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

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

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

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; [email protected]

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.

Gastrointestinal (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 , 4972 with gastric cancer, and 5008 with ) 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 . 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 the recovery of patients. However, effective methods to eval- uate prognosis of GIC patients are still lacking nowadays. Gastrointestinal cancers (GICs) are one of the most com- Currently, mounting reports have reported that noncoding mon causes of cancer-related deaths with a high mortality RNA could be used to predict the prognosis of GIC patients, worldwide, which mainly include esophageal, gastric, and For example, microRNAs are potentially eligible for predict- colorectal cancers (EC, GC, and CRC). In addition to aging ing the survival of GIC patients [5]. Many studies indicated and expansion of world population, cancer-causing behav- that long noncoding RNAs (lncRNAs) could competitively iors play a key role in the increasing largely global burden suppress microRNAs by acting as molecular sponges recently of GIC, such as smoking and changes in dietary patterns [6]. Besides, aberrant expression of specific lncRNAs as [1]. There are many therapy strategies applicable to GIC molecular was associated closely with prognosis patients, such as surgery, neoadjuvant chemoradiotherapy, of GIC patients and involved in targeted therapy, which and adjuvant chemoradiotherapy [2], and GIC patients at might promote the development of novel prevention strate- early stage could be curable by receiving suitable treatment gies and advanced therapies [7–12]. with a 90% five-year overall survival, However, five-year lncRNA is a long (more than 200 nucleotides) class overall survivals are still poor for patients with advanced of noncoding RNA that is often expressed in a disease-, tis- stages [3, 4]. Consequently, early diagnosis and selection of sue-, or stage-specific manner [13]. According to recent esti- high-risk individuals with poor prognosis are important in mate, more than 28000 distinct lncRNAs are encoded by 2 Disease Markers

Records identified through database searching (n = 1836)

Records aer duplicates and unrelated studies removed (n = 1362)

Records excluded (n = 280) Reviews and letters (n = 112) Case reports, statements and not clinical Records screened (n = 474) related studies (n = 168)

Full-text articles excluded (n = 83) Not data studies (n = 47) Not human studies (n = 14) Full-text articles assessed Not English studies (n = 6) for eligibility (n = 194) Not prognostic outcomes (n = 16)

Studies included in the meta-analysis (n = 111)

Figure 1: Study flow diagram. and they regulate gene expression by means development and make a clinical decision that is suitable for of different mechanisms, including chromatin modification, the individual quickly. , and posttranscriptional processing, which are becoming attractive therapeutic targets of cancers [14, 15]. 2. Materials and Methods Such upregulated lncRNA HOXA11-AS expression pro- motes tumor proliferation and invasion by scaffolding the 2.1. Search Strategy. To obtain the relevant studies for this chromatin modification factors PRC2, LSD1, and DNMT1 meta-analysis, two authors (Weibiao Kang and Qiang [16]. lncRNA FEZF1-AS1 recruits and bounds to LSD1 to Zheng) searched a wide range of database (PubMed, epigenetically repress downstream gene p21, thereby pro- Web of Science, and Embase) independently up to August moting proliferation [17], and lncRNA GHET1 promotes 27, 2018. Search terms are as follows: “LncRNA”, “Long non- gastric carcinoma cell proliferation by increasing c-Myc coding RNAs”, “lncRNAs”, “lncRNA”, “Long ncRNA”, mRNA stability [18]. Furthermore, lncRNA plays crucial “LincRNAs”, “LINC RNA”, “Long ncRNAs”, “cancer”, roles in the diverse biological processes such as development, “tumor”, “malignancy”, “carcinoma”, “neoplasia”, “neo- differentiation, and carcinogenesis [19]. In addition, lncRNA plasm”, “gastrointestine”, “gastroenteric”, “colon”, “colo- may induce resistance of an anticancer drug. For example, rectal”, “rectum”, “intestinal”, “gastric”, “esophageal”, upregulated lncRNA MALAT1 induces chemoresistance of “esophagus”, “follow up studies”, “prognosis”, “prediction”, CRC cells [20]. “survival”, “hazard ratio”, “incidence”, and “mortality”, Recently, mounting evidences have indicated that vari- which were combined with AND/OR. ous lncRNAs can function as or tumor suppres- sor genes and the dysregulation of lncRNA expression as 2.2. Selection Criteria. All eligible studies were assessed and molecular biomarkers presented promising huge prognostic extracted data by the same two investigators independently values in GIC patients [21–26]. However, the ability of eval- based on the selection criteria. Inclusion criteria are the fol- uating relationship between multiple lncRNA expression lowing: (1) patients who were diagnosed as having gastro- and prognosis of GIC patients was limited due to mono- intestinal cancer by pathologists and did not receive any centric, small samples and various experimental methods preoperative chemotherapy or radiotherapy before obtain- and criteria from different research departments. Therefore, ing samples; (2) predicting prognosis of full stage (I–IV) the purpose of the study was to elaborate the relationship patients on the basis of the expression levels of lncRNAs; between multiple lncRNA expression and prognosis of GIC (3) the expression levels of lncRNAs were divided into high patients so that further understanding of prognostic values and low levels; (4) we could obtain overall survival (OS), of lncRNAs might promote lncRNA-based target therapeutic disease-free survival (DFS), hazard ratio (HR), and 95% Disease Markers 3

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

lncRNAs Number OS Cut-off Detection Sample References Year Nations Follow-up (n = 105) (n = 12178) HR 95% CI value methods types ↓ ∗ 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# 4 Disease Markers

Table 1: Continued.

lncRNAs Number OS Cut-off Detection Sample References Year Nations Follow-up (n = 105) (n = 12178) HR 95% CI value methods types Liu and ↑ ∗ CARLo-5 2017 China 240 GC 2.41 1.13–5.94 0.041 RT-PCR Tissue <60 Shangguan [67] ↑ ∗ 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 ↑ ∗ ZFAS1 2016 China 159 CRC 1.88 1.01–3.53 Median RT-PCR Tissue <101 Xing [106] Disease Markers 5

Table 1: Continued.

lncRNAs Number OS Cut-off Detection Sample References Year Nations Follow-up (n = 105) (n = 12178) HR 95% CI value methods types ↑ 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 ↑ – Linc00152 1.89 1.22 2.58 Upper 95% ↑ Hu et al. [102]a POU3F3 2016 China 205 EC 1.82 1.17–2.51 CI in control RT-PCR Plasma <60 ↑ CFLAR 1.68 1.08–2.32 group ↑ ∗ 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. confidence interval (95% CI) directly from full text or extract random-effect model would be used finally. For some survival data from Kaplan-Meier survival curves. Exclusion studies from which we could not extract HR and corre- criteria are the following: (1) reviews, letters, case reports, sponding 95% CI (OS/DFS) directly, Engauge Digitizer statements, and not clinical related studies were excluded; 4.1 software was applied to obtain the necessary points (2) besides non-English and nonhuman studies, articles lack and the relevant data from Kaplan-Meier survival curves, of data were also excluded; (3) studies focused on lncRNA then HR and corresponding 95% CI were calculated by variants or relationship between lncRNA expression and published methods proposed by Tierney et al. [28]. Addi- prognosis in different histological types of GIC. We resolved tionally, forest plots of the pooled HR values and funnel disagreements by discussing with the third investigator plots used to analyse qualitatively publication bias were (Changjun Yu) and got consensus finally. presented. Furthermore, we also applied sensitivity analysis for this meta-analysis. 2.3. Data Extraction and Quality Assessment. The two authors (Weibiao Kang and Qiang Zheng) extracted data 3. Results independently and got consensus finally. The characteristics collected of individual articles were as follows: author, year 3.1. Study Identification and Characteristics. According to the of publication, nation of population enrolled, number of selection criteria, a total of 111 articles (21 EC, 47 GC, and 44 patients, HR and 95% CI (OS/DFS), cut-off value, method, CRC; one study involved GC and CRC) involving 13103 sample type, and follow-up. We assessed the quality of each patients (3123 with EC, 4972 with GC, and 5008 with CRC) study by using the guidelines for meta-analysis of observa- were identified and included in the meta-analysis; specific tion studies in epidemiology (MOOSE) [27]. steps were showed in Figure 1 [10–13, 15–26, 29–123]. Most of the studies taken into account refer to Asian popula- 2.4. Statistical Analysis. Statistical analysis was conducted tion, especially china. Cut-off values of high or low lncRNA by Review Manager 5.2 (provided by Cochrane collabora- expression were mostly median or mean. Detection methods tion). P <001 was considered statistically significant. The of lncRNA expression were mainly RT-PCR (reverse tran- 2 heterogeneity among studies was calculated by Q and I tests. scription PCR) or ISH (in situ hybridization). Sample types P >010 in combination with I2 < 50% indicated low het- were almost from tissues. As for clinical outcome indicators, erogeneity; fixed-effect models should be used. Otherwise, 74 studies [10–13, 16, 18–23, 25, 26, 29, 31–33, 36, 38, 40, 41, 6 Disease Markers

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

Detection Sample lncRNAs Number DFS Cut-off Follow-up References Year Nations methods types (n =37) (n = 4360) value 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# 1.08∗ – < a ↑ 71GC 1.29 3.56 Mean RT-PCR Tissue 72 Xu et al. [79] LSINCT5 2014 China ∗ 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.

