Research ArticleA Comprehensive Exploration of the lncRNA CCAT2: APan-Cancer Analysis Based on 33 Cancer Types and 13285 Cases

Bowen Huang ,1,2 Min Yu,2 Renguo Guan,1,2 Dong Liu,3 and Baohua Hou 1,2

1The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong Province, 510515, China2Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou,Guangdong Province, 510080, China3Jinan University, Guangzhou, Guangdong Province, 510632, China

Correspondence should be addressed to Baohua Hou; hbh1000@126.com

Received 11 June 2019; Accepted 31 December 2019; Published 24 February 2020

Academic Editor: Shih-Ping Hsu

Copyright © 2020 Bowen Huang et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Whether the lncRNA CCAT2 expression level affects the clinical progression and outcome of cancer patients has not yet been fullyelucidated. There is still an inconsistent view regarding the correlation between CCAT2 expression and clinicopathological factors,including survival data. Besides, the regulation mechanism of CCAT2 in human cancer is still unclear. Our study analyzed a largenumber of publication data and TCGA databases to identify the association of CCAT2 expression with clinicopathological factorsand to explore the regulatory mechanisms in human cancers. We designed a comprehensive study to determine the expression ofCCAT2 in human cancer by designing a meta-analysis of 20 selected studies and the TCGA database, using StataSE 12.0 to explorethe relationship between CCAT2 expression and both the prognosis and clinicopathological features of 33 cancer types and 13285tumor patients. Moreover, we performed GO and KEGG pathway enrichment analyses on potential target genes of CCAT2collected from GEPIA and LncRNA2Target V2.0. The level of CCAT2 expression in tumor tissues is higher than that in pairednormal tissues and is significantly associated with a poor prognosis in cancer patients. Besides, overexpression of CCAT2 wassignificantly associated with tumor size, clinical stage, and TNM classification. Meanwhile, CCAT2 expression is the highest instage II of human cancer, followed by stage III. Finally, 111 validated target gene symbols were identified, and GO and KEGGdemonstrated that the CCAT2 validation target was significantly enriched in several pathways, including microRNAs in thecancer pathway. In summary, CCAT2 can be a potential biomarker associated with the progression and prognosis of human cancer.

1. Introduction

The rapid advancement of exploring ncRNA is the result ofRNA-Seq technology, which provides a deeper understand-ing of the human transcriptome. Further research on theseRNAs will lead to new perspectives on cancer cell regulationmechanisms and innovative therapeutic targets [1]. Accord-ing to the ncRNA length, we divided it into two categories.Short RNA has a transcript of fewer than 200 nucleotides,including miRNA, siRNA, and piRNA. Besides, transcriptslonger than 200 nucleotides are classified as lncRNA [2].NcRNA does not encode a protein, which was previouslythought to be transcriptional noise or evolutionary junk[3]. However, ncRNA plays a vital role in a variety of biolog-ical processes [4]. LncRNA acts as a regulator of gene

expression to regulate the development and progression ofmany diseases, especially malignant tumors [5, 6]. Therefore,lncRNA is used as a biomarker to monitor tumor prognosis.For example, He et al. explored the association betweenlncRNA PVT1 and patient prognosis in the TCGA databaseand sought after some possible pathways of PVT1 [7].

CCAT2 is located in the 8q24 gene desert. The locus wasfirst named in colon tumor tissue in the 2000s [8]. TheCCAT2 genomic locus, including SNP rs6983267, isassociated with an increased risk of various malignancies[9]. Overexpression of CCAT2 promotes the proliferationand invasion of malignant tumors, claiming that CCAT2plays a carcinogenic role [10]. Studies also observed thatCCAT2 expression levels in tumor samples were higher thanthose in adjacent tissues and were associated with poor

HindawiDisease MarkersVolume 2020, Article ID 5354702, 13 pageshttps://doi.org/10.1155/2020/5354702

prognosis [11]. Moreover, different expression levels ofCCAT2 will affect the therapeutic effects of different treat-ment procedures, such as chemotherapy [12].

The above evidence indicates that CCAT2 is involvedin tumor progression. Moreover, some previous meta-analyses have reported that increased CCAT2 expressionis significantly associated with lymph node metastasis,distant metastasis, and higher clinical stage [13, 14].However, sample quantity is limited, and the relevance ofCCAT2 to other clinicopathological parameters has notbeen adequately studied in these studies. Therefore, wereviewed the entire literature and searched the TCGAdatabase for current research to explore the clinicalpathology and prognostic value of CCAT2 in various typesof cancer patients. We also listed potential target genes forCCAT2 by GO and KEGG analysis, and this paperdiscusses the possible mechanism of action of CCAT2 intumor progression.

2. Material and Methods

2.1. Literature Review and Selection.We searched the Englishand Chinese medical literature in PubMed, Web of Science,Wiley Online Library, Weipu, Wangfang Data, and CNKIto identify all publications related to CCAT2 in cancerpatients. The search strategy combining the terms “CCAT2”or “colon cancer associated transcript 2” is based on thepurpose of the study. We also reviewed comments and ref-erences related to CCAT2 by other methods, including theextraction of previous articles cited in the meta-analysis ofCCAT2 [13–15]. The deadline for our project was onDecember 29, 2019.

The meta-analysis study was evaluated by two indepen-dent investigators (RG Guan and D Liu) using the same mul-tistep approach. First, check the headlines and abstracts toexclude unqualified studies that are not relevant, duplicatepapers, reviews, or case reports. What is more, the full textof the remaining studies was further examined separatelyby the same investigator (M Yu). Finally, the third commen-tator (BW Huang) resolved any disputes.

We consider studies that meet the following inclusioncriteria to be eligible: (i) collecting clinical samples fromtumor tissue, (ii) studying the association between CCAT2and survival data and performing CCAT2 levels by qRT-PCR quantification, and (iii) providing sufficient data todetermine the HR value and its 95% CI. The exclusion cri-teria were as follows: (i) repeated studies, (ii) study data notsufficient to calculate HR values, (iii) studies of animals orcell lines, and (iv) reviews, comments, letters, case reports,and conference articles. If the survival analysis is not suffi-cient to calculate the HR value, we try to contact the authorto obtain the raw survival data.

2.2. Data Extraction. The following relevant informationfrom all eligible studies was extracted: first author name,publication year, country, cancer type, sample size, assaymethod, the criterion for dividing CCAT2 into high andlow groups, follow-up time, prognostic data, age classifica-tion, gender ratio, tumor size, clinical stage, TNM classifi-

cation, and histological differentiation. If the paper doesnot provide complete survival data, we follow the methodsof He et al. [7]. The HR and 95% CI were extracted indi-rectly from the Kaplan-Meier survival curve using EngaugeDigitizer version 11.2 (https://github.com/markummitchell/engauge-digitizer/releases).

2.3. Quality Assessment. The NOS was used to assess themethodological quality of two investigators (RG Guan andD Liu) independently evaluating eligible projects. They ratedeach study according to the following system: (i) selection, 0-4; (ii) comparability, 0-2; and (iii) exposure, 0-3. The highestscore is 9 points, and score ≥ 6 indicates that the researchquality is good.

