Disease Markers
Volume 2018 (2018), Article ID 5340894, 15 pages
https://doi.org/10.1155/2018/5340894

Review Article

Prognostic Value of Long Noncoding RNAs in Patients with Gastrointestinal Cancer: A Systematic Review and Meta-Analysis

Weibiao Kang,¹ Qiang Zheng,¹ Jun Lei,² Changyu Chen,³ and Changjun Yu¹

¹Department of Gastrointestinal Surgery, Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China
²Department of General Surgery, Lu’an People’s Hospital, Luan, China
³Department of General Surgery, First Affiliated Hospital of Anhui Traditional Medical University, Hefei, China

Correspondence should be addressed to Changjun Yu; yuchangjun1206@163.com

Received 22 June 2018; Revised 10 September 2018; Accepted 20 September 2018; Published 26 November 2018

Academic Editor: Giuseppe Biondi-Zoccai

Copyright © 2018 Weibiao Kang 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.

Abstract.

Gastrointestinal cancers (GICs) are a huge threat to human health, which mainly include esophageal, gastric, and colorectal cancers. The purpose of this study was to clarify the prognostic value of long noncoding RNAs (lncRNAs) in GICs. A total of 111 articles were included, and 13103 patients (3123 with esophageal cancer, 4972 with gastric cancer, and 5008 with colorectal cancer) were enrolled in this study. The pooled hazard ratio (HR) values and corresponding 95% confidence interval (95% CI) of overall survival (OS) related to different lncRNA expressions in esophageal, gastric, colorectal, and gastrointestinal cancer patients were 1.92 (1.70–2.16), 1.96 (1.77–2.16), 2.10 (1.87–2.36), and 2.00 (1.87–2.13), respectively. We have identified 74 lncRNAs which were associated closely with poor prognosis of GIC patients, including 58 significantly upregulated lncRNA expression and 16 significantly downregulated lncRNA expression. In addition, 47 of the included studies revealed relative mechanisms and 12 of them investigated the correlation between lncRNAs and microRNAs. Taken together, this meta-analysis supports that specific lncRNAs are significantly related to the prognosis of GIC patients and may serve as novel markers for predicting the prognosis of GIC patients. Furthermore, lncRNAs may have a promising contribution to lncRNA-based targeted therapy and clinical decision-making in the future.

1. Introduction

Gastrointestinal cancers (GICs) are one of the most common causes of cancer-related deaths with a high mortality worldwide, which mainly include esophageal, gastric, and colorectal cancers (EC, GC, and CRC). In addition to aging and expansion of world population, cancer-causing behaviors play a key role in the increasing largely global burden of GIC, such as smoking and changes in dietary patterns [1]. There are many therapy strategies applicable to GIC patients, such as surgery, neoadjuvant chemoradiotherapy, and adjuvant chemoradiotherapy [2], and GIC patients at early stage could be curable by receiving suitable treatment with a 90% five-year overall survival, However, five-year overall survivals are still poor for patients with advanced stages [3, 4]. Consequently, early diagnosis and selection of high-risk individuals with poor prognosis are important in the recovery of patients. However, effective methods to evaluate prognosis of GIC patients are still lacking nowadays. Currently, mounting reports have reported that noncoding RNA could be used to predict the prognosis of GIC patients, For example, microRNAs are potentially eligible for predicting the survival of GIC patients [5]. Many studies indicated that long noncoding RNAs (lncRNAs) could competitively suppress microRNAs by acting as molecular sponges recently [6]. Besides, aberrant expression of specific lncRNAs as molecular biomarkers was associated closely with prognosis of GIC patients and involved in targeted therapy, which might promote the development of novel prevention strategies and advanced therapies [7–12].

lncRNA is a long (more than 200 nucleotides) class of noncoding RNA that is often expressed in a disease-, tissue-, or stage-specific manner [13]. According to recent estimate, more than 28000 distinct lncRNAs are encoded by human genome and they regulate gene expression by means of different mechanisms, including chromatin modification, transcription, and posttranscriptional processing, which are becoming attractive therapeutic targets of cancers [14, 15]. Such upregulated lncRNA HOXA11-AS expression promotes tumor proliferation and invasion by scaffolding the chromatin modification factors PRC2, LSD1, and DNMT1 [16]. lncRNA FEZF1-AS1 recruits and bounds to LSD1 to epigenetically repress downstream gene p21, thereby promoting proliferation [17], and lncRNA GHET1 promotes gastric carcinoma cell proliferation by increasing c-Myc mRNA stability [18]. Furthermore, lncRNA plays crucial roles in the diverse biological processes such as development, differentiation, and carcinogenesis [19]. In addition, lncRNA may induce resistance of an anticancer drug. For example, upregulated lncRNA MALAT1 induces chemoresistance of CRC cells [20].

Recently, mounting evidences have indicated that various lncRNAs can function as oncogenes or tumor suppressor genes and the dysregulation of lncRNA expression as molecular biomarkers presented promising huge prognostic values in GIC patients [21–26]. However, the ability of evaluating relationship between multiple lncRNA expression and prognosis of GIC patients was limited due to monocentric, small samples and various experimental methods and criteria from different research departments. Therefore, the purpose of the study was to elaborate the relationship between multiple lncRNA expression and prognosis of GIC patients so that further understanding of prognostic values of lncRNAs might promote lncRNA-based target therapeutic development and make a clinical decision that is suitable for the individual quickly.