43–47, 50, 51, 53–58, 61, 63, 64, 66–68, 71–74, 77, 78, 83, 85, 34, 79, 95, 114, 123] included disease-free survival (DFS), 86, 88, 89, 91, 92, 96–102, 105–107, 109–112, 115–119, 121, and another 29 studies [15, 35, 37, 39, 42, 48, 49, 52, 59, 60, 122] included overall survival (OS), 8 studies [17, 24, 30, 62, 65, 69, 70, 75, 76, 80–82, 84, 87, 90, 93, 94, 103, 104, Disease Markers 7

Table 3: lncRNAs and relevant targets in gastrointestinal cancer.

lncRNAs Poor prognosis Role Relevant targets Function Reference (n =37) ↑ SNHG20 Upregulated 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] ↑ Proliferation/migration/invasion/ XIST Upregulated Oncogene miR-101, EZH2 [32] growth/metastasis ↑ ATB Upregulated Oncogene miR-200s, ZEB1, ZEB2 Invasion/EMT [21] ↑ Ets-2, Sox4, miR-204, miR-204-5p, Migration/invasion/proliferation/ [64, 66, UCA1 Upregulated Oncogene TGFβ1 apoptosis/chemoresistance/EMT 96, 97] ↑ Akt, vimentin, N-cadherin, Proliferation/apoptosis/EMT/ NEAT1 Upregulated Oncogene [65, 73] Zo-1, E-cadherin migration/invasion ↑ CCAT2 Upregulated Oncogene EZH2, E-cadherin, LATS2 Progression [39] ↑ CCAT1 Upregulated Oncogene c-Myc Proliferation/migration/invasion [43] ↑ N-cadherin, vimentin, β-catenin, EMT/growth/migration/invasion/ PANDAR Upregulated Oncogene [98] Snail, Twist, E-cadherin apoptosis ↑ E-cadherin, Rb-E2F, CDK8, [48, 77, H19 Upregulated Oncogene Migration/invasion/proliferation β-catenin, eIF4A3 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] ↑ miR211-3p, CD44, PSF, GAPLINC Upregulated Oncogene Invasion [12, 85] NONO, SNAI2 ↑ Cyclin D1, MMP2, MMP9, Proliferation/migration/invasion/ SPRY4-IT1 Upregulated Oncogene [75, 88] E-cadherin, vimentin EMT/metastasis ↑ 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] ↓ Proliferation/migration/invasion/ lnc-GNAT1-1 Downregulated Suppressor RKIP-NF-κB-Snail [33] metastasis ↓ 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]

108, 113, 120] included both OS and DFS. We have identified lncRNA expression and 16 significantly downregulated 74 lncRNAs which were associated closely with poor progno- lncRNA expression (Tables 1 and 2). Moreover, 47 of the sis of GIC patients, including 58 significantly upregulated included studies revealed relative mechanisms, and 12 of 8 Disease Markers

Hazard ratio Hazard ratio Study or subgroup Log[hazard ratio] SE Weight IV, fixed, 95% CI IV, fixed, 95% CI Jiao 2016/UCA1 ↑ 0.80648 0.33145 31.6% 2.24 [1.17, 4.29] Zheng 2015/UCA1 ↑ 0.85442 0.3341 31.1% 2.35 [1.22, 4.52] Zuo 2017/UCA1 ↑ 1.07158 0.51193 13.2% 2.92 [1.07, 7.96] Ni 2015/UCA1 ↑ 1.13462 0.84804 4.8% 3.11 [0.59, 16.39] Bian 2016/UCA1 ↑ 0.87547 0.42488 19.2% 2.40 [1.04, 5.52]

Total (95% CI) 100.0% 2.42 [1.68, 3.49] Heterogeneity: Chi2 = 0.28, df = 4 (P = 0.99); I2 = 0% 0.05 0.2 1 5 20 Test for overall effect: Z = 4.75 (P < 0.00001) Better OS Worse OS

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. them investigated the correlation between lncRNAs and expressed lncRNAs in GIC patients. The results from this microRNAs (Table 3). meta-analysis showed that the pooled HR values (95% CI) of OS and DFS related to different lncRNA expres- 3.2. Meta-Analysis Findings. Random-effect and fixed-effect sions in GIC patients were 2.00 (1.87–2.13) and 1.92 (1.73– models were applied to evaluate the pooled hazard ratio 2.14), respectively, which implied that aberrantly expressed (HR) and its corresponding 95% confidence interval (CI) of lncRNAs may serve as cancer biomarkers in GIC patients. OS or DFS based on the heterogeneity level. The pooled HR By detecting expression levels of specific lncRNAs in tissue value (95% CI) of OS which correlated with the expression or other body fluids, we cannot only make appropriate clin- of lncRNA-UCA1 [37, 64, 66, 96, 97] was 2.42 (1.68–3.49) ical decisions based on different prognoses but also monitor 2 with low heterogeneity (P =099, I = 0%) and statistically the therapeutic efficacy of GIC patients receiving different significant (P <000001) (Figure 2). For all included studies, treatments. In addition, lncRNAs may be used to screen the pooled HR values (95% CI) of OS related to different patients at high risk at the early stage based on abnormal lncRNA expressions in EC, GC, and CRC patients were expression. Moreover, elevated lncRNA-UCA1 expression 1.92 (1.70–2.16), 1.96 (1.77–2.16), and 2.10 (1.87–2.36), promoted tumor cell migration, invasion, EMT, prolifera- respectively. And the pooled HR value (95% CI) of OS related tion, and chemoresistance and inhibited its apoptosis by to different lncRNA expressions was 2.00 (1.87–2.13) in GIC different target genes, which was associated with poor with moderate heterogeneity (P =00001, I2 = 37%) and prognosis. For example, Jiao et al. [66] reported that statistically significant (P <000001) (Figure 3). Besides, the lncRNA-UCA1 as a competing endogenous RNA (ceRNA) pooled HR value (95% CI) of DFS related to different of Sox4 enhanced tumor cell proliferation by targeting lncRNA expressions was 1.92 (1.73–2.14) in GIC patients miR-204 and Sox4 and Bian et al. [96] demonstrated that with moderate heterogeneity (P =0006, I2 = 41%) and statis- lncRNA-UCA1 promoted tumor cell proliferation and 5- tically significant (P <000001) (Figure 4). Furthermore, fluorouracil resistance by functioning as a ceRNA of funnel plots of included studies related to lncRNA- miR-204-5p. The pooled HR value (95% CI) of OS which UCA1, OS, and DFS in GIC patients were presented in correlated with the expression of lncRNA-UCA1 was 2.42 2 Figures 5, 6, and 7, respectively. These figures are approx- (1.68–3.49) with low heterogeneity (P =099, I = 0%) and imately symmetrical, and we can think that there is no statistically significant (P <000001). Therefore, lncRNA- obvious publication bias. UCA1 as a molecular can be applied in predicting the prognosis of GIC patients. Generally, predicting progno- 4. Discussion sis of patients and exploring mechanisms of lncRNAs play pivotal roles in clinical decision-making and development GIC is still a huge threat to human health in spite of ongo- of novel targeted gene therapies. Therefore, we summarized ing emergence of new anticancer drugs because of chemo- the researches involved in mechanisms of lncRNAs; we therapy resistance and metastasis inducing poor prognosis. found that 37 lncRNAs had explicit targets and 11 lncRNAs In the last decade, more and more studies focused on the as ceRNAs regulated cancer progression by sponging corre- clinical roles of lncRNAs and many reports indicated that sponding microRNAs. These studies demonstrated that the lncRNA can be a molecular biomarker in gastrointestinal potential relationship between lncRNAs and microRNAs cancer patients for predicting prognosis. However, the prog- plays a key role in tumor pathogenesis and promoted carci- nostic value of lncRNAs that need to be clarified, verified, and nogenic study and development of gene therapy. Many stud- summarized was limited by various research centers and ies focusing on the same lncRNA revealed different targets, small samples. and the underlying correlation between lncRNAs and micro- The purpose of this study was to elucidate the relation- RNAs was still unclear. In the future, we should focus on the ship between multiple lncRNA expressions and prognosis interrelationship between lncRNA and microRNA or other of GIC patients. Through big data meta-analysis, we pro- types of RNA, in achieving targeted treatment by simulta- vided evidence to illustrate the prognostic value of aberrantly neous intervention of multiple types of RNA. Disease Markers 9