2.4. Statistical Analysis. We used StataSE 12.0 software toanalyze the information extracted from eligible studies. HRand 95% CI assessed survival data. The OR and 95% CI werecalculated to analyze the relationship between human cancerand clinicopathological parameters, including age, gender,tumor size, clinical stage, TNM classification, and histologi-cal differentiation. What is more, subgroup analyses werebased on the source of tumor type and overall survival data.The Cochrane Q test and the I2 index were used to assesspotential heterogeneity in selected studies, with P′ < 0:05 orI2 > 50% considered statistically significant. If the selectedparameter has significant heterogeneity (P′ < 0:05), therandom effects model is used to calculate the HR value;otherwise, a fixed effects model will be employed. Finally,Begg’s test was used to estimate publication bias (bilateralP′ < 0:05 was considered statistically significant).

2.5. Analysis of CCAT2 Expression Levels in All CancersBased on TCGA Data. CCAT2 expression levels and over-all survival data in the TCGA database were extractedfrom starBase (http://starbase.sysu.edu.cn/). Experimentaldata were divided into high and low groups based on themedian level of CCAT2 expression. The Cox proportionalhazard model of SPSS 22.0 was used to assess the effect ofCCAT2 overexpression on survival. The box diagram andbar graph of CCAT2 expression in tumor samples and adja-cent normal tissues were drawn by R 3.6.0 (https://www.r-project.org/) and established on the data extraction of theTCGA database (https://portal.gdc.cancer.gov/).

2.6. Pathway Analysis of GO and KEGG for CCAT2Verification of Target Genes. We used GEPIA (http://gepia.cancer-pku.cn/) and LncRNA2Target V2.0 (http://123.59.132.21/lncrna2target/index.jsp) based on all publishedlncRNA papers to identify potential CCAT2 target genes inhuman cancer. GO enrichment analysis and KEGG pathwayanalysis were performed using DAVID BioinformaticsResources 6.8 (https://david.ncifcrf.gov/). We used R 3.6.0to visualize the results of GO and KEGG and used Cytoscape3.7.1 (https://cytoscape.org/) software to display a network ofCCAT2 and its related genes.

2 Disease Markers

3. Results

3.1. Summary of Literature Selection and Study Characteristics.We researched to analyze the connection between CCAT2expression and prognosis in cancer patients in all publishedliterature (Figure 1). A total of 1304 potential studies wereidentified after the first search, 177 of which were consideredeligible after the title and abstract screening. Next, we exam-ined the full text of the remaining articles. Finally, 20 studies(n = 2192) were included in our analysis, and the main char-acteristics are shown in Table 1. The follow-up period wasbetween 40 and 100 months. All selected studies investigatedthe relationship between CCAT2 and survival analysis,including OS, PFS, RFS, or DFS; 15 studies explored the asso-ciation between CCAT2 and age, 11 for gender, 15 for tumorsize, 14 for clinical stage, 6 for T classification, 12 for N clas-sification, 7 for M classification, and 11 for histological differ-entiation (shown in Table 2).

3.2. Correlation between lncRNA CCAT2 andClinicopathological Characteristics of the Study Patients.As shown in Table 2, we discovered that a high CCAT2level was remarkably related to tumor size (OR = 1:50,95% CI: 1.03-2.20, P = 0:036, I2 = 70:4%, and P′ < 0:001)(Figure 2(c)), clinical stage (OR = 3:09, 95% CI: 2.49-3.83,P < 0:001, I2 = 19:8%, and P′ = 0:238) (Figure 2(d)), T stages(OR = 2:37, 95% CI: 1.68-3.37, P < 0:001, I2 = 10:8%, andP′ = 0:347) (Figure 2(e)), N stage (OR = 3:33, 95% CI: 2.29-4.84, P < 0:001, I2 = 55:8%, and P′ = 0:009) (Figure 2(f)),and M stage (OR = 6:85, 95% Cl: 4.23-11.11, P < 0:001, I2 =47:1%, and P′ = 0:078) (Figure 2(g)). However, no significantconnection was found for age (OR = 1:04, 95% CI: 0.85-1.27,P = 0:714, I2 = 0:0%, and P′ = 0:741) (Figure 2(a)), gender(OR = 1:06, 95% CI: 0.84-1.35, P = 0:621, I2 = 0:0%, andP′ = 0:996) (Figure 2(b)), and histological differentiation(OR = 1:17, 95% Cl: 0.75-1.81, P = 0:484, I2 = 69:1%, andP′ < 0:001) (Figure 2(h)). The above results indicate thattumors with high CCAT2 levels appear to exhibit invasivebiological behavior.

3.3. Correlation between lncRNA CCAT2 Expression andSurvival Data. We analyzed the association of CCAT2expression with OS based on the results of 20 selected studies(n = 2192) and the TCGA database (n = 11093), suggestingthat CCAT2 overexpression is significantly associated withpoor prognosis for certain cancer types. Considering the sig-nificant heterogeneity in the study, we performed two sub-group analyses based on survival data and the source of thecancer type. In a subgroup analysis of OS, we found that highexpression of CCAT2 was significantly associated with poorOS in all databases (HR = 1:15, 95% CI: 1.04-1.26, P <0:001), including publications (HR = 1:75, 95% CI: 1.50-2.01, P < 0:001) and TCGA (HR = 1:01, 95% CI: 0.90-1.12,P < 0:001) (Figure 3(a)). As pituitary adenoma, colorectalcancer, renal cell carcinoma, oral cancer, small cell lung can-cer, ovarian cancer, prostate cancer, and esophageal cancerwere studied separately, we have classified them as others.Similar results were generated in a subgroup analysis based

on tumor type (hepatocellular carcinoma (HR = 2:44, 95%CI: 1.56-3.33, P < 0:001), osteosarcoma (HR = 1:17, 95% CI:0.70-1.65, P < 0:001), cholangiocarcinoma (HR = 2:96, 95%CI: 1.95-3.97, P < 0:001), gastric cancer (HR = 1:47, 95% CI:1.04-1.89, P < 0:001), breast cancer (HR = 1:98, 95% CI:1.36-2.60, P < 0:001), and others (HR = 1:67, 95% CI: 1.40-1.94, P < 0:001)) (Figure 3(b)). No significant heterogeneitywas found in these studies.

3.4. Publishing Bias. Begg’s funnel plot was used to assesspublication bias in our study. No publication bias wasobserved in studies evaluating the association of CCAT2 withclinicopathological features and OS in the study group(P = 0:496) and TCGA (P = 0:455) (Figures 4(a) and 4(b)).Similarly, we conducted a publication bias analysis on theinfluencing factors of OS in patients (Figures 4(c)–4(j)).Among them, suspicious publication bias was found in thetumor size subgroup (Pr = 0:038). Therefore, we used thetrim method for further verification. The results indicatedthat the tumor size subgroup needed to increase three exper-iments to eliminate the bias, but the 95% CI after clippingand supplementation showed no statistical significance,reminding us that the previous results were stable.

3.5. The Expression Level of CCAT2 in Pan-Cancer. Based onthe results obtained from TCGA, we plotted a box diagram ofthe CCAT2 expression profile for tumor samples and adja-cent normal tissues (Figure 5(a)). We found that CCAT2 ishighly expressed in 6 of 33 tumor tissues (COAD/KIRC/-STAD/PRAD/ESCA/READ) (Figure 5(b)) and is weaklyexpressed in 4 tumor tissues (BRCA/LUSC/THCA/PAAD)(Figure 5(c)). And CCAT2 is mainly expressed in stageII of tumor pathology, followed by stage III (from GEPIA,Figure 5(d)).