2. Materials and Methods

2.1. Search Strategy

To obtain the relevant studies for this meta-analysis, two authors (Weibiao Kang and Qiang Zheng) searched a wide range of database (PubMed, Web of Science, and Embase) independently up to August 27, 2018. Search terms are as follows: “LncRNA”, “Long non-coding RNAs”, “lncRNAs”, “lncRNA”, “Long ncRNA”, “LincRNAs”, “LINC RNA”, “Long ncRNAs”, “cancer”, “tumor”, “malignancy”, “carcinoma”, “neoplasia”, “neoplasm”, “gastrointestine”, “gastroenteric”, “colon”, “colorectal”, “rectum”, “intestinal”, “gastric”, “esophageal”, “esophagus”, “follow up studies”, “prognosis”, “prediction”, “survival”, “hazard ratio”, “incidence”, and “mortality”, which were combined with AND/OR.

2.2. Selection Criteria

All eligible studies were assessed and extracted data by the same two investigators independently based on the selection criteria. Inclusion criteria are the following: (1) patients who were diagnosed as having gastrointestinal cancer by pathologists and did not receive any preoperative chemotherapy or radiotherapy before obtaining samples; (2) predicting prognosis of full stage (I–IV) patients on the basis of the expression levels of lncRNAs; (3) the expression levels of lncRNAs were divided into high and low levels; (4) we could obtain overall survival (OS), disease-free survival (DFS), hazard ratio (HR), and 95% confidence interval (95% CI) directly from full text or extract survival data from Kaplan-Meier survival curves. Exclusion criteria are the following: (1) reviews, letters, case reports, statements, and not clinical related studies were excluded; (2) besides non-English and nonhuman studies, articles lack of data were also excluded; (3) studies focused on lncRNA variants or relationship between lncRNA expression and prognosis in different histological types of GIC. We resolved disagreements by discussing with the third investigator (Changjun Yu) and got consensus finally.

2.3. Data Extraction and Quality Assessment

The two authors (Weibiao Kang and Qiang Zheng) extracted data independently and got consensus finally. The characteristics collected of individual articles were as follows: author, year of publication, nation of population enrolled, number of patients, HR and 95% CI (OS/DFS), cut-off value, method, sample type, and follow-up. We assessed the quality of each study by using the guidelines for meta-analysis of observation studies in epidemiology (MOOSE) [27].

2.4. Statistical Analysis

Statistical analysis was conducted by Review Manager 5.2 (provided by Cochrane collaboration). was considered statistically significant. The heterogeneity among studies was calculated by and tests. in combination with  < 50% indicated low heterogeneity; fixed-effect models should be used. Otherwise, random-effect model would be used finally. For some studies from which we could not extract HR and corresponding 95% CI (OS/DFS) directly, Engauge Digitizer 4.1 software was applied to obtain the necessary points and the relevant data from Kaplan-Meier survival curves, then HR and corresponding 95% CI were calculated by published methods proposed by Tierney et al. [28]. Additionally, forest plots of the pooled HR values and funnel plots used to analyse qualitatively publication bias were presented. Furthermore, we also applied sensitivity analysis for this meta-analysis.

3. Results

3.1. Study Identification and Characteristics

According to the selection criteria, a total of 111 articles (21 EC, 47 GC, and 44 CRC; one study involved GC and CRC) involving 13103 patients (3123 with EC, 4972 with GC, and 5008 with CRC) were identified and included in the meta-analysis; specific steps were showed in Figure 1 [10–13, 15–26, 29–123]. Most of the studies taken into account refer to Asian population, especially china. Cut-off values of high or low lncRNA expression were mostly median or mean. Detection methods of lncRNA expression were mainly RT-PCR (reverse transcription PCR) or ISH (in situ hybridization). Sample types were almost from tissues. As for clinical outcome indicators, 74 studies [10–13, 16, 18–23, 25, 26, 29, 31–33, 36, 38, 40, 41, 43–47, 50, 51, 53–58, 61, 63, 64, 66–68, 71–74, 77, 78, 83, 85, 86, 88, 89, 91, 92, 96–102, 105–107, 109–112, 115–119, 121, 122] included overall survival (OS), 8 studies [17, 24, 30, 34, 79, 95, 114, 123] included disease-free survival (DFS), and another 29 studies [15, 35, 37, 39, 42, 48, 49, 52, 59, 60, 62, 65, 69, 70, 75, 76, 80–82, 84, 87, 90, 93, 94, 103, 104, 108, 113, 120] included both OS and DFS. We have identified 74 lncRNAs which were associated closely with poor prognosis of GIC patients, including 58 significantly upregulated lncRNA expression and 16 significantly downregulated lncRNA expression (Tables 1 and 2). Moreover, 47 of the included studies revealed relative mechanisms, and 12 of them investigated the correlation between lncRNAs and microRNAs (Table 3).

Figure 1: Study flow diagram.

Table 1: Characteristics of studies and lncRNA expression related to OS in GIC patients.

References
lncRNAs ()
Year
Nations
Number ()
OS
Cut-off value
Detection methods
Sample types
Follow-up

HR
95% CI

Sun et al. [13]
RNAGAS5↓
2014
China
89 GC
2.43
1.29–4.59
Median
RT-PCR
Tissue
<40

Li et al. [29]
SNHG20↑
2016
China
107 CRC
2.97
1.51–5.82
YI
RT-PCR
Tissue
<40

Kong et al. [15]!
PVT1↑
2015
China
80 GC
2.09
1.07–4.10
Median
RT-PCR
Tissue
<40

Qi et al. [31]
AGAP2-AS1↑
2017
China
50 GC
2.67#
1.45–4.93
Median
RT-PCR
Tissue
6–36#

Chen et al. [32]
XIST↑
2016
China
106 GC
3.11
1.67–3.78
Median
RT-PCR
Tissue
<120