Hazard ratio Hazard ratio Study or subgroup Log[hazard ratio] SE Weight IV, random, 95% CI IV, random, 95% CI 1.1.1 EC Chen 2015/NEAT1 ↑ 0.65233 0.39127 0.6% 1.92 [0.89, 4.13] Li 2013/HOTAIR ↑ 0.6471 0.33878 0.8% 1.91 [0.89, 3.71] Jiang 2016/TUG1 ↑ 0.33647 0.16783 1.8% 1.40 [1.01, 1.95] Pan 2014/FOXCUT ↑ 0.75612 0.22163 1.3% 2.13 [1.38, 3.29] Wang 2015/ZEB1-AS1 ↑ 0.86289 0.39916 0.6% 2.37 [1.08, 5.18] Wu 2014/uc002yug.2 ↑ 0.95935 0.23578 1.2% 2.61 [1.64, 4.14] Jiao 2016/UCA1 ↑ 0.80648 0.33145 0.8% 2.24 [1.17, 4.29] Yao 2017/RP11-766N7.4 ↓ 0.76081 0.33872 0.8% 2.14 [1.10, 4.16] Xie 2014/SPRY4-IT1 ↑ 0.71784 0.34555 0.7% 2.05 [1.04, 4.04] Zhou 2016/AFAP1-AS1 ↑ 0.63658 0.22264 1.3% 1.89 [1.22, 2.92] Zhao 2016/BC200 ↑ 0.80648 0.35422 0.7% 2.24 [1.12, 4.48] Lv2016/MEG3 ↓ 0.75142 0.35786 0.7% 2.12 [1.05, 4.28] Yao 2016/MALAT1 ↑ 0.23902 0.17682 0.7% 1.27 [0.90, 1.80] Chen 2013/HOTAIR ↑ 0.87547 0.29435 0.9% 2.40 [1.35, 4.27] Hu 2016/Linc00152 ↑ 0.63658 0.19106 1.6% 1.89 [1.30, 2.75] Hu 2016/POU3F3 ↑ 0.59884 0.19471 1.6% 1.82 [1.24, 2.67] Hu 2016/CFLAR ↑ 0.51879 0.19505 1.6% 1.68 [1.15, 2.46] Liu 2016/BANCR ↑ −0.0513 0.38504 0.6% 0.95 [0.45, 2.02] Huang 2016/MALAT1 ↑ 1.89311 0.41401 0.5% 6.64 [2.95, 14.95] Li 2017/PVT1 ↑ 1.0116 0.36247 0.7% 2.75 [1.35, 5.60] Shi 2017/ZFAS1 ↑ 0.46373 0.20179 1.5% 1.59 [1.07, 2.36] Wu 2017/XIST ↑ 0.87547 0.26126 1.1% 2.40 [1.44, 4.00] Yang 2018/LINC01133 ↓ 0.77932 0.29109 0.9% 2.18 [1.23, 3.86] Subtotal (95% CI) 24.0% 1.92 [1.70, 2.16] Heterogeneity: Tau2 = 0.02; Chi2 = 27.85, df = 22 (P = 0.18); I2 = 21% Test for overall effect: Z = 10.52 (P < 0.00001)

1.1.2 GC Sun 2014/GAS5 ↓ 0.88789 0.32379 0.8% 2.43 [1.29, 4.58] Kong 2015/PVT1 ↑ 0.73716 0.34269 0.7% 2.09 [1.07, 4.09] Qi 2017/AGAP2-AS1 ↑ 0.98208 0.31219 0.9% 2.67 [1.45, 4.92] Chen 2016/XIST ↑ 1.13462 0.20839 1.4% 3.11 [2.07, 4.68] Saito 2015/ATB ↑ 1.25276 0.37213 0.7% 3.50 [1.69, 7.26] Yuan 2016/PVT1 ↑ 0.82418 0.39453 0.6% 2.28 [1.05, 4.94] Zheng 2015/UCA1 ↑ 0.85442 0.3341 0.8% 2.35 [1.22, 4.52] Wang 2016/CCAT2 ↑ 0.74669 0.2037 1.5% 2.11 [1.42, 3.15] Zhao 2015/HOTAIR ↑ 0.38526 0.17436 1.7% 1.47 [1.04, 2.07] Zhang 2016/Sox2ot ↑ 0.71784 0.2416 1.2% 2.05 [1.28, 3.29] Chen 2015/HIF1A-AS2 ↑ 0.54232 0.27683 1.0% 1.72 [1.00, 2.96] Park 2013/BM742401 ↓ 0.02956 0.30444 0.9% 1.03 [0.57, 1.87] Chen 2016/H19 ↑ 0.67294 0.3595 0.7% 1.96 [0.97, 3.97] Zou 2016/Sox2ot ↑ 1.17557 0.41986 0.5% 3.24 [1.42, 7.38] Wang2016/OTUB1-isoform 2 ↑ 0.43178 0.19912 1.5% 1.54 [1.04, 2.28] Zhou 2014/LET ↓ 0.82418 0.35266 0.7% 2.28 [1.14, 4.55] Hu 2015/linc-UBC1 ↑ 1.26976 0.37338 0.6% 3.56 [1.71, 7.40] Wang 2015/CCAT2 ↑ 0.87963 0.38677 0.6% 2.41 [1.13, 5.14] Li 2015/BANCR ↑ 0.41211 0.19705 1.5% 1.51 [1.03, 2.22] Ma 2016/PANDAR ↑ 1.30291 0.604 0.3% 3.68 [1.13, 12.02] Sun 2016/HOXA11-AS ↑ 1.04732 0.27819 1.0% 2.85 [1.65, 4.92] Liu 2017/CARLo-5 ↑ 0.87963 0.42334 0.5% 2.41 [1.05, 5.53] Yang 2014/GHET1 ↑ 0.64185 0.65284 0.2% 1.90 [0.53, 6.83] Lu 2016/BC032469 ↑ 1.02245 0.54641 0.3% 2.78 [0.95, 8.11] Hu 2014/GAPLINC ↑ 0.43178 0.11833 0.3% 1.54 [1.22, 1.94] Fu 2016/NEAT1 ↑ 0.47623 0.22925 1.3% 1.61 [1.03, 2.52] Peng 2015/SPRY4-IT1 ↑ −0.1985 0.41384 0.5% 0.82 [0.36, 1.85] Nie 2016/ZFAS1 ↑ 0.73237 0.32252 0.8% 2.08 [1.11, 3.91] Sun 2015/RP11-119F7.4 ↓ 0.18232 0.18134 1.7% 1.20 [0.84, 1.71] Zhang 2014/ANRIL ↑ 0.55389 0.26423 1.1% 1.74 [1.04, 2.92] Chen 2016/LINC00152 ↑ 0.50682 0.25366 1.1% 1.66 [1.01, 2.73] Jin 2016/HULC ↑ 0.65233 0.33168 0.8% 1.92 [1.00, 3.68] Xu 2014/FENDRR ↓ 0.56531 0.28026 1.0% 1.76 [1.02, 3.05] Zuo 2017/UCA1 ↑ 1.07158 0.51193 0.4% 2.92 [1.07, 7.96] Ma 2016/DUXAP8 ↑ 0.86289 0.27281 1.0% 2.37 [1.39, 4.05] Fei 2016/LINC00982 ↓ 1.05431 0.38955 0.6% 2.87 [1.34, 6.16] Chen 2016/SNHG15 ↑ 1.075 0.41369 0.5% 2.93 [1.30, 6.59] Li 2017/ZEB1-AS1 ↑ 0.85866 0.26343 1.1% 2.36 [1.41, 3.95] Huang 2017/LINC00673 ↑ 0.8671 0.3847 0.6% 2.38 [1.12, 5.06] Ba 2017/LINC00673 ↑ 0.94001 0.38341 0.6% 2.56 [1.21, 5.43] Subtotal (95% CI) 36.4% 1.96 [1.77, 2.16] Heterogeneity: Tau2 = 0.02; Chi2 = 49.11, df = 39 (P = 0.13); I2 = 21% Test for overall effect: Z = 13.09 (P < 0.00001)