3.6. Functional Analysis of CCAT2-Related Genes in HumanTumors. To explore the underlying mechanism of action ofCCAT2, we identified a total of 111 target genes using GEPIAand LncRNA2Target V2.0. GO and KEGG analysis was per-formed. CCAT2 and target gene symbols were analyzed byGO enrichment analysis, including BP, CC, and MF, andthe results are shown in Figure 6. Furthermore, KEGGenrichment analysis revealed that CCAT2 might play a rolein cancers such as microRNAs in cancer pathway, Hipposignaling pathway, RNA degradation pathway, ribosomebiogenesis in eukaryote pathway, and cell cycle pathway(Figures 6 and 7).

4. Discussion

Numerous studies have shown that overexpression ofCCAT2 is significantly associated with clinical outcomesand other clinicopathological parameters in cancer patients[16–18]. The review article also summarizes the critical rolethat CCAT2may play in the development of multiple cancers[19]. A meta-analysis also showed that the upregulation ofCCAT2 was associated with lymph node metastasis, distantmetastasis, and poor OS in patients with malignancy,although the association between CCAT2 and other clinico-pathological parameters was not discussed in previous

3Disease Markers

studies [15]. To obtain more convincing conclusions andexplore the potential mechanism of action of CCAT2 intumors, we performed current studies by combining theresults of published studies with TCGA survival datafollowed by GO and KEGG analysis.

A meta-analysis of 2192 patients from 20 eligible studiesand 11093 patients from TCGA currently explores the asso-ciation between CCAT2 overexpression and prognosis, aswell as the clinicopathological parameters of cancer patients.Therefore, our research is by far the most comprehensiveanalysis. We assessed the quality of all selected studiesthrough NOS and used Begg’s method to examine publica-tion bias. Our results show that high expression of CCAT2is associated with poor OS. For clinicopathological featuresof cancer patients, our study suggests that high CCAT2 is sig-nificantly associated with cancer growth and metastasis,including tumor size, clinical stage, and TNM classification,although age, gender, and histological differentiation arenot significant factors. The results suggest that CCAT2 maybe a potential tumor biomarker and is associated with tumorinvasiveness, which is why CCAT2 is mainly expressed instage II, followed by stage III.

Furthermore, a subgroup analysis of CCAT2 expressionand overall survival was not statistically significant in TCGA,and CCAT2 is likely overexpressed in certain types oftumors. Besides, subgroup analysis was also performed onspecific cancers, including hepatocellular carcinoma, osteo-sarcoma, cholangiocarcinoma, gastric carcinoma, and breastcancer. Increased CCAT2 expression was associated withworse HR observed in hepatocellular carcinoma, cholangio-carcinoma, gastric cancer, and breast cancer, whereas no sig-nificant association between CCAT2 expression and HR was

detected in osteosarcoma. However, KIRC, PRAD, READ,SKCM, and STAD in the TCGA data set are associated witha good prognosis. We reviewed related studies and found thatoverexpression of CCAT2 levels is associated with worse out-comes in renal cell carcinoma [20], prostate cancer [21], gas-tric cancer [17, 22, 23], and colorectal cancer [24], and thereis no corresponding melanoma report. Sampling errors andpublication bias may cause the inconsistent conclusions ofliterature studies and TCGA in these tumors. Based on theevidence from our study, all of these results suggest thatCCAT2 may serve as a reliable independent diagnostic andprognostic biomarker, and even all types of cancers with highCCAT2 expression may have a poor prognosis and moreadverse clinical pathology parameters. Although these find-ings suggest that CCAT2 may play a role in cancer, the exactmechanism remains to be elucidated. The associationbetween CCAT2 and the prognosis of different types oftumors needs to be confirmed with more research.

Studies have shown that CCAT2 expression levels areupregulated in cancerous tissues compared to paired adjacenttissues; the same results were found in in vitro cell line sam-ples [25]. Research on the mechanism of action of CCAT2 incancer has proliferated in recent years, and there is increasingevidence that CCAT2 can affect the different biologicalbehaviors of different types of tumors. Yu et al. observed thatCCAT2 could positively regulate the expression of thePOU5F1B gene by inhibiting the PI3K/mTOR signalingpathway. The silencing of the CCAT2 gene inhibits the pro-liferation of BGC-823 cells and induces apoptosis andautophagy in BGC-823 cells [26]. Cai et al. revealed that thesilencing of CCAT2 inhibited the proliferation and invasionof PANC-1 cells in vitro and reduced the tumorigenesis of

Records identified throughdatabase searching (n = 1304)

Studied a�er duplicatesremoved (n = 1108)

Records excluded(n = 931)

Full-text articlesreviewed for eligibility

(n = 177)

Full-text articles excluded(n = 157)

Studies includedfor meta-analysis (n = 20)

Figure 1: Flow diagram of the search and selection of study patients.

4 Disease Markers

Table1:Maincharacteristicsof

theselected

stud

ies.

Firstauthor

Year

Cou

ntry

Cancertype

N(M

/F)

Highexpression

Follow-up(m

onths)

Outcome

HR(95%

CI)

Qualityscore

Fu[35]

2019

China

HCC

122(79/43)

>Medianexpression

60∗

OS

2.126(1.273-8.775)

8

Ruan[36]

2018

China

OS

50(32/18)

>Medianexpression

70∗

OS

1.32

(0.88-1.97)

8

Yan

[10]

2018

China

OS

40NR

60∗

OS/PFS

0.69

(0.22-2.21)/1.42

(0.55-3.62)

7

Xu[37]

2018

China

CCA

60(27/33)

Medianvalue:2.95-fold

60∗

OS

2.391(1.024-5.583)

8

Fu[38]

2018

China

PA

74>M

edianexpression

40∗

OS

1.56

(1.06-2.29)

7

Bai[39]

2018

China

CCA

106(78/28)

Above

thecut-off

value

72∗

OS/PFS

3.10

(2.17-4.43)/3.31

(2.32-4.72)

8

Chen[40]

2017

China

HCC

60(48/12)

>Medianexpression

40∗

OS

2.46

(1.71-3.53)

8

Ozawa[24]

2017

USA

/Japan

CRC

300

NR

60∗

OS/RFS

2.40

(1.22-4.59)/2.39

(1.10-5.08)

7

Wu[22]

2017

China

GC

208(124/84)

>twofoldchange

85OS/DFS

1.214(0.898-1.882)/1.687(0.833-1.896)

8

Huang

[20]

2017

China

RCC

61(34/27)

>Medianexpression

60∗

OS

3.02

(1.14-7.96)

8

Ma[41]

2017

China

OC

62(40/22)

>eightfold

60∗

OS

1.60

(1.03-2.47)

8

Deng[42]

2017

China

BC

120

>Medianexpression

100∗

OS

1.89

(1.36-2.63)

8

Chen[18]

2016

China

SCLC

112(67:45)

>Medianexpression

60∗

OS

1.66

(1.22-2.27)

7

Wang[23]

2016

China

GC

108(64:44)

>Medianexpression

70∗

OS/DFS

2.108(1.442-3.202)/2.305(1.554-3.418)

8

Huang

[43]

2016

China

ORC

109

>Medianexpression

60∗

OS/DFS

2.938(1.526-5.873)/1.74

(1.27-2.39)

8

Zheng

[21]

2016

China

PC

96>M

edianexpression

60∗

OS/PFS

2.292(1.370-3.528)/2.276(1.199-2.768)

7

Zhang

[16]

2015

China

EC

229(170/59)

>Medianexpression

80∗

OS

1.432(1.005-2.041)