Ye et al. [33]
lnc-GNAT1-1↓
2016
China
68 CRC
2.16
1.01–4.63
Median
RT-PCR
Tissue
<20

Saito et al. [21]
ATB↑
2015
Japan
183 GC
3.50
1.73–7.44
Median
RT-PCR
Tissue
0.192–134.4

Yuan et al. [35]!
PVT1↑
2016
China
111 GC
2.28
1.05–4.93
Median
RT-PCR
Tissue
20–48

Ye et al. [36]
CLMAT3↑
2015
China
90 CRC
2.05
1.10–3.82
Dichotomize
RT-PCR
Tissue
<45

Zheng et al. [37]!
UCA1↑
2015
China
112 GC
2.35
1.22–4.52
Dichotomize
RT-PCR
Tissue
<92

Chen et al. [38]
NEAT1↑
2015
China
96 EC
1.92
1.40–6.49
YI
RT-PCR
Tissue
<80

Wang et al. [39]!
CCAT2↑
2016
China
108 GC
2.11
1.44–3.20
Median
RT-PCR
Tissue
<70

Zhao et al. [22]
HOTAIR↑
2015
China
168 GC
1.47
1.04–2.06
Median
RT-PCR
Tissue
<70

Zhang et al. [40]
Sox2ot↑
2016
China
132 GC
2.05
1.28–3.30
Median
RT-PCR
Tissue
<96

Chen et al. [41]
HIF1A-AS2↑
2015
China
83 GC
1.72
1.00–2.96
Median
RT-PCR
Tissue
<60

Li et al. [10]
HOTAIR↑
2013
China
100 EC
1.91
1.06–4.00
125-fold
RT-PCR
Tissue
<60

Yue et al. [42]!
FER1L4↓
2015
China
70 CC
3.99
1.67–9.01
Median
RT-PCR
Tissue
<80

He et al. [43]
CCAT1↑
2014
China
48 CC
2.09#
1.42–3.06
Median
RT-PCR
Tissue
24–37#

Yin et al. [44]
MEG3↓
2015
China
62 CRC
0.13
0.02–0.99
Mean
RT-PCR
Tissue
<60

Nie et al. [45]
MIR31HG↓
2016
China
48 CC
2.35#
1.15–4.79
Median
RT-PCR
Tissue
3–36#

Park et al. [46]
BM742401↓
2013
Korea
113 GC
1.03
0.57–1.88
Median
RT-PCR
Tissue
<80

Liu et al. [23]
CRNDE-h↑
2016
China
148 CRC
2.39
1.30–4.39
Median
RT-PCR
Serum
1–65

Li et al. [47]
PANDAR↑
2017
China
102 CRC
3.08
0.84–7.89
Median
RT-PCR
Tissue
<60

Chen et al. [48]!
H19↑
2016
China
128 GC
1.96
0.97–3.97
Median
RT-PCR
Tissue
20–48

Zou et al. [49]!
Sox2ot↑
2016
China
155 GC
3.24
1.24–6.43
Median
RT-PCR
Tissue
<70

Jiang et al. [50]
TUG1↑
2016
China
218 EC
1.40
1.01–1.95
NR
RT-PCR
Tissue
12–72

Svoboda et al. [51]
HOTAIR↑
2014
Czech
84 CRC
5.9
1.34–26.1
Median
RT-PCR
Blood
12–54

Wang et al. [52]!
OTUB1-isoform 2↑
2016
China
156 GC
1.54
1.04–2.27
Median
RT-PCR
Tissue
<80

Guo et al. [53]
FTX↑
2015
China
187 CRC
2.37
1.42–2.74
Median
RT-PCR
Tissue
<60

Pan et al. [54]
FOXCUT↑
2014
China
82 EC
2.13#
1.38–3.29
Mean
RT-PCR
Tissue
1–72

Zhou et al. [55]
LET↓
2014
China
93 GC
2.28
1.30–5.18
Mean
RT-PCR
Tissue
<60

Hu et al. [56]
linc-UBC1↑
2015
China
85 GC
3.56#
1.71–7.39
Median
RT-PCR
Tissue
<100

Wang et al. [57]
CCAT2↑
2015
China
86 GC
2.41
1.19–5.42
Mean
RT-PCR
Tissue
<60

Ren et al. [58]
HOTTIP↑
2015
China
156 CRC
2.15
1.31–3.42
Median
RT-PCR
Tissue
33–65

Liu et al. [59]!
DANCR↑
2015
China
104 CRC
2.13
1.16–7.06
Median
RT-PCR
Tissue
<60

Wang et al. [60]!
ZEB1-AS1↑
2015
China
87 EC
2.37
1.28–6.12
Median
RT-PCR
Tissue
<61

Li et al. [61]
BANCR↑
2015
China
184 GC
1.51
1.03–2.23
Median
RT-PCR
Tissue
5–93

Ma [62]!
PANDAR↑
2016
China
100 GC
3.68
1.13–12.06
NR
RT-PCR
Tissue
2–36

Huang et al. [63]
MALAT1↑
2016
China
132 EC
6.64
2.95–14.95
NR
RT-PCR
Tissue
<60

Ni et al. [64]
UCA1↑
2015
China
54 CRC
3.11#
0.59–16.39
Median
RT-PCR
Tissue
9–51#

Wu et al. [25]
uc002yug.2↑
2014
China
684 EC
2.61
1.50–3.78
NR
RT-PCR
Tissue
<140

Sun et al. [16]
HOXA11-AS↑
2016
China
85 GC
2.85#
1.65–4.91
Median
ISH
Tissue
9–36

