

Weirauch

M.T.

Gilbert

Drenkow

Bell

Zhao

Srinivasan

K.G.

Sung

W.K.

Ooi

H.S.

Chiu

K.P.

Foissac

Alioto

Brent

Pachter

Tress

M.L.

Valencia

Choo

S.W.

Choo

C.Y.

Ucla

Manzano

Wyss

Cheung

Clark

T.G.

Brown

J.B.

Ganesh

Patel

Tammana

Chrast

Henrichsen

C.N.

Kai

Kawai

Nagalakshmi

Lian

Newburger

Zhang

Bickel

Mattick

J.S.

Carninci

Hayashizaki

Weissman

Hubbard

Myers

R.M.

Rogers

Stadler

P.F.

Lowe

T.M.

Wei

C.L.

Ruan

Struhl

Gerstein

Antonarakis

S.E.

Green

E.D.

Karaoz

Siepel

Taylor

Liefer

L.A.

Wetterstrand

K.A.

Good

P.J.

Feingold

E.A.

Guyer

M.S.

Cooper

G.M.

Asimenos

Dewey

C.N.

Hou

Nikolaev

Montoya-Burgos

J.I.

Loytynoja

Whelan

Pardi

Massingham

Huang

Zhang

N.R.

Holmes

Mullikin

J.C.

Ureta-Vidal

Paten

Seringhaus

Church

Rosenbloom

Kent

W.J.

Stone

E.A.

Program

N.C.S.

, C. Baylor College of Medicine Human Genome Sequencing, C. Washington University Genome Sequencing Broad

, I. Children’s Hospital Oakland Research Batzoglou

Goldman

Hardison

R.C.

Haussler

Miller

Sidow

Trinklein

N.D.

Zhang

Z.D.

Barrera

Stuart

King

D.C.

Ameur

Enroth

Bieda

M.C.

Kim

Bhinge

A.A.

Jiang

Liu

Yao

Vega

V.B.

Lee

C.W.

Shahab

Yang

Moqtaderi

Zhu

Squazzo

Oberley

M.J.

Inman

Singer

M.A.

Richmond

T.A.

Munn

K.J.

Rada-Iglesias

Wallerman

Komorowski

Fowler

J.C.

Couttet

Bruce

A.W.

Dovey

O.M.

Ellis

P.D.

Langford

C.F.

Nix

D.A.

Euskirchen

Hartman

Urban

A.E.

Kraus

Van Calcar

Heintzman

Kim

T.H.

Wang

Hon

Luna

Glass

C.K.

Rosenfeld

M.G.

Aldred

S.F.

Cooper

S.J.

Halees

Lin

J.M.

Shulha

H.P.

Zhang

Haidar

J.N.

Ruan

Iyer

V.R.

Green

R.D.

Wadelius

Farnham

P.J.

Ren

Harte

R.A.

Hinrichs

A.S.

Trumbower

Clawson

Hillman-Jackson

Zweig

A.S.

Smith

Thakkapallayil

Barber

Kuhn

R.M.

Karolchik

Armengol

Bird

C.P.

de Bakker

P.I.

Kern

A.D.

Lopez-Bigas

Martin

J.D.

Stranger

B.E.

Woodroffe

Davydov

Dimas

Eyras

Hallgrimsdottir

I.B.

Huppert

Zody

M.C.

Abecasis

G.R.

Estivill

Bouffard

G.G.

Guan

Hansen

N.F.

Idol

J.R.

Maduro

V.V.

Maskeri

McDowell

J.C.

Park

Thomas

P.J.

Young

A.C.

Blakesley

R.W.

Muzny

D.M.

Sodergren

Wheeler

D.A.

Worley

K.C.

Jiang

Weinstock

G.M.

Gibbs

R.A.

Graves

Fulton

Mardis

E.R.

Wilson

R.K.

Clamp

Cuff

Gnerre

Jaffe

D.B.

Chang

J.L.

Lindblad-Toh

Lander

E.S.

Koriabine

Nefedov

Osoegawa

Yoshinaga

Zhu

and de Jong

P.J.

, Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project, Nature 447 (2007), 799–816.

Sun

and Kraus

W.L.

, From discovery to function: the expanding roles of long noncoding RNAs in physiology and disease, Endocr Rev 36 (2015), 25–64.

Yan

Feng

Yuan

Zhao

S.D.

Zhang

Yang

Shan

Fan

Kandalaft

L.E.

Tanyi

J.L.

Yuan

C.X.

Zhang

Yuan

Hua

Katsaros

Huang

Montone

Fan

Coukos

Boyd

Sood

A.K.

Rebbeck

Mills

G.B.

Dang

C.V.

and Zhang

, Comprehensive Genomic Characterization of Long Non-coding RNAs across Human Cancers, Cancer Cell 28 (2015), 529–540.

Hayes

Peruzzi

P.P.

and Lawler

, MicroRNAs in cancer: biomarkers, functions and therapy, Trends Mol Med 20 (2014), 460–469.

Ulitsky

and Bartel

D.P.

, lincRNAs: genomics, evolution, and mechanisms, Cell 154 (2013), 26–46.

10.

McHugh

C.A.

Chen

C.K.

Chow

Surka

C.F.