8

Wang[17]

2015

China

GC

85(41/44)

>Meanexpression

60∗

OS/PFS

2.405(1.194-5.417)/2.315(1.097-5.283)

8

Cai[44]

2015

China

BC

67>eightfold

60∗

OS

3.57

(1.77-7.21)

7

Chen[45]

2015

China

CC

123

>Meanexpression

60∗

OS/PFS

2.813(1.504-6.172)/3.072(1.716-8.174)

8

N(M

/F):nu

mber(m

ale/female);H

R:h

azardratio;

CI:confi

denceinterval;H

CC:h

epatocellularcarcinom

a;OS:osteosarcoma;CCA:cho

langiocarcinom

a;PA:p

ituitary

adenom

as;C

RC:colorectalcarcinom

a;GC:gastriccarcinom

a;RCC:renalcellcarcinom

a;OC:oralcarcino

ma;BC:breastcancer;SCLC

:smallcelllun

gcancer;O

RC:ovarian

cancer;P

C:prostatecancer;E

C:esoph

agealcarcino

ma;CC:cervicalcancer;

OS:

overallsurvival;P

FS:p

rogression

-freesurvival;R

FS:relapse-freesurvival;D

FS:d

isease-freesurvival;N

R:n

otrepo

rted;∗

App

roximatetimes

extractedfrom

survival

curve.

5Disease Markers

Table2:Com

bination

sof

data

evaluating

theassociations

ofCCAT2withtheclinicop

atho

logicalcharacteristics

ofthestud

ypatients.

Clin

icop

atho

logicalp

aram

eters

Stud

ies(n)

Test/event

total

Con

trol/event

total

Mod

elOR(95%

CI)

Testfor

heterogeneity

Pvalue

I2P′

Age

(old/you

ng)

15(1649)

449

859

405

790

Fixed

1.04

(0.85-1.27)

0.0%

0.741

0.714

Gender(m

ale/female)

11(1201)

392

764

221

437

Fixed

1.06

(0.84-1.35)

0.0%

0.996

0.621

Tum

orsize

(large/small)

15(1656)

418

761

435

895

Rando

m1.50

(1.03-2.20)

70.4%

0.000

0.036

Clin

icalstage(advanced/early)

14(1584)

568

891

258

693

Fixed

3.09

(2.49-3.83)

19.8%

0.238

0.000

T(T3-T4/T1-T2)

6(579)

145

231

147

348

Fixed

2.37

(1.68-3.37)

10.8%

0.347

0.000

N(N

1-N3/N0)

12(1356)

399

602

306

754

Rando

m3.33

(2.29-4.84)

55.8%

0.009

0.000

M(M

1/M0)

7(690)

118

142

224

548

Fixed

6.85

(4.23-11.11)

47.1%

0.078

0.000

Differentiation(poorly/well)

11(1331)

324

616

349

715

Rando

m1.17

(0.75-1.81)

69.1%

0.000

0.484

6 Disease Markers

Overall (I2 = 0.0%, P = 0.741)

Y.Xu 2018 (60)

X.Zhang 2015 (229)

Y.Wang 2016 (108)S.Huang 2016 (109)

C.Wang 2015 (85)

J.Zheng 2016 (96)

R.Ruan 2018 (50)

J.Huang 2017 (61)

X.Chen 2015 (123)

StudyID

X.Dend 2017 (120)

F.Chen 2017 (60)S.Wu 2017 (208)

J.Bai 2018 (106)

S.Chen 2016 (112)

C.Fu 2019 (122)

1.04 (0.85, 1.27)

0.60 (0.21, 1.68)

0.81 (0.48, 1.39)

1.89 (0.87, 4.10)1.20 (0.56, 2.55)

1.25 (0.53, 2.95)

1.01 (0.42, 2.43)

0.83 (0.27, 2.55)

1.44 (0.52, 4.01)

0.74 (0.37, 1.51)

OR (95% CI)

0.55 (0.23, 1.30)

0.66 (0.18, 2.36)1.11 (0.64, 1.93)

1.66 (0.76, 3.61)

1.24 (0.59, 2.63)

1.19 (0.57, 2.49)

100.00

4.91

15.78

4.916.49

4.94

5.21

3.54

3.23

9.32

%Weight

7.44

3.0712.64

5.19

6.49

6.84

.183 1 5.47

(a)

Overall (I2 = 0.0%, P = 0.996)

F.Chen 2017 (60)

Y.Wang 2016 (108)

J.Huang 2017 (61)

X.Zhang 2015 (229)

S.Chen 2016 (112)

S.Wu 2017 (208)

Y.Xu 2018 (60)

C.Wang 2015 (85)

StudyID

R.Ruan 2018 (50)

C.Fu 2019 ( 122)

J.Bai 2018 (106)

1.06 (0.84, 1.35)

1.00 (0.28, 3.54)

1.13 (0.53, 2.44)

1.15 (0.41, 3.20)

0.88 (0.49, 1.60)

1.08 (0.51, 2.29)

1.05 (0.60, 1.84)

1.21 (0.43, 3.39)

1.40 (0.60, 3.29)

OR (95% CI)

0.80 (0.25, 2.55)

1.37 (0.65, 2.89)

0.80 (0.34, 1.91)

100.00

3.72

9.47

5.31

18.04

10.05

18.56

5.11

6.89

%Weight

4.96

9.15

8.74

1.251 1 3.99

(b)

Note: weights are from random effects analysis

Overall (I2 = 70.4%, P = 0.000)

Y.XuU 2018 (60)

X.Zhang 2015 (229)

Y.Wang 2016 (108)S.Huang 2016 (109)

C.Wang 2015 (85)

J.Zheng 2016 (96)

R.Ruan 2018 (50)

J.Huang 2017 (61)

X.Chen 2015 (123)

StudyID

X.Deng 2017 (120)

F.Chen 2017 (60)S.Wu 2017 (208)

J.Bai 2018 (106)

S.Chen 2016 (112)

C.Fu 2019 (122)

1.50 (1.03, 2.20)

4.72 (1.44, 15.46)

0.81 (0.48, 1.36)

2.35 (1.05, 5.27)0.67 (0.31, 1.43)

0.89 (0.37, 2.12)

0.51 (0.22, 1.16)

4.52 (1.37, 14.98)

3.37 (1.14, 9.94)

0.79 (0.38, 1.61)

OR (95% CI)

0.80 (0.35, 1.83)

7.43 (2.46, 22.44)1.09 (0.63, 1.90)

1.47 (0.68, 3.20)

4.22 (1.90, 9.39)

1.29 (0.63, 2.63)

100.00

5.06

8.34

6.857.09

6.54

6.71

5.01

5.51

7.33

6.75

5.408.17

7.02

6.90

7.33

%Weight

1.0446 22.4

(c)

Overall (I2 = 19.8%, P = 0.238)

R.Ruan 2018 (50)

Y.Wang 2016 (108)

Y.Xu 2018 (60)

X.Dend 2017 (120)

J.Zheng 2016 (96)

IDStudy

S.Huang 2016 (109)

S.Wu 2017 (208)

X.Chen 2015 (123)

J.Bai 2018 (106)

C.Wang 2015 (85)

X.Zhang 2015 (229)

S.Chen 2016 (112)

C.Fu 2019 (122)

F.Chen 2017 (60)

3.09 (2.49, 3.83)

3.31 (1.02, 10.72)

6.79 (2.92, 15.77)