Peng et al. [65]!
NEAT1↑
2016
China
56 CRC
1.70#
1.04–2.80
NR
RT-PCR
Tissue
<60

Jiao et al. [66]
UCA1↑
2016
China
66 EC
2.24#
1.17–4.29
Median
RT-PCR
Tissue
5–30#

Liu and Shangguan [67]
CARLo-5↑
2017
China
240 GC
2.41
1.13–5.94
0.041
RT-PCR
Tissue
<60

Ma et al. [11]
CCAL↑
2016
China
252 CRC
2.25
1.35–3.74
Median
RT-PCR
Tissue
<100

Yang et al. [18]
GHET1↑
2014
China
42 GC
1.90#
0.53–6.85
Median
RT-PCR
Tissue
7–40#

Wu et al. [68]
HOTAIR↑
2014
China
120 CC
3.92
1.23–12.50
5-fold
RT-PCR
Tissue
10–72

Zhou et al. [69]!
ROR↑
2016
China
60 CC
7.22
2.43–17.43
Median
RT-PCR
Tissue
<80

Yang et al. [70]!
Loc554202↓
2016
China
178 CRC
2.45
1.34–7.74
Median
RT-PCR
Tissue
<70

Lü et al. [71]
BC032469↑
2016
China
58 GC
2.78#
0.95–8.09
Mean
RT-PCR
Tissue
<23

Su et al. [72]
BLACAT1↑
2017
China
48 CRC
1.50
1.32–1.70
Mean
RT-PCR
Tissue
<60

Hu et al. [12]
GAPLINC↑
2014
China
90 GC
1.54
1.22–1.94
Median
ISH
Tissue
<80

Fu et al. [73]
NEAT1↑
2016
China
140 GC
1.61
1.03–2.53
Median
RT-PCR
Tissue
<96

Yao et al. [26]
RP11-766N7.4↓
2017
China
50 EC
2.14#
1.10–4.15
Median
RT-PCR
Tissue
32–60#

Xie et al. [74]
SPRY4-IT1↑
2014
China
92 EC
2.05
1.04–4.03
Median
RT-PCR
Tissue
3–60

Peng [75]!
SPRY4-IT1↑
2015
China
175 GC
0.82
0.31–1.57
Median
RT-PCR
Tissue
<60

Nie et al. [76]!
ZFAS1↑
2016
China
54 GC
2.08#
1.11–3.93
Median
RT-PCR
Tissue
3–36#

Ohtsuka et al. [77]
H19↑
2016
USA
117 CC
1.28
1.08–1.50
0.64
RT-PCR
Tissue
<90

Li et al. [20]
MALAT1↑
2017
China
68 CRC
2.17#
1.32–3.55
Median
RT-PCR
Tissue
1–51#

Zhou et al. [78]
AFAP1-AS1↑
2016
China
162 EC
1.89
1.22–2.92
Median
RT-PCR
Tissue
6–72

Sun et al. [80]!
RP11-119F7.4↓
2015
China
96 GC
1.20#
0.84–1.71
Median
RT-PCR
Tissue
<100

Zhang et al. [81]!
ANRIL↑
2014
China
120 GC
1.74
1.04–2.93
3-fold
RT-PCR
Tissue
<60

Li et al. [82]!
NEAT1↑
2015
China
239 CRC
1.70
1.18–2.45
2-fold
RT-PCR
Tissue
<60

Chen et al. [83]
LINC00152↑
2016
China
97 GC
1.66
1.01-2.73
Median
RT-PCR
Tissue
<60

Chen et al. [19]
FEZF1-AS1↑
2016
China
153 CRC
2.40
1.07–5.41
NR
ISH
Tissue
<100

Han et al. [84]!
H19↑
2016
China
83 CRC
1.43
1.24–1.79
3-fold
RT-PCR
Tissue
<50

Yang et al. [85]
GAPLINC↑
2016
China
180 CRC
2.21
1.38–3.57
NR
ISH
Tissue
<100

Jin et al. [86]
HULC↑
2016
China
54 GC
1.92#
1.00–3.67
2-fold
RT-PCR
Serum
11–32#

Cao et al. [87]!
BC200↑
2016
China
70 EC
2.24
1.12–4.49
Median
RT-PCR
Tissue
<50

Cao et al. [88]
SPRY4-IT1↑
2016
China
84 CRC
3.21
1.55–6.67
2.87-fold
RT-PCR
Tissue
3–36

Gao et al. [89]
linc-UBC1↑
2017
China
96 CRC
2.43
1.09–5.42
Median
RT-PCR
Tissue
<60

Wang et al. [90]!
AFAP1-AS1↑
2016
China
52 CRC
2.36
1.11–5.01
Median
RT-PCR
Tissue
<50

Ge et al. [91]
PCAT-1↑
2013
China
108 CRC
3.12
1.36–7.19
NR
RT-PCR
Tissue
<100

Deng et al. [92]
91H↑
2014
China
72 CRC
3.66
1.66–8.10
2.86-fold
RT-PCR
Tissue
2–36

Sun et al. [93]!
AK098081↑
2016
China
84 CRC
1.90
1.39–2.58
Mean
RT-PCR
Tissue
1–118#

Xu et al. [94]!
FENDRR↓
2014
China
158 GC
1.76
1.04–3.12
Median
RT-PCR
Tissue
20–48

Bian et al. [96]
UCA1↑
2016
China
90 CRC
2.40
1.04-5.50
Median
RT-PCR
Tissue
<100

Zuo et al. [97]
UCA1↑
2017
China
37 GC
2.92
1.07–7.96
Median
RT-PCR
Tissue
<40

Lu et al. [98]
PANDAR↑
2017
China
124 CRC
3.53
1.41–4.45
Median
RT-PCR
Tissue
<60