1.1.3 CRC Li 2016/SNHG20 ↑ 1.08856 0.34418 0.7% 2.97 [1.51, 5.83] Ye 2016/Inc-GNAT1-1 ↓ 0.77011 0.38842 0.6% 2.16 [1.01, 4.62] Ye 2015/CLMAT3 ↑ 0.71784 0.31759 0.8% 2.05 [1.10, 3.82] He 2014/CCAT1 ↑ 0.73716 0.19586 1.5% 2.09 [1.42, 3.07] Yin 2015/MEG3 ↓ −2.0402 0.9954 0.1% 0.13 [0.02, 0.91] Nie 2016/MIR31HG ↓ 0.85442 0.36397 0.7% 2.35 [1.51, 4.80] Liu 2016/CRNDE-h ↑ 0.87129 0.31045 0.9% 2.39 [1.30, 4.39] Li 2017/PANDAR ↑ 1.12493 0.57142 0.3% 3.08 [1.00, 9.44] Miroslav 2014/HOTAIR ↑ 1.77495 0.75747 0.2% 5.90 [1.34, 26.04] Guo 2015/FTX ↑ 0.86289 0.16768 1.8% 2.37 [1.71, 3.29] Ren 2015/HOTTIP ↑ 0.76547 0.2448 1.2% 2.15 [1.33, 3.47] Liu 2015/DANCR ↑ 0.75612 0.46072 0.5% 2.13 [0.86, 5.25] Ni 2015/UCA1 ↑ 1.13462 0.84804 0.1% 3.11 [0.59, 16.39] Peng 2016/NEAT1 ↑ 0.53063 0.25265 1.1% 1.70 [1.04, 2.79] Ma 2016/CCAL ↑ 0.81093 0.25994 1.1% 2.25 [1.35, 3.74] Wu 2014/HOTAIR ↑ 1.36609 0.59151 0.3% 3.92 [1.23, 12.50] Zhou 2016/ROR ↑ 1.97685 0.50263 0.4% 7.22 [2.70, 19.34] Yang 2016/Loc554202 ↓ 0.89609 0.44738 0.5% 2.45 [1.02, 5.89] Su 2017/BLACAT1 ↑ 0.40547 0.06454 2.9% 1.50 [1.32, 1.70] Ohtsuka 2016/H19 ↑ 0.24686 0.0838 2.7% 1.28 [1.09, 1.51] Li 2017/MALAT1 ↑ 0.77473 0.25238 1.1% 2.17 [1.32, 3.56] Li 2015/NEAT1 ↑ 0.53063 0.18637 1.6% 1.70 [1.18, 2.45] Chen 2016/FEZF1-AS1 ↑ 0.87547 0.41342 0.5% 2.40 [1.07, 5.40] Han 2016/H19 ↑ 0.35767 0.09365 2.6% 1.43 [1.19, 1.72] Yang 2016/GAPLINC ↑ 0.79299 0.24247 1.2% 2.21 [1.37, 3.55] Cao 2016/SPRY4-IT1 ↑ 1.16627 0.37229 0.7% 3.21 [1.55, 6.66] Cao 2017/linc-UBC1 ↑ 0.88789 0.40916 0.6% 2.43 [1.09, 5.42] Wang 2016/AFAP1-AS1 ↑ 0.85866 0.38446 0.6% 2.36 [1.11, 5.01] Ge 2013/PCAT-1 ↑ 1.13783 0.4248 0.5% 3.12 [1.36, 7.17] Deng 2014/91H ↑ 1.29746 0.40435 0.6% 3.66 [1.66, 8.08] Sun 2016/AK098081 ↑ 0.64185 0.15778 1.9% 1.90 [1.39, 2.59] Bian 2016/UCA1 ↑ 0.87547 0.42488 0.5% 2.40 [1.04, 5.52] Lu 2017/PANDAR ↑ 1.2613 0.29319 0.9% 3.53 [1.99, 6.27] Xu 2017/TUSC7 ↓ 1.07158 0.53324 0.4% 2.97 [1.03, 8.30] Tan 2017/SPRY4-IT1 ↑ 0.85015 0.36832 0.7% 2.34 [1.14, 4.82] Wang 2016/ZFAS1 ↑ 0.63127 0.31922 0.8% 1.88 [1.01, 3.51] Yue 2015/FER1L4 ↓ 1.38379 0.42998 0.5% 3.99 [1.72, 9.27] Yu 2018/u50535 ↑ 1.38879 0.67833 0.2% 4.01 [1.06, 15.15] Jiang 2017/CRNDE ↑ 0.52473 0.24469 1.2% 1.69 [1.05, 2.73] Cui 2018/HEIH ↑ 0.37844 0.18178 1.7% 1.46 [1.02, 2.08] Wu 2017/GHRLOS ↓ 0.67294 0.19341 1.6% 1.96 [1.34, 2.86] Zhu 2017/SNHG1 ↑ 1.15373 0.35406 0.7% 3.17 [1.58, 6.35] Subtotal (95% CI) 39.6% 2.10 [1.87, 2.36] Heterogeneity: Tau2 = 0.05; Chi2 = 84.78, df = 41 (P < 0.0001); I2 = 52% Test for overall effect: Z = 12.61 (P < 0.00001)

Total (95% CI) 100.% 2.00 [1.87, 2.13] 2 2 2 Heterogeneity: Tau = 0.03; Chi = 164.84, df = 104 (P < 0.0001); I = 37% 0.02 0.1 1 10 50 Z P Test for overall effect: = 20.61 ( < 0.00001) Better OS Worse OS Test for subgroup differences: Chi2 = 1.32, df = 2 (P = 0.52); I2 = 0%

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. 10 Disease Markers

Hazard ratio Hazard ratio Study of subgroup Log[hazard ratio] SE Weight IV, random, 95% CI IV, random, 95% CI Wang 2015/ZEB1-AS1 ↑ 0.99325 0.45923 1.2% 2.70 [1.10, 6.64] Zhao 2016/BC200 ↑ 0.77473 0.33657 1.9% 2.17 [1.12, 4.20] Kong 2015/PVT1 ↑ 0.79751 0.34909 1.8% 2.22 [1.12, 4.40] Liu 2017/FEZF1-AS1 ↑ 0.41871 0.27929 2.5% 1.52 [0.88, 2.63] Fan 2016/LINC00261 ↓ 0.41871 0.27376 2.6% 1.52 [0.89, 2.60] Xu 2017/PVT1 ↑ 0.55962 0.18287 4.0% 1.75 [1.22, 2.50] Yuan 2016/PVT1 ↑ 0.79299 0.35134 1.8% 2.21 [1.11, 4.40] Zheng 2015/UCA1 ↑ 0.93609 0.33629 1.9% 2.55 [1.32, 4.93] Wang 2016/CCAT2 ↑ 0.83725 0.20188 3.6% 2.31 [1.56, 3.43] Chen 2016/H19 ↑ 0.25464 0.12775 5.2% 1.29 [1.00, 1.66] Zou 2016/Sox2ot ↑ 1.34547 0.34848 1.8% 3.84 [1.94, 7.60] Wang2016/OTUB1-isoform 2 ↑ 0.40547 0.19609 3.8% 1.50 [1.02, 2.20] Ma 2016/PANDAR ↑ 0.85866 0.36609 1.7% 2.36 [1.15, 4.84] Peng 2015/SPRY4-IT1 ↑ 0.55389 0.16087 4.5% 1.74 [1.27, 2.38] Nie 2016/ZFAS1 ↑ 0.60432 0.27544 2.5% 1.83 [1.07, 3.14] Xu 2014/LSINCT5 ↑ 0.07696 0.25896 2.7% 1.08 [0.65,1.79] Sun 2015/RP11-119F7.4 ↓ 0.14842 0.1815 4.0% 1.16 [0.81, 1.66] Zhang 2014/ANRIL ↑ 0.54232 0.25627 2.8% 1.72 [1.04, 2.84] Xu 2014/FENDRR ↓ 0.58779 0.24587 2.9% 1.80 [1.11, 2.91] Shang 2015/UCA1 ↑ 0.93216 0.43167 1.3% 2.54 [1.09, 5.92] Fei 2016/LINC00982 ↓ 0.87547 0.35631 1.8% 2.40 [1.19, 4.83] Chen 2016/SNHG15 ↑ 0.87547 0.28271 2.4% 2.40 [1.38, 4.18] Wang 2016/NR_034119 ↓ 0.65752 0.31747 2.1% 1.93 [1.04, 3.60] Liu 2015/DANCR ↑ 0.87547 0.4224 1.3% 2.40 [1.05, 5.49] Peng 2016/NEAT1 ↑ 0.87129 0.28518 2.4% 2.39 [1.37, 4.18] Zhou 2016/ROR ↑ 1.72988 0.54997 0.9% 6.64 [1.92, 16.57] Yang 2016/Loc554202 ↓ 1.0116 0.41643 1.4% 2.75 [1.22, 6.22] Xu 2014/LSINCT5 ↑ 0.26236 0.31661 2.1% 1.30 [0.70, 2.42] Li 2015/NEAT1 ↑ 0.58779 0.17783 4.1% 1.80 [1.27, 2.55] Han 2016/H19 ↑ 0.41871 0.09681 6.0% 1.52 [1.26, 1.84] Wang 2016/AFAP1-AS1 ↑ 0.75142 0.3675 1.7% 2.12 [1.03, 4.36] Sun 2016 /AK098081 ↑ 0.33647 0.24872 2.9% 1.40 [0.86, 2.28] Yue 2015/FER1L4 ↓ 1.5063 0.38554 1.6% 4.51 [2.12, 9.60] Liu 2016/BANCR ↑ 1.22964 0.20426 3.6% 3.42 [2.29, 5.10] Yu 2017/linc00261 ↓ 0.94391 0.28209 2.5% 2.57 [1.48, 4.47] Ba 2017/LINC00673 ↑ 1.07841 0.31389 2.1% 2.94 [1.59, 5.44] Xu 2018/FOXD2-AS1 ↑ 0.55962 0.26769 2.6% 1.75 [1.04, 2.96] Wu 2017/GHRLOS ↓ 0.7031 0.18039 4.1% 2.02 [1.42, 2.88]