4.97 (1.46, 16.89)

2.89 (1.29, 6.46)

5.16 (2.10, 12.68)

4.09 (1.67, 9.99)

1.98 (1.03, 3.80)

3.01 (1.44, 6.26)

5.44 (2.34, 12.63)

1.86 (0.79, 4.40)

1.82 (1.07, 3.09)

5.65 (2.18, 14.61)

2.53 (1.22, 5.27)

3.60 (1.22, 10.64)

100.00

3.22

4.34

2.44

7.54

4.27

Weight%

5.19

13.32

8.61

4.88

7.96

21.10

4.06

9.44

3.63

OR (95% CI)

1.0592 1 16.9

(d)

Overall (I2 = 10.8%, P = 0.347)

X.Chen 2015 (123)

J.Huang 2017 (61)

J.Zheng 2016 (96)

Y.Wang 2016 (108)

J.Bai 2018 (106)

IDStudy

C.Wang 2015 (85)

2.37 (1.68, 3.37)

3.58 (1.69, 7.61)

4.62 (1.55, 13.82)

1.48 (0.58, 3.74)

OR (95% CI)

2.00 (0.93, 4.33)

3.03 (1.25, 7.34)

1.38 (0.58, 3.24)

100.00

17.41

7.15

18.17

22.16

13.57

21.54

%Weight

.0724 1 13.8

(e)

Note: weights are from random effects analysis

Overall (I2 = 55.8%, P = 0.009)

X.Chen 2015 (123)

S.Wu 2017 (208)

S.Chen 2016 (112)

J.Huang 2017 (61)

X.Zhang 2015 (229)

X.Deng 2017 (120)

J.Bai 2018 (106)

Y.Xu 2018 (60)

C.Wang 2015 (85)

StudyID

J.Zheng 2016 (96)

R.Ruan 2018 (50)

Y.Wang 2016 (108)

3.33 (2.29, 4.84)

5.52 (2.43, 12.56)

1.71 (0.97, 3.00)

6.32 (2.70, 14.79)

2.67 (0.76, 9.41)

1.80 (1.06, 3.04)

3.86 (1.66, 8.94)

6.22 (2.58, 15.01)

3.79 (1.27, 11.25)

7.22 (2.69, 19.37)

1.31 (0.41, 4.18)

1.29 (0.39, 4.25)

5.63 (2.46, 12.88)

100.00

8.99

11.67

8.73

5.67

12.08

8.81

8.45

6.78

7.55

6.28

6.07

8.93

OR (95% CI)

7.55

%Weight

1.0516 1 19.4

(f)

Overall (I2 = 47.1%, P = 0.078)

F.Chen 2017 (60)

J.Zheng 2016 (96)

J.Bai 2018 (106)

S.Chen 2016 (112)

C.Wang 2015 (85)

IDStudy

C.Fu 2019 (122)

S.Huang 2016 (109)

6.85 (4.23, 11.11)

1.30 (0.31, 5.40)

4.30 (1.69, 10.95)

9.23 (1.07, 79.65)

59.34 (3.43, 1026.07)

4.22 (1.08, 16.44)

8.14 (1.76, 37.61)

15.16 (5.50, 41.80)

100.00

22.40

30.24

4.94

1.92

15.65

10.80

14.06

OR (95% CI) %Weight

1.00097 1 1026

(g)

Note: weights are from random effects analysis

Overall (I2 = 69.1%, P = 0.000)

J.Huang 2017 (61)

X.Zhang 2015 (229)

Y.Wang 2016 (108)

C.Wang 2015 (85)

S.Huang 2016 (109)

X.Deng 2017 (120)

J.Bai 2018 (106)

X.Chen 2015 (123)

StudyID

Y.Xu 2018 (60)

C.Fu 2019 (122)

S.Wu 2017 (208)

1.17 (0.75, 1.81)

2.48 (0.84, 7.30)

0.72 (0.35, 1.45)

1.69 (0.79, 3.62)

0.64 (0.25, 1.62)

3.15 (1.41, 7.08)

0.68 (0.31, 1.50)

0.44 (0.20, 0.97)

0.70 (0.34, 1.42)

1.76 (0.60, 5.14)

4.44 (1.87, 10.57)

0.81 (0.44, 1.51)

100.00

7.39

9.92

9.54

8.40

9.21

9.40

9.37

9.92

7.44

8.80

10.59

OR (95% CI) %Weight

.0946 1 10.6

(h)

Figure 2: Meta-analysis estimating the correlation between CCAT2 and clinicopathological parameters in cancer patients: (a) Age(P′ = 0:741, fixed effects model); (b) gender (P′ = 0:996, fixed effects model); (c) tumor size (P′ < 0:001, random effects model); (d)clinical stage (P′ = 0:238, fixed effects model); (e) T (P′ = 0:347, fixed effects model); (f) N (P′ = 0:009, random effects model); (g) M(P′ = 0:078, fixed effects model); (h) differentiation (P′ < 0:001, random effects model).

7Disease Markers

PANC-1 xenografts in vivo, and KRAS regulated CCAT2 viathe MEK/ERK signaling pathway [27]. Even though we havemade progress in understanding the role of CCAT2 in malig-nant tumors, the precise molecular mechanism of its biolog-ical function remains unclear. Therefore, we collectedvalidated CCAT2 targeting genes using the GEPIA andLncRNA2Target platforms and performed a comprehensivetarget gene network analysis.

The analysis of GO and KEGG pathways suggests thatCCAT2 may play a key role in human tumors through dif-ferent pathways, including miRNAs in the cancer pathway,etc. miRNAs are defined as small noncoding sets of 19 to24 nucleotides associated with mRNA expression and reg-ulate the expression of downstream gene targets, includingoncogenes, tumor suppressor genes, and transcription fac-tors [28]. Studies have shown that miRNAs are expressedin several malignancies, including hepatocellular carcinoma[29], hepatoblastoma [30], cervical cancer [31], and coloncancer [32], which play the vital part in the diagnosisand prognosis.

Compared to previous meta-analyses, our research hasseveral advantages [13–15]. First of all, the included studiesand cases extended from 11 studies with 1335 cases [13] to20 studies and TCGA database with 13285 cases. Moreover,we performed several subgroup analyses to further explorethe role of CCAT2 in different types of tumors and alsoachieved a significant correlation between high CCAT2expression and worse OS in survival curve studies. Last butnot least, all types of tumors were included in our study,

which was lacking in previous meta-analyses. More impor-tantly, our study found that CCAT2 is involved in tumorprogression by modulating miRNAs in the cancer pathway.These findings are following previous publications thatCCAT2 increases the growth, invasion, and migration ofcolon cancer cells and endometrial cancer cells by lncRNA-miRNA crosstalk [33, 34].