Lv et al. [99]
MEG3↓
2016
China
96 EC
2.12
1.05–4.27
NR
RT-PCR
Tissue
<120

Xu et al. [100]
TUSC7↓
2017
China
63 CRC
2.92
1.03–8.33
NR
RT-PCR
Tissue
<120

Ma et al. [101]
DUXAP8↑
2016
China
72 GC
2.37#
1.39–4.05
Median
RT-PCR
Tissue
5–36#

Fei et al. [103]!
LINC00982↓
2016
China
106 GC
2.87
1.34–6.17
Median
RT-PCR
Tissue
20–48

Chen et al. [104]!
SNHG15↑
2016
China
106 GC
2.93
1.30–6.58
Median
RT-PCR
Tissue
20–48

Tan et al. [105]
SPRY4-IT1↑
2017
China
106 CRC
2.34
1.14–4.83
Mean
RT-PCR
Tissue
<70

Wang and Xing [106]
ZFAS1↑
2016
China
159 CRC
1.88∗
1.01–3.53
Median
RT-PCR
Tissue
<101

Yao et al. [107]
MALAT-1↑
2016
China
137 EC
1.27#
0.90–1.80
0.5-fold
RT-PCR
Tissue
3–36#

Liu et al. [108]!
BANCR↑
2016
China
142 EC
0.95∗
0.21–0.95
Median
RT-PCR
Tissue
1–60#

Chen et al. [109]
HOTAIR↑
2013
China
78 EC
2.40
1.35–4.28
Mean
RT-PCR
Tissue
2–60

Hu et al. [102]a
Linc00152↑
2016
China
205 EC
1.89
1.22–2.58
Upper 95% CI in control group
RT-PCR
Plasma
<60

POU3F3↑
1.82
1.17–2.51

CFLAR↑
1.68
1.08–2.32

Yu et al. [110]
u50535↑
2018
China
98CRC
4.01
1.06–15.14
NR
RT-PCR
Tissue
<60

Jiang et al. [111]
CRNDE↑
2017
China
251CRC
1.69
1.05–2.74
NR
ISH
Tissue
1–117

Cui et al. [112]
HEIH↑
2018
China
84CRC
1.46
1.02–2.08
Median
RT-PCR
Tissue
<60

Wu et al. [113]!
GHRLOS↓
2017
China
366CRC
1.96
1.34–2.86
1/2-fold
RT-PCR
Tissue
5–85

Li et al. [115]
ZEB1-AS1↑
2017
China
24GC
2.36
1.41–3.96
Median
RT-PCR
Tissue
72

Huang et al. [116]
LINC00673↑
2017
China
73GC
2.38
1.12–5.06
2-fold
RT-PCR
Tissue
<20

Li et al. [117]
PVT1↑
2017
China
104ESCC
2.75
1.35–5.59
Median
RT-PCR
Tissue
<80

Shi et al. [118]
ZFAS1↑
2017
China
246ESCC
1.59
1.07–2.36
Median
RT-PCR
Tissue
114

Wu et al. [119]
XIST↑
2017
China
127ESCC
2.4
1.44–4.01
Median
RT-PCR
Tissue
<80

Ba et al. [120]
LINC00673↑
2017
China
79GC
2.56
1.01–4.54
Median
RT-PCR
Tissue
<50

Zhu et al. [121]
SNHG1↑
2017
China
108CRC
3.17
1.55–6.21
Median
RT-PCR
Tissue
<50

Yang et al. [122]
LINC01133↓
2018
China
149ESCC
2.18
1.23–3.85
Median
RT-PCR
Tissue
<60

aOne study involved lncRNA Linc00152, lncRNA POU3F3, and lncRNA CFLAR. indicates adjusted HR; # indicates calculated HR of OS and follow-up time; ! indicates studies included OS and DFS; ↑ or ↓ indicates upregulated or downregulated with poor prognosis. OS: overall survival; DFS: disease-free survival; HR: hazard ratio; CI: confidence interval; EC: esophageal cancer; GC: gastric cancer; CRC: colorectal cancer; GIC: gastrointestinal cancer; NR: no report; YI: Youden index; RT-PCR: reverse transcription PCR; ISH: in situ hybridization.

Table 2: Characteristics of studies and lncRNAs expression related to DFS in GIC patients.

References
lncRNAs ()
Year
Nations
Number ()
DFS
Cut-off value
Detection methods
Sample types
Follow-up

HR
95% CI

Kong et al. [15]!
PVT1↑
2015
China
80GC
2.22
1.13–4.44
Median
RT-PCR
Tissue
<40

Liu et al. [17]
FEZF1-AS1↑
2017
China
82GC
1.52#
0.88–2.63
2-fold
RT-PCR
Tissue
1–43#

Fan et al. [30]
LINC00261↓
2016
China
138GC
1.81
1.06–3.10
Median
RT-PCR
Tissue
20–48

Xu et al. [34]
PVT1↑
2017
China
190GC
1.75
1.25–2.56
Mean
RT-PCR
Tissue
1–85

Yuan et al. [35]!
PVT1↑
2016
China
111GC
2.21
1.11–4.40
Median
RT-PCR
Tissue
20–48

Zheng et al. [37]!
UCA1↑
2015
China
112GC
2.55
1.33–4.97
Dichotomize
RT-PCR
Tissue
<92

Wang et al. [39]!
CCAT2↑
2016
China
108GC
2.31
1.55–3.42
Median
RT-PCR
Tissue
<70

Yue et al. [42]!
FER1L4↓
2015
China
70CC
4.51
1.99–9.02
Median
RT-PCR
Tissue
<80

Chen et al. [48]!
H19↑
2016
China
128GC
1.29
1.00-1.65
Median
RT-PCR
Tissue
20–48