Total (95% CI) 100.0% 1.92 [1.73, 2.14] Heterogeneity: Tau2 = 0.04; Chi2 = 62.44, df = 37 (P = 0.006); I2 = 41% Test for overall effect Z = 12.08 (P < 0.00001) 0.05 20510.2 Better DFS Worse DFS

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.

Several limitations should not be ignored. First, most of the publication bias have reached a consensus. Fifth, we cal- included patients were from East Asia, especially China, culated the HR estimates from the Kaplan-Meier survival which makes our conclusions may just be suitable for Chi- curves because of some studies from which we could not nese patients. Second, the cut-off values and detection extract HR and 95% CI directly. Sixth, the confounding fac- methods in evaluating different lncRNA expressions were tors in some included studies without the adjusted HR values various in different included studies, which may lead to het- would lead to high heterogeneity. erogeneity between studies. Third, language bias was also one In summary, this meta-analysis supports the fact that of the limitations, because we only enrolled English papers in specific lncRNAs are significantly related to the prognosis the meta-analysis. Fourth, the majority of authors were gen- of GIC patients and may serve as novel markers for predict- erally more inclined to report positive results so that the ing the prognosis in GIC patients. In addition, lncRNAs may pooled effect values calculated might overestimated the pre- have a promising contribution to lncRNA-based targeted dictive significance of lncRNAs in prognosis of GIC patients; therapy and clinical decision-making in the future. Disease Markers 11

0 SE(log[hazard ratio]) Conflicts of Interest The authors have declared that they have no conflict of 0.2 interest.

0.4 Authors’ Contributions

0.6 Weibiao Kang and Qiang Zheng contributed equally.

0.8 Acknowledgments

Hazard ratio The authors thank all authors of the included studies. This 1 0.05 0.2 1 5 20 work was supported by the Natural Science Research Projects at Higher Institutions in Anhui Province (KJ2018ZD017). Figure 5: Funnel plot of included studies: highly expressed lncRNA UCA1 related to overall survival in gastrointestinal cancer patients. References

0 SE(log[hazard ratio]) [1] A. Jemal, F. Bray, M. M. Center, J. Ferlay, E. Ward, and D. Forman, “Global Cancer statistics,” CA: a Cancer Journal for Clinicians, vol. 61, no. 2, pp. 69–90, 2011. 0.2 [2] Japanese Gastric Cancer Association, “Japanese gastric can- cer treatment guidelines 2014 (ver. 4),” Gastric Cancer, 0.4 vol. 20, no. 1, pp. 1–19, 2017. [3] H. Suzuki, T. Gotoda, M. Sasako, and D. Saito, “Detection of 0.6 early gastric cancer: misunderstanding the role of mass screening,” Gastric Cancer, vol. 9, no. 4, pp. 315–319, 2006. 0.8 [4] E. G. McFarland, B. Levin, D. A. Lieberman et al., “Revised colorectal screening guidelines: joint effort of the American Hazard ratio Cancer Society, U.S. Multisociety Task Force on Colorectal 1 ” 0.02 0.1 1 10 50 Cancer, and American College of Radiology, Radiology, vol. 248, no. 3, pp. 717–720, 2008. Subgroups [5] Q. Zheng, C. Chen, H. Guan, W. Kang, and C. Yu, “Prognos- EC tic role of microRNAs in human gastrointestinal cancer: a GC systematic review and meta-analysis,” Oncotarget, vol. 8, CRC no. 28, pp. 46611–46623, 2017. “ Figure 6: Funnel plot of included studies: aberrantly expressed [6] M. Cesana, D. Cacchiarelli, I. Legnini et al., A long noncod- ing RNA controls muscle differentiation by functioning as a lncRNAs related to overall survival in gastrointestinal cancer ” – patients. EC: esophageal cancer; GC: gastric cancer; CRC: competing endogenous RNA, Cell, vol. 147, no. 2, pp. 358 colorectal cancer. 369, 2011. [7] C. Wahlestedt, “Targeting long non-coding RNA to thera- peutically upregulate gene expression,” Nature Reviews Drug – 0 SE(log[hazard ratio]) Discovery, vol. 12, no. 6, pp. 433 446, 2013. [8] X. Zhou, C. Yin, Y. Dang, F. Ye, and G. Zhang, “Identification of the long non-coding RNA H19 in plasma as a novel bio- 0.2 marker for diagnosis of gastric cancer,” Scientific Reports, vol. 5, no. 1, article 11516, 2015. 0.4 [9] P. Qi and X. Du, “The long non-coding RNAs, a new cancer diagnostic and therapeutic gold mine,” Modern Pathology, vol. 26, no. 2, pp. 155–165, 2013. 0.6 [10] X. Li, Z. Wu, Q. Mei et al., “Long non-coding RNA HOTAIR, a driver of malignancy, predicts negative prognosis and 0.8 exhibits oncogenic activity in oesophageal squamous cell car- cinoma,” British Journal of Cancer, vol. 109, no. 8, pp. 2266– Hazard ratio 2278, 2013. 1 0.05 0.2 1 5 20 [11] Y. Ma, Y. Yang, F. Wang et al., “Long non-coding RNA CCAL regulates colorectal cancer progression by activating Figure 7: Funnel plot of included studies: aberrantly expressed Wnt/β-catenin signalling pathway via suppression of activa- lncRNAs related to disease-free survival in gastrointestinal cancer tor protein 2Α,” Gut, vol. 65, no. 9, pp. 1494–1504, 2016. patients. [12] Y. Hu, J. Wang, J. Qian et al., “Long noncoding RNA GAPLINC regulates CD44-dependent cell invasiveness and 12 Disease Markers