Although our study attempts to fully elucidate the associ-ation between CCAT2 and cancer progression and prognosis,our research has some limitations. For the meta-analysis, dif-ferent definitions of high CCAT2 expression levels in selectedstudies are factors that contribute to publication bias.Besides, the current eligible countries in the meta-analysisare only China, the USA, and Japan, and more trials in othercountries should confirm our research. At the same time,since there is no direct data for multivariate analysis in someexisting studies, we have to extract relevant data throughKaplan-Meier curves, which may lead to deviations in HRvalues. More importantly, all available studies are retrospec-tive studies that tend to be published when positive resultsare confirmed. Thus, the impact of CCAT2 on the prognosisand clinicopathological parameters of malignant tumors maybe overestimated. Therefore, further research is needed tostudy the clinical significance and diagnostic value of CCAT2in human cancer. Furthermore, although CCAT2 can actthrough a variety of mechanisms, based on the correlationof gene expression levels between CCAT2 and miRNA, onlyone possible mechanism of the role of CCAT2 in gene regu-lation has been investigated. In order to understand more

Note: weights are from random effects analysisOverall (I2 = 88.1%, P = 0.000)

Y.Cai 2015 (67)

THCA (509)

UVM (80)

SARC (261)

ESCA (162)

TCGA

STAD (365)

PCPG (183)

Y.Ma 2017 (62)

X.Zhang 2015 (229)

READ (159)

THYM (118)

PRAD (495)

S.Wu 2017 (208)

X.Chen 2015 (123)

Study

Subtotal (I2 = 31.3%, P = 0.091)

J.Huang 2017 (61)

LUAD (503)

S.Huang 2016 (109)

LGG (523)

CHOL (36)

J.Zheng 2016 (96)

Subtotal (I2 = 90.8%, P = 0.000)

SKCM (440)

KICH (64)

LUSC (469)

DLBC (47)

BRCA (1082)

HNSC (495)

ACC (79)

COAD (447)

C.Wang 2015 (85)

CESC (306)

OV (374)

R.Ruan 2018 (50)

UCEC (537)

KIRC (517)

Y.Wang 2016 (108)

F.Chen 2017 (60)

D.Fu 2018 (74)J.Bai 2018 (106)

UCS (56)

S.Chen 2016 (112)

LIHC (369)

PAAD (178)

C.Fu 2019 (122)

Y.Xu 2018 (60)

T.Ozawa 2017 (300)

MESO (85)

TGCT (139)

KIRP (288)LAML (75)

X.Deng 2017 (120)

L.Yan 2018 (40)

BLCA (406)

IDStudy

1.15 (1.04, 1.26)

HR (95% CI)

3.57 (1.77, 7.21)

1.10 (0.71, 1.69)

1.04 (0.71, 1.52)

1.43 (1.17, 1.74)

0.80 (0.63, 1.01)

0.79 (0.68, 0.92)

2.00 (0.93, 4.31)

1.60 (1.03, 2.47)

1.43 (1.00, 2.04)

0.34 (0.25, 0.45)

1.77 (0.92, 3.38)

0.76 (0.62, 0.92)

1.21 (0.90, 1.88)

2.81 (1.50, 6.17)1.75 (1.50, 2.01)

3.02 (1.14, 7.96)

1.00 (0.88, 1.13)

2.94 (1.53, 5.87)

1.09 (0.97, 1.22)

0.65 (0.38, 1.12)

2.29 (1.37, 3.53)

1.01 (0.90, 1.12)

0.84 (0.75, 0.95)

0.46 (0.15, 1.38)

1.11 (0.97, 1.26)

0.07 (0.00, 1.34)

0.94 (0.86, 1.04)

1.02 (0.91, 1.14)

2.48 (1.66, 3.69)

1.09 (0.91, 1.29)

2.40 (1.19, 5.42)

0.86 (0.70, 1.07)

1.15 (1.02, 1.30)

1.32 (0.88, 1.97)

1.43 (1.23, 1.67)

0.54 (0.46, 0.65)

2.11 (1.44, 3.20)

2.46 (1.71, 3.53)

1.56 (1.06, 2.29)3.10 (2.17, 4.43)

2.17 (1.52, 3.09)

1.66 (1.22, 2.27)

1.51 (1.25, 1.82)

1.48 (1.23, 1.78)

2.13 (1.27, 8.77)

2.39 (1.02, 5.58)

2.40 (1.22, 4.59)

1.04 (0.82, 1.33)

1.50 (0.33, 6.72)

1.31 (1.01, 1.69)1.06 (0.77, 1.47)

1.89 (1.36, 2.63)

0.69 (0.22, 2.21)

0.88 (0.76, 1.01)

100.00

0.16

2.04

2.34

2.78

3.11

3.29

0.38

1.38

1.94

3.33

0.65

3.22

2.03

0.2118.53

0.10

3.28

0.24

3.28

2.47

0.79

81.47

3.33

1.65

3.23

1.50

3.35

3.30

0.87

3.11

0.25

3.12

3.25

1.86

3.01

3.34

1.07

1.02

1.650.74

1.24

1.92

2.78

2.82

0.08

0.22

0.38

2.89

0.11

2.582.54

1.60

0.90

3.28

Weight%

1.15 (1.04, 1.26)

1.10 (0.71, 1.69)

1.04 (0.71, 1.52)

1.43 (1.17, 1.74)

0.80 (0.63, 1.01)

0.79 (0.68, 0.92)

2.00 (0.93, 4.31)

0.34 (0.25, 0.45)

1.77 (0.92, 3.38)

0.76 (0.62, 0.92)

1.00 (0.88, 1.13)

1.09 (0.97, 1.22)

0.65 (0.38, 1.12)

1.01 (0.90, 1.12)

0.84 (0.75, 0.95)

0.46 (0.15, 1.38)

1.11 (0.97, 1.26)

0.07 (0.00, 1.34)

0.94 (0.86, 1.04)

1.02 (0.91, 1.14)

2.48 (1.66, 3.69)

1.09 (0.91, 1.29)

0.86 (0.70, 1.07)

1.15 (1.02, 1.30)

1.43 (1.23, 1.67)

0.54 (0.46, 0.65)

2.17 (1.52, 3.09)

1.51 (1.25, 1.82)

1.48 (1.23, 1.78)

1.04 (0.82, 1.33)

1.50 (0.33, 6.72)

1.31 (1.01, 1.69)1.06 (0.77, 1.47)

0.88 (0.76, 1.01)

100.00

2.04

2.34

2.78

3.11

3.29

0.38

3.33

0.65

3.22

3.28

3.28

2.47

81.47

3.33

1.65

3.23

1.50

3.35

3.30

0.87

3.11

3.12

3.25

3.01

3.34

1.24

2.78

2.82

2.89

0.11

2.582.54

3.28

0-8.77 0 8.77

(a)

Heterogeneity between groups: P = 0.010Overall (I2 = 31.3%, P = 0.091)

D.Fu 2018 (74)

L.Yan 2018 (40)

S.Chen 2016 (112)

J.Zheng 2016 (96)

Y.Ma 2017 (62)

Y.Wang 2016 (108)

Subtotal (I2 = 28.0%, P = 0.238)

GC

BC

J.Huang 2017 (61)

Y.Xu 2018 (60)

S.Wu 2017 (208)

C.Wang 2015 (85)

X.Deng 2017 (120)

Subtotal (I2 = 0.0%, P = 0.670)

S.Huang 2016 (109)

Y.Cai 2015 (67)

Subtotal (I2 = 47.5%, P = 0.149)

X.Chen 2015 (123)

Others

IDStudy

R.Ruan 2018 (50)

X.Zhang 2015 (229)

CCA

T.Ozawa 2017 (300)

Subtotal (I2 = 15.6%, P = 0.276)

J.Bai 2018 (106)Subtotal (I2 = 0.0%, P = 0.585)

OS

Subtotal (I2 = 0.0%, P = 0.865)F.Chen 2017 (60)C.Fu 2019 (122)HCC

1.66 (1.47, 1.85)

1.56 (1.06, 2.29)

HR (95% CI)

0.69 (0.22, 2.21)