Tran

McDonel

Pandya-Jones

Blanco

Burghard

Moradian

Sweredoski

M.J.

Shishkin

A.A.

Lander

E.S.

Hess

Plath

and Guttman

, The Xist lncRNA interacts directly with SHARP to silence transcription through HDAC3, Nature 521 (2015), 232–236.

11.

Zhou

Luo

Gao

Deng

and Jiang

, Long non-coding RNA XIST promotes cell growth and invasion through regulating miR-497/MACC1 axis in gastric cancer, Oncotarget 8 (2017), 4125–4135.

12.

Tsai

M.C.

Manor

Wan

Mosammaparast

Wang

J.K.

Lan

Shi

Segal

and Chang

H.Y.

, Long noncoding RNA as modular scaffold of histone modification complexes, Science 329 (2010), 689–693.

13.

Xia

Yao

Zhang

Wang

Mei

Guo

and Huang

, Long noncoding RNA HOTAIR promotes metastasis of renal cell carcinoma by up-regulating histone H3K27 demethylase JMJD3, Oncotarget (2017).

14.

Zhan

Tan

and Wang

, The expression and significance of HOX transcript antisense RNA and epithelial-mesenchymal transition-related factors in esophageal squamous cell carcinoma, Mol Med Rep (2017).

15.

Yang

Wang

and An

, Dysregulation of long non-coding RNA profiles in human colorectal cancer and its association with overall survival, Oncol Lett 12 (2016), 4068–4074.

16.

Ning

Wei

Chen

and Yang

, LncRNA, NEAT1 is a prognosis biomarker and regulates cancer progression via epithelial-mesenchymal transition in clear cell renal cell carcinoma, Cancer Biomark (2017).

17.

Lim

Z.F.

Zhou

Wang

Yang

and Zhang

, NORAD Expression Is Associated with Adverse Prognosis in Esophageal Squamous Cell Carcinoma, Oncol Res Treat 40 (2017), 370–374.

18.

Huang

Liu

H.W.

Chen

J.Q.

Wang

S.H.

Hao

L.Q.

Liu

and Wang

, The long noncoding RNA PVT1 functions as a competing endogenous RNA by sponging miR-186 in gastric cancer, Biomed Pharmacother 88 (2017), 302–308.

19.

Sarma

Cifuentes-Rojas

Ergun

Del Rosario

Jeon

White

Sadreyev

and Lee

J.T.

, ATRX directs binding of PRC2 to Xist RNA and Polycomb targets, Cell 159 (2014), 869–883.

20.

Dinglin

Wang

Luo

Shen

Zhou

Zhu

Tan

Yang

and Zhang

, Long noncoding RNA XIST promotes malignancies of esophageal squamous cell carcinoma via regulation of miR-101/EZH2, Oncotarget (2017).

21.

Liu

Xiao

Z.D.

Han

Zhang

Lee

S.W.

Wang

Lee

Zhuang

Chen

Lin

H.K.

Wang

Liang

and Gan

, LncRNA NBR2 engages a metabolic checkpoint by regulating AMPK under energy stress, Nat Cell Biol 18 (2016), 431–442.

22.

Liu

Luo

Xiao

Tao

Wang

Huang

Wang

Zeng

and Jiang

, LncRNA SPRY4-IT1 sponges miR-101-3p to promote proliferation and metastasis of bladder cancer cells through up-regulating EZH2, Cancer Lett 388 (2017), 281–291.

23.

Zheng

Huang

Tan

Chang

Wei

Han

Wang

Che

Zhou

Miao

Jiang

Yang

Cao

Zuo

Wang

Cheung

S.T.

Jia

Zheng

Shen

and Lin

, Pancreatic cancer risk variant in LINC00673 creates a miR-1231 binding site and interferes with PTPN11 degradation, Nat Genet 48 (2016), 747–757.

24.

Shen

Luo

Deng

Zhou

Chen

Lim

Wang

and Yang

, Long non-coding RNA ATB promotes malignancy of esophageal squamous cell carcinoma by regulating miR-200b/Kindlin-2 axis, Cell Death Dis 8 (2017), e2888.

25.

Tsao

S.W.

Tsang

C.M.

K.F.

and Lo

K.W.

, The role of Epstein-Barr virus in epithelial malignancies, J Pathol 235 (2015), 323–333.

26.

N. Cancer Genome Atlas Research, Comprehensive molecular characterization of gastric adenocarcinoma, Nature 513 (2014), 202–209.

27.

Sun

T.T.

Liang

Ren

L.L.

Yan

T.T.

T.C.

Tang

J.Y.

Bao

Y.J.

Lin

Sun

Chen

Y.X.

Hong

Chen

Zou

and Fang

J.Y.

, LncRNA GClnc1 Promotes Gastric Carcinogenesis and May Act as a Modular Scaffold of WDR5 and KAT2A Complexes to Specify the Histone Modification Pattern, Cancer Discov 6 (2016), 784–801.

28.

Yuan

Sun

Lin

Huang

Bin

Liao

and Liao

, Long non-coding RNA MALAT1 promotes gastric cancer tumorigenicity and metastasis by regulating vasculogenic mimicry and angiogenesis, Cancer Lett (2017).