associates with poor prognosis of gastric cancer,” Cancer [27] D. F. Stroup, J. A. Berlin, S. C. Morton et al., “Meta-analysis of Research, vol. 74, no. 23, pp. 6890–6902, 2014. observational studies in epidemiology: a proposal for report- [13] M. Sun, F. Y. Jin, R. Xia et al., “Decreased expression of long ing. Meta-analysis of observational studies in epidemiology ” noncoding RNA GAS5 indicates a poor prognosis and pro- (MOOSE) group, Journal of the American Medical Associa- – motes cell proliferation in gastric cancer,” BMC Cancer, tion, vol. 283, no. 15, pp. 2008 2012, 2000. vol. 14, no. 1, 2014. [28] J. F. Tierney, L. A. Stewart, D. Ghersi, S. Burdett, and [14] M. Huarte, “The emerging role of lncRNAs in cancer,” M. R. Sydes, “Practical methods for incorporating summary Nature Medicine, vol. 21, no. 11, pp. 1253–1261, 2015. time-to-event data into meta-analysis,” Trials, vol. 8, no. 1, [15] R. Kong, E. B. Zhang, D. D. Yin et al., “Long noncoding RNA 2007. PVT1 indicates a poor prognosis of gastric cancer and pro- [29] C. Li, L. Zhou, J. He, X. Q. Fang, S. W. Zhu, and M. M. Xiong, motes cell proliferation through epigenetically regulating P “Increased long noncoding RNA SNHG20 predicts poor 15 and P 16,” Molecular Cancer, vol. 14, no. 1, p. 82, 2015. prognosis in colorectal cancer,” BMC Cancer, vol. 16, no. 1, [16] M. Sun, F. Nie, Y. Wang et al., “LncRNA HOXA11-AS pro- p. 655, 2016. motes proliferation and invasion of gastric cancer by scaffold- [30] Y. Fan, Y. F. Wang, H. F. Su et al., “Decreased expression of ing the chromatin modification factors PRC2, LSD1, and the long noncoding RNA LINC00261 indicate poor progno- DNMT1,” Cancer Research, vol. 76, no. 21, pp. 6299–6310, sis in gastric cancer and suppress gastric cancer metastasis 2016. by affecting the epithelial–mesenchymal transition,” Journal [17] Y. Liu, R. Xia, K. Lu et al., “LincRNAFEZF1-AS1 represses P of Hematology & Oncology, vol. 9, no. 1, p. 57, 2016. 21 expression to promote gastric cancer proliferation through [31] F. Qi, X. Liu, H. Wu et al., “Long noncoding AGAP2-AS1 is LSD1-mediated H3K4me2 demethylation,” Molecular Can- activated by SP1 and promotes cell proliferation and invasion cer, vol. 16, no. 1, p. 39, 2017. in gastric cancer,” Journal of Hematology & Oncology, vol. 10, [18] F. Yang, X. Xue, L. Zheng et al., “Long non-coding RNA no. 1, p. 48, 2017. GHET1 promotes gastric carcinoma cell proliferation by [32] D. Chen, H. Q. Ju, Y. X. Lu et al., “Long non-coding RNA increasing c-Myc mRNA stability,” The FEBS Journal, XIST regulates gastric cancer progression by acting as a vol. 281, no. 3, pp. 802–813, 2014. molecular sponge of miR-101 to modulate EZH2 expression,” [19] N. Chen, D. Guo, Q. Xu et al., “Long non-coding RNA Journal of Experimental & Clinical Cancer Research, vol. 35, FEZF1-AS1 facilitates cell proliferation and migration in no. 1, p. 142, 2016. colorectal carcinoma,” Oncotarget, vol. 7, no. 10, pp. 11271– [33] C. Ye, Z. Shen, B. Wang et al., “A novel long non-coding RNA 11283, 2016. lnc-GNAT1-1 is low expressed in colorectal cancer and acts κ [20] P. Li, X. Zhang, H. Wang et al., “MALAT1 is associated with as a tumor suppressor through regulating RKIP-NF- B-snail ” poor response to oxaliplatin-based chemotherapy in colorec- circuit, Journal of Experimental & Clinical Cancer Research, tal cancer patients and promotes chemoresistance through vol. 35, no. 1, p. 187, 2016. EZH2,” Molecular Cancer Therapeutics, vol. 16, no. 4, [34] M. Xu, Y. Wang, W. Weng et al., “A positive feedback pp. 739–751, 2017. loop of lncRNA-PVT1 and FOXM1 facilitates gastric cancer ” [21] T. Saito, J. Kurashige, S. Nambara et al., “A long non-coding growth and invasion, Clinical Cancer Research, vol. 23, – RNA activated by transforming growth factor-β is an inde- no. 8, pp. 2071 2080, 2017. pendent prognostic marker of gastric cancer,” Annals of Sur- [35] C. L. Yuan, H. Li, L. Zhu, Z. Liu, J. Zhou, and Y. Shu, “Aber- gical Oncology, vol. 22, no. S3, pp. 915–922, 2015. rant expression of long noncoding RNA PVT1 and its diag- fi [22] W. Zhao, S. Dong, B. Duan et al., “HOTAIR is a predictive nostic and prognostic signi cance in patients with gastric ” – and prognostic biomarker for patients with advanced gastric cancer, Neoplasma, vol. 63, no. 3, pp. 442 449, 2016. adenocarcinoma receiving fluorouracil and platinum combi- [36] L. Ye, L. Ren, J. J. Qiu et al., “Aberrant expression of long nation chemotherapy,” American Journal of Translational noncoding RNAs in colorectal cancer with liver metastasis,” Research, vol. 7, no. 7, pp. 1295–1302, 2015. Tumor Biology, vol. 36, no. 11, pp. 8747–8754, 2015. [23] T. Liu, X. Zhang, S. Gao et al., “Exosomal long noncoding [37] Q. Zheng, F. Wu, W. Y. Dai et al., “Aberrant expression RNA CRNDE-h as a novel serum-based biomarker for diag- of UCA1 in gastric cancer and its clinical significance,” nosis and prognosis of colorectal cancer,” Oncotarget, vol. 7, Clinical and Translational Oncology, vol. 17, no. 8, pp. 640– no. 51, pp. 85551–85563, 2016. 646, 2015. [24] R. Wang, L. du, X. Yang et al., “Identification of long non- [38] X. Chen, J. Kong, Z. Ma, S. Gao, and X. Feng, “Up regulation coding RNAs as potential novel diagnosis and prognosis of the long non-coding RNA NEAT1 promotes esophageal biomarkers in colorectal cancer,” Journal of Cancer Research squamous cell carcinoma cell progression and correlates with and Clinical Oncology, vol. 142, no. 11, pp. 2291–2301, poor prognosis,” American Journal of Cancer Research, vol. 5, 2016. no. 9, pp. 2808–2815, 2015. [25] H. Wu, J. Zheng, J. Deng et al., “LincRNA-uc002yug.2 [39] Y. J. Wang, J. Z. Liu, P. Lv, Y. Dang, J. Y. Gao, and Y. Wang, involves in alternative splicing of RUNX1 and serves as a pre- “Long non-coding RNA CCAT2 promotes gastric cancer dictor for esophageal cancer and prognosis,” Oncogene, proliferation and invasion by regulating the E-cadherin and vol. 34, no. 36, pp. 4723–4734, 2015. LATS2,” American Journal of Cancer Research, vol. 6, – [26] G.-L. Yao, C.-F. Pan, H. Xu et al., “Long noncoding RNA no. 11, pp. 2651 2660, 2016. RP11-766N7.4 functions as a tumor suppressor by regulating [40] Y. Zhang, R. Yang, J. Lian, and H. Xu, “LncRNA sox2ot over- epithelial-mesenchymal transition in esophageal squamous expression serves as a poor prognostic biomarker in gastric cell carcinoma,” Biomedicine & Pharmacotherapy, vol. 88, cancer,” American Journal of Translational Research, vol. 8, pp. 778–785, 2017. no. 11, pp. 5035–5043, 2016. Disease Markers 13