1.66 (1.22, 2.27)

2.29 (1.37, 3.53)

1.60 (1.03, 2.47)

2.11 (1.44, 3.20)

1.98 (1.36, 2.60)

3.02 (1.14, 7.96)

2.39 (1.02, 5.58)

1.21 (0.90, 1.88)

2.40 (1.19, 5.42)

1.89 (1.36, 2.63)

1.67 (1.40, 1.94)

2.94 (1.53, 5.87)

3.57 (1.77, 7.21)

1.47 (1.04, 1.89)

2.81 (1.50, 6.17)

1.32 (0.88, 1.97)

1.43 (1.00, 2.04)

2.40 (1.22, 4.59)

1.17 (0.70, 1.65)

3.10 (2.17, 4.43)2.96 (1.95, 3.97)

2.44 (1.56, 3.33)2.46 (1.71, 3.53)2.13 (1.27, 8.77)

100.00

9.27

3.54

12.72

3.01

6.77

4.53

9.17

0.30

0.67

14.49

0.79

8.70

47.77

0.74

0.47

19.80

0.64

Weight%

11.81

13.07

1.24

15.35

2.753.42

4.484.240.25

1.66 (1.47, 1.85)

1.56 (1.06, 2.29)

0.69 (0.22, 2.21)

1.66 (1.22, 2.27)

2.29 (1.37, 3.53)

1.60 (1.03, 2.47)

2.11 (1.44, 3.20)

1.98 (1.36, 2.60)

3.02 (1.14, 7.96)

2.39 (1.02, 5.58)

1.21 (0.90, 1.88)

2.40 (1.19, 5.42)

1.89 (1.36, 2.63)

1.67 (1.40, 1.94)

2.94 (1.53, 5.87)

3.57 (1.77, 7.21)

1.47 (1.04, 1.89)

2.81 (1.50, 6.17)

1.32 (0.88, 1.97)

1.43 (1.00, 2.04)

2.40 (1.22, 4.59)

1.17 (0.70, 1.65)

3.10 (2.17, 4.43)2.96 (1.95, 3.97)

100.00

9.27

3.54

12.72

3.01

6.77

4.53

9.17

0.30

0.67

14.49

0.79

8.70

47.77

0.74

0.47

19.80

0.64

11.81

13.07

1.24

15.35

2.753.42

0−8.77 0 8.77

(b)

Figure 3: Subgroup analyses. (a) A meta-analysis of the selected studies and TCGA data estimating the association of CCAT2 with thepatients’ OS (I2 ≥ 50%, random effects model). (b) Subgroup analyses of the OS based on the tumor type (I2 < 50%, fixed effects model).

8 Disease Markers

features of CCAT2, further research is needed to exploreother possible mechanisms.

In light of our findings, we believe that the expression ofCCAT2 may serve as potential candidates for prognostic fac-tors as well as therapeutic targets in malignant tumors. Ofnote, the prognostic roles of CCAT2 varied greatly acrosscancers, which implied a noteworthy amount of heterogene-ity between different types of tumors. In addition, the expres-sion of CCAT2 was closely associated with tumor size,

clinical stage, and TNM classification and mainly expressedin stage II, which indicated that CCAT2 is a significant bio-marker to monitor tumor progression. Current findingsenhance our understanding of the CCAT2 in cancer monitorand identify strategies for the early invention in clinical man-agement. Moreover, further illumination of the underlyingmechanism and the interaction between CCAT2 and tumorsmay provide important implications for the success of earlymonitoring and prognosis prediction in cancers.

0

.2

.4

.6

s.e. o

f HR

–.5 0 .5 1 1.5 2HR

Funnel plot with pseudo 95% confidence limits

(a)

0

.2

.4

.6

.8

s.e. o

f HR

–3 –2 –1 0 1 2HR

Funnel plot with pseudo 95% confidence limits

(b)

0

.2

.4

.6

.8

s.e. o

f OR

–1 0 1 2OR

Funnel plot with pseudo 95% confidence limits

(c)

0

.2

.4

.6

s.e. o

f OR

–1 –.5 0 .5 1 1.5OR

Funnel plot with pseudo 95% confidence limits

(d)

0

.2

.4

.6

s.e. o

f OR

–1 0 1 2OR

Funnel plot with pseudo 95% confidence limits

(e)

0

.2

.4

.6

s.e. o

f OR

0 .5 1 1.5 2 2.5OR

Funnel plot with pseudo 95% confidence limits

(f)

s.e. o

f OR

–.5 0 .5 1 1.5 2OR

Funnel plot with pseudo 95% confidence limits0

.2

.4

.6

(g)

0

.2

.4

.6

s.e. o

f OR

0 .5 1 1.5 2 2.5OR

Funnel plot with pseudo 95% confidence limits

(h)

0

.5

1

1.5

s.e. o

f OR

–2 0 2 4 6OR

Funnel plot with pseudo 95% confidence limits

(i)

0

.2

.4

.6

s.e. o

f OR

–1 –.5 0 .5 1 1.5OR

Funnel plot with pseudo 95% confidence limits

(j)

Figure 4: Funnel plots (Begg’s method) of potential publication bias in the selected studies. (a) OS of study patients (Pr = 0:496); (b) OS inTCGA data (Pr = 0:455); (c) age (Pr = 0:921); (d) gender (Pr = 1:000); (e) tumor size (Pr = 0:038); (f) clinical stage (Pr = 0:050); (g) T(Pr = 1:000); (h) N (Pr = 0:837); (i) M (Pr = 0:548); (j) differentiation (Pr = 0:213).

9Disease Markers

n =

110

9n

= 1

13

n =

151

n =

0

n =

502

n =

49

n =

529

n =

0

n =

480

n =

41

n =

552

n =

35

n =

379

n =

0

n =

539

n =

72

n =

374

n =

50

n =

510

n =

58

n =

56

n =

0

n =

375

n =

32

n =

156

n =

0

n =

86

n =

0

n =

502

n =

44

n =

471

n =

1

n =

535

n =

59

n =

499

n =

52

n =

178

n =

4

n =

79

n =

0

n =

306

n =

3

n =

183

n =

3

n =

119

n =

2

n =

169

n =

5

n =

65

n =

24

n =

263

n =

2

n =

162

n =

11

n =

48

n =

0

n =

414

n =

19

n =

289

n =

32

n =

167

n =

10

n =

36

n =

9

n =

80

n =

0

Tumor

8.0BRCA LAML LUSC LGG COAD UCEC OV KIRC LIHC THCA UCS STAD TGCT MESO HNSC SKCM

CCAT2[log2(⨯+1)] in tumorLUAD PRAD PAAD ACC CESC PCPG THYM GBM KICH SARC ESCA DLBC BLCA KRIP READ CHOL UVM

7.5

6.5

5.5

4.5

3.5

2.5

1.5

0.5

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

Status

Normal

(a)

2.5

2

1.5

1

0.5

0COAD KIRC

TumorNormal

STAD PRAD

Tumor > normal

ESCA READ

(b)

1.61.41.2

0.80.60.40.2

0

1

Normal > tumor

BRCA LUSC THCA PAAD

(c)

0.0

0.5

1.0

1.5

2.0

2.5

3.0 F value = 1.98Pr(>F) = 0.0783

Stage 0 Stage I Stage II Stage III Stage IV Stage X

(d)

Figure 5: CCAT2 expression profile across tumor samples and adjacent normal tissues from TCGA; log2(TPM + 1) scale. (a) The level ofCCAT2 expression of all types of tumors is shown, box plot. (b) The level of CCAT2 expression was higher in the tumor than in adjacentnormal tissue (COAD/KIRC/STAD/PRAD/ESCA/READ), bar plot. (c) The level of CCAT2 expression was lower in the tumor than inadjacent normal tissue (BRCA/LUSC/THCA/PAAD), bar plot. (d) The pathological stage plot of CCAT2 from GEPIA. ∗The height of thebar represents the median expression of specific tumor types or normal tissues.