Zou et al. [49]!
Sox2ot↑
2016
China
155GC
3.84
1.87–7.33
Median
RT-PCR
Tissue
<70

Wang et al. [24]
NR_034119↓
2016
China
107CRC
1.93
1.04–3.61
NR
RT-PCR
Serum
11–74

Wang et al. [52]!
OTUB1-isoform 2↑
2016
China
156GC
1.50
1.02–2.20
Median
RT-PCR
Tissue
<80

Liu et al. [59]!
DANCR↑
2015
China
104CRC
2.40
1.39–7.28
Median
RT-PCR
Tissue
<60

Wang et al. [60]!
ZEB1-AS1↑
2015
China
87EC
2.7
1.38–8.35
Median
RT-PCR
Tissue
<61

Ma et al. [62]!
PANDAR↑
2016
China
100GC
2.36
1.15–4.83
NR
RT-PCR
Tissue
2–36

Peng et al. [65]!
NEAT1↑
2016
China
56CRC
2.39#
1.37–4.19
NR
RT-PCR
Tissue
<60

Zhou et al. [69]!
ROR↑
2016
China
60CC
5.64
1.92–16.58
Median
RT-PCR
Tissue
<80

Yang et al. [70]!
Loc554202↓
2016
China
178CRC
2.75
1.55–7.93
Median
RT-PCR
Tissue
<70

Peng et al. [75]!
SPRY4-IT1↑
2015
China
175GC
1.74
1.32–2.48
Median
RT-PCR
Tissue
<60

Nie et al. [76]!
ZFAS1↑
2016
China
54GC
1.83#
1.07–3.15
Median
RT-PCR
Tissue
3–36#

Xu et al. [79]a
LSINCT5↑
2014
China
71GC
1.08
1.29–3.56
Mean
RT-PCR
Tissue
<72

74CRC
1.30
1.11–3.84
Mean
RT-PCR
Tissue
<72

Sun et al. [80]!
RP11-119F7.4↓
2015
China
96GC
1.16#
0.81–1.65
Median
RT-PCR
Tissue
<100

Zhang et al. [81]!
ANRIL↑
2014
China
120GC
1.72
1.04–2.84
3-fold
RT-PCR
Tissue
<60

Li et al. [82]!
NEAT1↑
2015
China
239CRC
1.80
1.27–2.55
2-fold
RT-PCR
Tissue
<60

Han et al. [84]!
H19↑
2016
China
83CRC
1.52
1.30–1.90
3-fold
RT-PCR
Tissue
<50

Cao et al. [87]!
BC200↑
2016
China
70EC
2.17
1.12–4.19
Median
RT-PCR
Tissue
<50

Wang et al. [90]!
AFAP1-AS1↑
2016
China
52CRC
2.12
1.03-4.35
Median
RT-PCR
Tissue
<50

Sun et al. [93]!
AK098081↑
2016
China
84CRC
1.40#
0.86–2.28
Mean
RT-PCR
Tissue
1–118#

Xu et al. [94]!
FENDRR↓
2014
China
158GC
1.8
1.11–2.91
Median
RT-PCR
Tissue
20–48

Shang et al. [95]
UCA1↑
2016
China
77GC
2.54
1.09–5.92
NR
RT-PCR
Tissue
<60

Fei et al. [103]!
LINC00982↓
2016
China
106GC
2.40
1.19--4.81
Median
RT-PCR
Tissue
20–48

Chen et al. [104]!
SNHG15↑
2016
China
106GC
2.40
1.38–4.18
Median
RT-PCR
Tissue
20–48

Liu et al. [108]!
BANCR↑
2016
China
142EC
3.42#
2.29–5.10
Median
RT-PCR
Tissue
1–60#

Wu et al. [113]!
GHRLOS↓
2017
China
366CRC
2.02
1.42–2.88
1/2-fold
RT-PCR
Tissue
5–85

Yu et al. [114]
linc00261↓
2017
China
80GC
2.57
1.39–4.20
NR
RT-PCR
Tissue
<30

Ba et al. [120]
LINC00673↑
2017
China
79GC
2.94
1.23–4.21
Median
RT-PCR
Tissue
<50

Xu et al. [123]
FOXD2-AS1↑
2018
China
106GC
1.75
1.04–2.97
Median
RT-PCR
Tissue
20–48

aOne study involved GC and CRC. indicates adjusted HR; # indicates calculated HR of DFS and follow-up time; ! indicates studies included OS and DFS; ↑ or ↓ indicates upregulated or downregulated with poor prognosis. OS: overall survival; DFS: disease-free survival; HR: hazard ratio; CI: confidence interval; EC: esophageal cancer; GC: gastric cancer; CRC: colorectal cancer; GIC: gastrointestinal cancer; NR: no report; RT-PCR: reverse transcription PCR.