[41] W. Chen, M. D. Huang, R. Kong et al., “Antisense long non- [55] B. Zhou, X. Y. Jing, J. Q. Wu, H. F. Xi, and G. J. Lu, coding RNA HIF1A-AS2 is upregulated in gastric cancer and “Down-regulation of long non-coding RNA LET is associ- associated with poor prognosis,” Digestive Diseases and Sci- ated with poor prognosis in gastric cancer,” International ences, vol. 60, no. 6, pp. 1655–1662, 2015. Journal of Clinical and Experimental Pathology, vol. 7, – [42] B. Yue, B. Sun, C. Liu et al., “Long non-coding RNA Fer-1- no. 12, pp. 8893 8898, 2014. like protein 4 suppresses oncogenesis and exhibits prognostic [56] Y. Hu, J. Pan, Y. Wang, L. Li, and Y. Huang, “Long noncoding value by associating with miR-106a-5p in colon cancer,” Can- RNA linc-UBC1 is negative prognostic factor and exhibits cer Science, vol. 106, no. 10, pp. 1323–1332, 2015. tumor pro-oncogenic activity in gastric cancer,” Interna- [43] X. He, X. Tan, X. Wang et al., “C-Myc-activated long noncod- tional Journal of Clinical & Experimental Pathology, vol. 8, – ing RNA CCAT1 promotes colon cancer cell proliferation no. 1, pp. 594 600, 2015. and invasion,” Tumor Biology, vol. 35, no. 12, pp. 12181– [57] C. Y. Wang, L. Hua, K. H. Yao, J. T. Chen, J. J. Zhang, and 12188, 2014. J. H. Hu, “Long non-coding RNA CCAT2 is up-regulated in ” [44] D. Yin, Z. J. Liu, E. Zhang, R. Kong, Z. H. Zhang, and R. H. gastric cancer and associated with poor prognosis, Interna- “ tional Journal of Clinical and Experimental Pathology, Guo, Decreased expression of long noncoding RNA MEG3 – affects cell proliferation and predicts a poor prognosis in vol. 8, no. 1, pp. 779 785, 2015. “ patients with colorectal cancer,” Tumor Biology, vol. 36, [58] Y. K. Ren, Y. Xiao, X. B. Wan et al., Association of long non- no. 6, pp. 4851–4859, 2015. coding RNA HOTTIP with progression and prognosis in colorectal cancer,” International Journal of Clinical and [45] F. Nie, S. Ma, M. Xie, Y. W. Liu, W. de, and X. H. Liu, Experimental Pathology, vol. 8, no. 9, pp. 11458–11463, 2015. “Decreased long noncoding RNA MIR31HG is correlated “ with poor prognosis and contributes to cell proliferation in [59] Y. Liu, M. Zhang, L. Liang, J. Li, and Y. X. Chen, Over- gastric cancer,” Tumor Biology, vol. 37, no. 6, pp. 7693– expression of lncRNA DANCR is associated with advanced 7701, 2016. tumor progression and poor prognosis in patients with colo- rectal cancer,” International Journal of Clinical and Experi- [46] S.-M. Park, S.-J. Park, H.-J. Kim et al., “A known expressed mental Pathology, vol. 8, no. 9, pp. 11480–11484, 2015. sequence tag, BM742401, is a potent lincRNA inhibiting can- ” [60] Y. L. Wang, Y. Bai, W. J. Yao, L. Guo, and Z. M. Wang, cer metastasis, Experimental & Molecular Medicine, vol. 45, “ no. 7, 2013. Expression of long non-coding RNA ZEB1-AS1 in esoph- “ ageal squamous cell carcinoma and its correlation with [47] X. Li, F. Wang, Y. Sun, Q. Fan, and G. Cui, Expression of tumor progression and patient survival,” International Jour- long non-coding RNA PANDAR and its prognostic value in ” nal of Clinical and Experimental Pathology, vol. 8, no. 9, colorectal cancer patients, The International Journal of Bio- pp. 11871–11876, 2015. logical Markers, vol. 32, no. 2, pp. 218–223, 2017. [61] L. Li, L. Zhang, Y. Zhang, and F. Zhou, “Increased expression “ [48] J. S. Chen, Y. F. Wang, X. Q. Zhang et al., H19 serves as a of LncRNA BANCR is associated with clinical progression diagnostic biomarker and up-regulation of H19 expression and poor prognosis in gastric cancer,” Biomedicine & Phar- ” contributes to poor prognosis in patients with gastric cancer, macotherapy, vol. 72, pp. 109–112, 2015. Neoplasma, vol. 63, no. 2, pp. 223–230, 2016. [62] P. Ma, T. Xu, M. Huang, and Y. Shu, “Increased expres- “ [49] J. H. Zou, C. Y. Li, J. Bao, and G. Q. Zheng, High expression sion of LncRNA PANDAR predicts a poor prognosis in gas- of long noncoding RNA Sox2ot is associated with the aggres- tric cancer,” Biomedicine & Pharmacotherapy, vol. 78, ” sive progression and poor outcome of gastric cancer, Euro- pp. 172–176, 2016. pean Review for Medical and Pharmacological Sciences, “ – [63] C. Huang, Z. Yu, H. Yang, and Y. Lin, Increased MALAT1 vol. 20, no. 21, pp. 4482 4486, 2016. expression predicts poor prognosis in esophageal cancer [50] L. Jiang, W. Wang, G. Li et al., “High TUG1 expression is patients,” Biomedicine & Pharmacotherapy, vol. 83, pp. 8– associated with chemotherapy resistance and poor prognosis 13, 2016. ” in esophageal squamous cell carcinoma, Cancer Chemother- [64] B. Ni, X. Yu, X. Guo et al., “Increased urothelial cancer asso- – apy and Pharmacology, vol. 78, no. 2, pp. 333 339, 2016. ciated 1 is associated with tumor proliferation and metastasis [51] M. Svoboda, J. Slyskova, M. Schneiderova et al., “HOTAIR and predicts poor prognosis in colorectal cancer,” Interna- long non-coding RNA is a negative prognostic factor not only tional Journal of Oncology, vol. 47, no. 4, pp. 1329–1338, in primary tumors, but also in the blood of colorectal cancer 2015. ” – patients, Carcinogenesis, vol. 35, no. 7, pp. 1510 1515, 2014. [65] W. Peng, Z. Wang, and H. Fan, “LncRNA NEAT1 impacts [52] Y. Wang, Q. Y. Zhang, W. W. Weng et al., “Upregulation of cell proliferation and apoptosis of colorectal cancer via regu- the non-coding RNA OTUB1-isoform 2 contributes to gas- lation of Akt signaling,” Pathology & Oncology Research, tric cancer cell proliferation and invasion and predicts poor vol. 23, no. 3, pp. 651–656, 2017. gastric cancer prognosis,” International Journal of Biological [66] C. Jiao, Z. Song, J. Chen et al., “lncRNA-UCA1 enhances Sciences, vol. 12, no. 5, pp. 545–557, 2016. cell proliferation through functioning as a ceRNA of sox4 [53] X. B. Guo, Z. Hua, C. Li et al., “Biological significance of in esophageal cancer,” Oncology Reports, vol. 36, no. 5, long non-coding RNA FTX expression in human colorectal pp. 2960–2966, 2016. cancer,” International Journal of Clinical and Experimental [67] J. N. Liu and Y. M. Shangguan, “Long non-coding RNA Medicine, vol. 8, no. 9, pp. 15591–15600, 2015. CARLo-5 upregulation associates with poor prognosis in [54] F. Pan, J. Yao, Y. Chen et al., “A novel long non-coding RNA patients suffering gastric cancer,” European Review for Medi- FOXCUT and mRNA FOXC1 pair promote progression and cal and Pharmacological Sciences, vol. 21, no. 3, pp. 530–534, predict poor prognosis in esophageal squamous cell carci- 2017. noma,” International Journal of Clinical and Experimental [68] Z. Wu, X. L. Wang, H. M. Tang et al., “Long non-coding RNA Pathology, vol. 7, no. 6, pp. 2838–2849, 2014. HOTAIR is a powerful predictor of metastasis and poor 14 Disease Markers