6

5

4

3

2

Transcription, DNA-templated

Path

way

nam

e

Regulaton of transcription, DNA-templatedRegulaton of organ growth

Regulaton of mRNA stabilityProtein phosphorylation

Posttranscription al regulation of gene expressionPositive regulation of transcription, DNA-templated

Positive regulation of transcription from RNA polymerase II promoterPeptidyl-serine phosphorylation

Ovarian follicle developmentNuclear-transcribed mRNA catabolic process

Negative regulation of cyclin-development protein serine/threonine kinase activityMitotic nuclear division

Microtubule organizing center organizationInner cell mass cellular morphogenesis

Inner cell mass cell fate commitmentHormone-mediate signaling pathway

Hippo signalingG1/S transition of mitotic cell cycle

DNA repairDNA methylation

Deadenylation-dependent decapping of nuclear-transcribe mRNACell division

0.000 0.025 0.050P value

Pathway enrichment

Count

–Log10(P value)

510

1520

0.075 0.100

(a)

7

6

5

4

3

2

Trans-golgi networkSpindle pole

NucleusNucleoplasm

Microtubule organizing centerIntracellular

Intermediate filament cytoskeletonCytoplasmic mRNA processing body

Apical junction complex

Path

way

nam

e

Pathway enrichment

0.000 0.025 0.050P value

0.075

102030

Count

–Log10(P value)

(b)

Path

way

nam

e

Transcriptional activator activity, RNA polymerase II core promoter proximal region sequence-specific bindingTranscription factor activity, sequence-specific DNA binding

Transcription coactivator activityRNA polymerase II core promoter proximal region sequence-specific DNA binding

Protein bindingPoly(A) RNA binding

Nucleic acid bindingMetal ion binding

DNA bindingATP binding

Ligase activityHelicase activity

Count

–Log10(P value)

1020

3040

8

6

4

2

Pathway enrichment

0.000 0.025 0.050P value

0.075

(c)

RNA degradation

Ribosome biogenesis in eukaryotes

MicroRNAs in cancer

Hippo signaling pathway

Path

way

nam

e

Cell cycle

Count

–log10(P value)

3.03.54.0

4.55.0

2.5

2.0

1.5

Pathway enrichment

0.000 0.025 0.050P value

0.075

(d)

Figure 6: Bubble plot for GO/KEGG term analysis of CCAT2: (a) BPs; (b) CCs; (c) MFs; (d) KEGG pathway.

10 Disease Markers

5. Conclusions

Our study demonstrated that a higher CCAT2 expressionwas significantly associated with an aggressive disease coursein patients with cancer, predicting a larger tumor size, moreadvanced clinical stage, more inferior TNM classification,and shorter OS. We also demonstrated that CCAT2 playsan essential role in the biological processes of tumor progres-sion via a variety of pathways.

Abbreviations

lncRNA: Long noncoding RNA

CCAT2: Colon cancer-associated transcript 2 geneTCGA: The Cancer Genome AtlasGO: Gene OntologyKEGG: Kyoto Encyclopedia of Genes and GenomesT: Tumor classificationN: Lymph node classificationM: Metastasis classificationncRNA: Noncoding RNARNA-Seq: RNA deep sequencingCNKI: China National Knowledge InfrastructureqRT-PCR: Quantitative real-time polymerase chain reactionNOS: Newcastle-Ottawa ScaleHR: Hazard ratio

LATS1

Regulation of mRNAstability

ZNF45

Proteinphosphorylation

Microtubuleorganizing

center

Inner cell mass cellular

morphogenesis

Hormone-mediatedsignalingpathway

Regulation of organ growth

Inner cell mass cell fate commitment

BIRC6

Hipposignaling

Deadenylation-dependentdecapping of

nuclear-transcribedmRNA

Mitoticnucleardivision

RNAdegradation

Apical junction complex

G1/Stransition of mitotic cell

cycle

Cell division

CytoplasmicmRNA

processingbody

ORC2SHPRH

SMG1

Nucleic acid binding

Transcriptionfactor activity,

sequence-specificDNA binding

MED14

RC3H1

Peptidyl-serinephosphorylation

Negativeregulation of

cyclin-dependentprotein

serine/threoninekinase activity

Intracellular

CBLC

Metal ion binding

ZNF283PHC3

ZNF558

ZNF292

NFAT5 GNL3L

MicroRNAs in cancer

MIR145

Regulation of transcription,

DNA-templated

MIR20A

Transcription,DNA-templated

DNA binding

DCP1A

PHIP

NOBOX

ATG5

MYC

TET3

INO80D

LATS2

XRN1

PVR

DNAmethylation

ATRX

SRCAP

ZNF180

RNApolymerase II core promoter

proximalregion

sequence-specificDNA binding

Transcriptionalactivator

activity, RNA polymerase II core promoter

proximalregion

sequence-specificbinding

VIM

Nuclear-transcribedmRNA

catabolicprocess

SNAI2

POU5F1B

ZBTB7A

ZNF142

Spindle pole

PKN2

ZNF121

ZNF284

ATP binding

Nucleus

Poly(A) RNA binding

ANAPC1

QRSL1

Posttranscriptionalregulation of

geneexpression

WDR43

CDH1

Trans-Golginetwork

ZNF224ZNF317

ZNF234ZNF227

ZNF699

Hipposignalingpathway

MIR17 Protein binding

GCC2

RBM26

NOL9

Microtubuleorganizing

centerorganization

CD24

KMT2ETAF4

Ovarian follicle development

RNF43

Positiveregulation of transcription

from RNA polymerase II

promoter

XPO4

NAA25

PUM1

PAN3

Cell cycle

Nucleoplasm

Ribosomebiogenesis in eukaryotes

Intermediatefilament

cytoskeleton

Transcriptioncoactivator

activity

ZNF460

LCOR

DNA repair

PRDM10

VPS36

CCAT2

Ligase activity

POU2F1

Positiveregulation of transcription,

DNA-templated

Helicaseactivity

Figure 7: The network for the GO/KEGG analysis of CCAT2.

11Disease Markers

CI: Confidence intervalOR: Odds ratioOS: Overall survivalPFS: Progression-free survivalRFS: Recurrence-free survivalDFS: Disease-free survivalmiRNA: MicroRNABP: Biological processesCC: Cellular componentsMF: Molecular factors.

Data Availability

Data from cancer patients used in this study were col-lected in the published papers and the TCGA database.The results of the statistical analysis of these data wereplaced in this manuscript.

Conflicts of Interest

The authors declared no conflict of interest.

Authors’ Contributions

Bowen Huang and Min Yu contributed equally to this work.

Acknowledgments

This study was sponsored by grants from the NationalNatural Science Foundation of China (No. 81672475),Guangdong Provincial Science and Technology Plan Pro-jects (No. 2016A030313769), and Guangzhou Science andTechnology Plan Project, People’s Republic of China(No. 201707010323).

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13Disease Markers