Table 3: lncRNAs and relevant targets in gastrointestinal cancer.

lncRNAs ()
Poor prognosis
Role
Relevant targets
Function
Reference

SNHG20↑
Upregulated
Oncogene
Cyclin A1, p21
Proliferation/invasion/migration
[29]

PVT1↑
Upregulated
Oncogene
EZH2, p15, p16, FOXM1
Proliferation/metastasis
[15, 34]

FEZF1-AS1↑
Upregulated
Oncogene
LSD1, P21, FEZF1
Proliferation/invasion/migration
[17, 19]

AGAP2-AS1↑
Upregulated
Oncogene
LSD1, EZH2, P21, E-cadherin
Proliferation/migration/invasion
[31]

XIST↑
Upregulated
Oncogene
miR-101, EZH2
Proliferation/migration/invasion/growth/metastasis
[32]

ATB↑
Upregulated
Oncogene
miR-200s, ZEB1, ZEB2
Invasion/EMT
[21]

UCA1↑
Upregulated
Oncogene
Ets-2, Sox4, miR-204, miR-204-5p, TGFβ1
Migration/invasion/proliferation/apoptosis/chemoresistance/EMT
[64, 66, 96, 97]

NEAT1↑
Upregulated
Oncogene
Akt, vimentin, N-cadherin, Zo-1, E-cadherin
Proliferation/apoptosis/EMT/migration/invasion
[65, 73]

CCAT2↑
Upregulated
Oncogene
EZH2, E-cadherin, LATS2
Progression
[39]

CCAT1↑
Upregulated
Oncogene
c-Myc
Proliferation/migration/invasion
[43]

PANDAR↑
Upregulated
Oncogene
N-cadherin, vimentin, β-catenin, Snail, Twist, E-cadherin
EMT/growth/migration/invasion/apoptosis
[98]

H19↑
Upregulated
Oncogene
E-cadherin, Rb-E2F, CDK8, β-catenin, eIF4A3
Migration/invasion/proliferation
[48, 77, 84]

FOXCUT↑
Upregulated
Oncogene
FOXC1 (mRNA)
Proliferation/migration/invasion
[54]

MALAT1↑
Upregulated
Oncogene
EZH2, miR-218
Chemoresistance/EMT
[20]

uc002yug.2↑
Upregulated
Oncogene
RUNX1
Proliferation/migration/invasion
[25]

HOXA11-AS↑
Upregulated
Oncogene
EZH2, LSD1, miR-1297
Growth/migration/invasion/apoptosis
[16]

CCAL↑
Upregulated
Oncogene
AP-2α
Progression/multidrug resistance
[11]

GHET1↑
Upregulated
Oncogene
c-Myc (mRNA)
Proliferation
[18]

ROR↑
Upregulated
Oncogene
miR-145
Proliferation/migration/invasion
[69]

BC032469↑
Upregulated
Oncogene
miR-1207-5p
Proliferation
[71]

BLACAT1↑
Upregulated
Oncogene
EZH2, p15
Proliferation
[72]

GAPLINC↑
Upregulated
Oncogene
miR211-3p, CD44, PSF, NONO, SNAI2
Invasion
[12, 85]

SPRY4-IT1↑
Upregulated
Oncogene
Cyclin D1, MMP2, MMP9, E-cadherin, vimentin
Proliferation/migration/invasion/EMT/metastasis
[75, 88]

ZFAS1↑
Upregulated
Oncogene
EZH2, LSD1, CoREST, KLF2, NKD2
Proliferation
[76]

ANRIL↑
Upregulated
Oncogene
PRC2, miR-99a, miR-449a
Proliferation
[81]

LINC00152↑
Upregulated
Oncogene
EZH2, p15, p21
Proliferation
[83]

DUXAP8↑
Upregulated
Oncogene
EZH2, SUZ12, PLEKHO1
Proliferation/migration
[101]

SNHG15↑
Upregulated
Oncogene
MMP2, MMP9
Proliferation/migration/invasion
[104]

GAS5↓
Downregulated
Suppressor
E2F1, P21
Proliferation
[13]

lnc-GNAT1-1↓
Downregulated
Suppressor
RKIP-NF-κB-Snail
Proliferation/migration/invasion/metastasis
[33]

FER1L4↓
Downregulated
Suppressor
miR-106a-5p
Proliferation/migration/invasion
[42]

MEG3↓
Downregulated
Suppressor
p53
Proliferation/apoptosis
[99]

MIR31HG↓
Downregulated
Suppressor
E2F1, P21
Proliferation
[45]

RP11-766N7.4↓
Downregulated
Suppressor
Vimentin, N-cadherin, E-cadherin
Migration/invasion/EMT
[26]

FENDRR↓
Downregulated
Suppressor
FN1, MMP2, MMP9
Migration/invasion
[94]

TUSC7↓
Downregulated
Suppressor
miR-211-3p
Proliferation
[100]

LINC00982↓
Downregulated
Suppressor
P15, P16
Proliferation
[103]

3.2. Meta-Analysis Findings

Random-effect and fixed-effect models were applied to evaluate the pooled hazard ratio (HR) and its corresponding 95% confidence interval (CI) of OS or DFS based on the heterogeneity level. The pooled HR value (95% CI) of OS which correlated with the expression of lncRNA-UCA1 [37, 64, 66, 96, 97] was 2.42 (1.68–3.49) with low heterogeneity (,  = 0%) and statistically significant () (Figure 2). For all included studies, the pooled HR values (95% CI) of OS related to different lncRNA expressions in EC, GC, and CRC patients were 1.92 (1.70–2.16), 1.96 (1.77–2.16), and 2.10 (1.87–2.36), respectively. And the pooled HR value (95% CI) of OS related to different lncRNA expressions was 2.00 (1.87–2.13) in GIC with moderate heterogeneity (,  = 37%) and statistically significant () (Figure 3). Besides, the pooled HR value (95% CI) of DFS related to different lncRNA expressions was 1.92 (1.73–2.14) in GIC patients with moderate heterogeneity (,  = 41%) and statistically significant () (Figure 4). Furthermore, funnel plots of included studies related to lncRNA-UCA1, OS, and DFS in GIC patients were presented in Figures 5, 6, and 7, respectively. These figures are approximately symmetrical, and we can think that there is no obvious publication bias.

Figure 2: Forest plot showing the pooled HR and corresponding 95% CI of OS related to the expression level of lncRNA UCA1 in gastrointestinal cancer patients. HR: hazard ratio; CI: confidence interval; OS: overall survival.

Figure 3: Forest plot showing the pooled HR (95% CI) of OS related to the expression level of different lncRNAs in gastrointestinal cancer patients. (1.1.1) Specific lncRNA expression in EC (esophageal cancer); (1.1.2) specific lncRNA expression in GC (gastric cancer); (1.1.3) specific lncRNA expression in CRC (colorectal cancer). HR: hazard ratio; CI: confidence interval; OS: overall survival.

Figure 4: Forest plot showing the pooled HR (95% CI) of DFS related to the expression level of different lncRNAs in GIC patients. HR: hazard ratio; CI: confidence interval; DFS: disease-free survival; GIC: gastrointestinal cancer.

Figure 5: Funnel plot of included studies: highly expressed lncRNA UCA1 related to overall survival in gastrointestinal cancer patients.

Figure 6: Funnel plot of included studies: aberrantly expressed lncRNAs related to overall survival in gastrointestinal cancer patients. EC: esophageal cancer; GC: gastric cancer; CRC: colorectal cancer.

Figure 7: Funnel plot of included studies: aberrantly expressed lncRNAs related to disease-free survival in gastrointestinal cancer patients.

4. Discussion

GIC is still a huge threat to human health in spite of ongoing emergence of new anticancer drugs because of chemotherapy resistance and metastasis inducing poor prognosis. In the last decade, more and more studies focused on the clinical roles of lncRNAs and many reports indicated that lncRNA can be a molecular biomarker in gastrointestinal cancer patients for predicting prognosis. However, the prognostic value of lncRNAs that need to be clarified, verified, and summarized was limited by various research centers and small samples.

The purpose of this study was to elucidate the relationship between multiple lncRNA expressions and prognosis of GIC patients. Through big data meta-analysis, we provided evidence to illustrate the prognostic value of aberrantly expressed lncRNAs in GIC patients. The results from this meta-analysis showed that the pooled HR values (95% CI) of OS and DFS related to different lncRNA expressions in GIC patients were 2.00 (1.87–2.13) and 1.92 (1.73–2.14), respectively, which implied that aberrantly expressed lncRNAs may serve as cancer biomarkers in GIC patients. By detecting expression levels of specific lncRNAs in tissue or other body fluids, we cannot only make appropriate clinical decisions based on different prognoses but also monitor the therapeutic efficacy of GIC patients receiving different treatments. In addition, lncRNAs may be used to screen patients at high risk at the early stage based on abnormal expression. Moreover, elevated lncRNA-UCA1 expression promoted tumor cell migration, invasion, EMT, proliferation, and chemoresistance and inhibited its apoptosis by different target genes, which was associated with poor prognosis. For example, Jiao et al. [66] reported that lncRNA-UCA1 as a competing endogenous RNA (ceRNA) of Sox4 enhanced tumor cell proliferation by targeting miR-204 and Sox4 and Bian et al. [96] demonstrated that lncRNA-UCA1 promoted tumor cell proliferation and 5-fluorouracil resistance by functioning as a ceRNA of miR-204-5p. The pooled HR value (95% CI) of OS which correlated with the expression of lncRNA-UCA1 was 2.42 (1.68–3.49) with low heterogeneity (,  = 0%) and statistically significant (). Therefore, lncRNA-UCA1 as a molecular biomarker can be applied in predicting the prognosis of GIC patients. Generally, predicting prognosis of patients and exploring mechanisms of lncRNAs play pivotal roles in clinical decision-making and development of novel targeted gene therapies. Therefore, we summarized the researches involved in mechanisms of lncRNAs; we found that 37 lncRNAs had explicit targets and 11 lncRNAs as ceRNAs regulated cancer progression by sponging corresponding microRNAs. These studies demonstrated that the potential relationship between lncRNAs and microRNAs plays a key role in tumor pathogenesis and promoted carcinogenic study and development of gene therapy. Many studies focusing on the same lncRNA revealed different targets, and the underlying correlation between lncRNAs and microRNAs was still unclear. In the future, we should focus on the interrelationship between lncRNA and microRNA or other types of RNA, in achieving targeted treatment by simultaneous intervention of multiple types of RNA.

Several limitations should not be ignored. First, most of included patients were from East Asia, especially China, which makes our conclusions may just be suitable for Chinese patients. Second, the cut-off values and detection methods in evaluating different lncRNA expressions were various in different included studies, which may lead to heterogeneity between studies. Third, language bias was also one of the limitations, because we only enrolled English papers in the meta-analysis. Fourth, the majority of authors were generally more inclined to report positive results so that the pooled effect values calculated might overestimated the predictive significance of lncRNAs in prognosis of GIC patients; the publication bias have reached a consensus. Fifth, we calculated the HR estimates from the Kaplan-Meier survival curves because of some studies from which we could not extract HR and 95% CI directly. Sixth, the confounding factors in some included studies without the adjusted HR values would lead to high heterogeneity.

In summary, this meta-analysis supports the fact that specific lncRNAs are significantly related to the prognosis of GIC patients and may serve as novel markers for predicting the prognosis in GIC patients. In addition, lncRNAs may have a promising contribution to lncRNA-based targeted therapy and clinical decision-making in the future.

Conflicts of Interest

The authors have declared that they have no conflict of interest.

Authors’ Contributions

Weibiao Kang and Qiang Zheng contributed equally.

Acknowledgments

The authors thank all authors of the included studies. This work was supported by the Natural Science Research Projects at Higher Institutions in Anhui Province (KJ2018ZD017).

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