prognosis and is associated with epithelial-mesenchymal [82] Y. Li, Y. Li, W. Chen et al., “NEAT expression is associated transition in colon cancer,” Oncology Reports, vol. 32, no. 1, with tumor recurrence and unfavorable prognosis in colorec- pp. 395–402, 2014. tal cancer,” Oncotarget, vol. 6, no. 29, pp. 27641–27650, 2015. [69] P. Zhou, L. Sun, D. Liu, C. Liu, and L. Sun, “Long non-coding [83] W. Chen, M. D. Huang, D. P. Sun et al., “Long intergenic RNA lincRNA-ROR promotes the progression of colon can- non-coding RNA 00152 promotes tumor cell cycle progres- cer and holds prognostic value by associating with miR-145,” sion by binding to EZH2 and repressing P 15 and P 21 in gas- Pathology Oncology Research, vol. 22, no. 4, pp. 733–740, tric cancer,” Oncotarget, vol. 7, no. 9, pp. 9773–9787, 2016. 2016. [84] D. Han, X. Gao, M. Wang et al., “Long noncoding RNA H19 [70] L. Yang, H. Wei, and H. J. Xiao, “Long non-coding RNA indicates a poor prognosis of colorectal cancer and promotes Loc 554202 expression as a prognostic factor in patients tumor growth by recruiting and binding to eIF4A3,” Onco- with colorectal cancer,” European Review for Medical and target, vol. 7, no. 16, pp. 22159–22173, 2016. Pharmacological Sciences, vol. 20, no. 20, pp. 4243–4247, [85] P. Yang, T. Chen, Z. Xu, H. Zhu, J. Wang, and Z. He, “Long 2016. noncoding RNA GAPLINC promotes invasion in colorectal [71] M.-H. Lü, B. Tang, S. Zeng et al., “Long noncoding RNA cancer by targeting SNAI2 through binding with PSF and BC032469, a novel competing endogenous RNA, upregulates NONO,” Oncotarget, vol. 7, no. 27, pp. 42183–42194, 2016. hTERT expression by sponging miR-1207-5p and promotes [86] C. Jin, W. Shi, F. Wang et al., “Long non-coding RNA HULC proliferation in gastric cancer,” Oncogene, vol. 35, no. 27, as a novel serum biomarker for diagnosis and prognosis pp. 3524–3534, 2016. prediction of gastric cancer,” Oncotarget, vol. 7, no. 32, [72] J. Su, E. Zhang, L. Han et al., “Long noncoding RNA BLA- pp. 51763–51772, 2016. CAT1 indicates a poor prognosis of colorectal cancer and [87] X.-G. Cao, R. Zhao, C. Zhu et al., “BC200 LncRNA a potential affects cell proliferation by epigenetically silencing of P 15,” predictive marker of poor prognosis in esophageal squamous Cell Death & Disease, vol. 8, no. 3, p. e2665, 2017. cell carcinoma patients,” OncoTargets and Therapy, vol. 9, [73] J. Fu, Y. Kong, and X. Sun, “Long noncoding RNA NEAT1 is pp. 2221–2226, 2016. an unfavorable prognostic factor and regulates migration and [88] D. Cao, Q. Ding, W. Yu, M. Gao, and Y. Wang, “Long non- invasion in gastric cancer,” Journal of Cancer Research and coding RNA SPRY4-IT1 promotes malignant development Clinical Oncology, vol. 142, no. 7, pp. 1571–1579, 2016. of colorectal cancer by targeting epithelial-mesenchymal [74] H.-W. Xie, Q.-Q. Wu, B. Zhu et al., “Long noncoding RNA transition,” OncoTargets and Therapy, vol. 9, pp. 5417– SPRY4-IT1 is upregulated in esophageal squamous cell carci- 5425, 2016. noma and associated with poor prognosis,” Tumor Biology, [89] X. Gao, J. Wen, P. Gao, G. Zhang, and G. Zhang, “Overex- vol. 35, no. 8, pp. 7743–7754, 2014. pression of the long non-coding RNA, linc-UBC1, is associ- [75] W. Peng, G. Wu, H. Fan, J. Wu, and J. Feng, “Long noncoding ated with poor prognosis and facilitates cell proliferation, RNA SPRY4-IT1 predicts poor patient prognosis and pro- migration, and invasion in colorectal cancer,” OncoTargets motes tumorigenesis in gastric cancer,” Tumor Biology, and Therapy, vol. 10, pp. 1017–1026, 2017. vol. 36, no. 9, pp. 6751–6758, 2015. [90] F. Wang, H. Ni, F. Sun, M. Li, and L. Chen, “Overexpression [76] F. Nie, X. Yu, M. Huang et al., “Long noncoding RNA ZFAS1 of lncRNA AFAP1-AS1 correlates with poor prognosis and promotes gastric cancer cells proliferation by epigenetically promotes tumorigenesis in colorectal cancer,” Biomedicine repressing KLF2 and NKD2 expression,” Oncotarget, vol. 8, & Pharmacotherapy, vol. 81, pp. 152–159, 2016. no. 24, pp. 38227–38238, 2017. [91] X. Ge, Y. Chen, X. Liao et al., “Overexpression of long non- [77] M. Ohtsuka, H. Ling, C. Ivan et al., “H19 noncoding RNA, an coding RNA PCAT-1 is a novel biomarker of poor prognosis independent prognostic factor, regulates essential Rb-E2F in patients with colorectal cancer,” Medical Oncology, vol. 30, and CDK8-β-catenin signaling in colorectal cancer,” eBioMe- no. 2, p. 588, 2013. dicine, vol. 13, pp. 113–124, 2016. [92] Q. Deng, B. He, T. Gao et al., “Up-regulation of 91H [78] X.-L. Zhou, W.-W. Wang, W.-G. Zhu et al., “High promotes tumor metastasis and predicts poor prognosis for expression of long non-coding RNAAFAP1-AS1predicts patients with colorectal cancer,” PLoS One, vol. 9, no. 7, chemoradioresistance and poor prognosis in patients with article e103022, 2014. esophageal squamous cell carcinoma treated with definitive [93] X. Sun, Y. Hu, L. Zhang et al., “Mining, validation, and clin- chemoradiotherapy,” Molecular Carcinogenesis, vol. 55, ical significance of colorectal cancer (CRC)-associated no. 12, pp. 2095–2105, 2016. lncRNAs,” PLoS One, vol. 11, no. 10, article e0164590, 2016. [79] M.-D. Xu, P. Qi, W.-W. Weng et al., “Long non-coding RNA [94] T. Xu, M. D. Huang, R. Xia et al., “Decreased expression LSINCT5 predicts negative prognosis and exhibits oncogenic of the long non-coding RNA FENDRR is associated with activity in gastric cancer,” Medicine, vol. 93, no. 28, article poor prognosis in gastric cancer and FENDRR regulates e303, 2014. gastric cancer cell metastasis by affecting fibronectin 1 [80] J. Sun, Y. Song, X. Chen et al., “Novel long non-coding RNA expression,” Journal of Hematology & Oncology, vol. 7, RP11-119F7.4 as a potential biomarker for the development no. 1, p. 63, 2014. and progression of gastric cancer,” Oncology Letters, vol. 10, [95] C. Shang, Y. Guo, J. Zhang, and B. Huang, “Silence of long no. 1, pp. 115–120, 2015. noncoding RNA UCA1 inhibits malignant proliferation and ” [81] E. Zhang, R. Kong, D. D. Yin et al., “Long noncoding RNA chemotherapy resistance to adriamycin in gastric cancer, ANRIL indicates a poor prognosis of gastric cancer and Cancer Chemotherapy and Pharmacology, vol. 77, no. 5, – promotes tumor growth by epigenetically silencing of pp. 1061 1067, 2016. miR-99a/miR-449a,” Oncotarget, vol. 5, no. 8, pp. 2276– [96] Z. Bian, L. Jin, J. Zhang et al., “LncRNA—UCA1 enhances 2292, 2014. cell proliferation and 5-fluorouracil resistance in colorectal Disease Markers 15

cancer by inhibiting miR-204-5p,” Scientific Reports, vol. 6, [111] H. Jiang, Y. Wang, M. Ai et al., “Long noncoding RNA no. 1, 2016. CRNDE stabilized by hnRNPUL2 accelerates cell prolifera- [97] Z. K. Zuo, Y. Gong, X. H. Chen et al., “TGFβ1-induced tion and migration in colorectal carcinoma via activating ” LncRNA UCA1 upregulation promotes gastric cancer inva- Ras/MAPK signaling pathways, Cell Death and Disease, sion and migration,” DNA and Cell Biology, vol. 36, no. 2, vol. 8, no. 6, article e2862, 2017. pp. 159–167, 2017. [112] C. Cui, D. Zhai, L. Cai, Q. Duan, L. Xie, and J. Yu, “Long [98] M. Lu, Z. Liu, B. Li, G. Wang, D. Li, and Y. Zhu, “The high noncoding RNA HEIH promotes colorectal cancer tumori- expression of long non-coding RNA PANDAR indicates a genesis via counteracting miR-939–mediated transcriptional poor prognosis for colorectal cancer and promotes metastasis repression of Bcl-xL,” Cancer Research and Treatment, by EMT pathway,” Journal of Cancer Research and Clinical vol. 50, no. 3, pp. 992–1008, 2018. Oncology, vol. 143, no. 1, pp. 71–81, 2017. [113] S. Wu, J. Liu, X. Wang, M. Li, Z. Chen, and Y. Tang, “Aber- [99] D. Lv, R. Sun, Q. Yu, and X. Zhang, “The long non-coding rant expression of the long non-coding RNAGHRLOS and RNA maternally expressed gene 3 activates P 53 and is down- its prognostic significance in patients with colorectal cancer,” regulated in esophageal squamous cell cancer,” Tumour Biol- Journal of Cancer, vol. 8, no. 19, pp. 4040–4047, 2017. ogy, vol. 37, no. 12, pp. 16259–16267, 2016. [114] Y. Yu, L. Li, Z. Zheng, S. Chen, E. Chen, and Y. Hu, “Long [100] J. Xu, R. Zhang, and J. Zhao, “The novel long noncoding non-coding RNA linc00261 suppresses gastric cancer pro- RNA TUSC7 inhibits proliferation by sponging MiR-211 gression via promoting Slug degradation,” Journal of Cellular in colorectal cancer,” Cellular Physiology and Biochemistry, and Molecular Medicine, vol. 21, no. 5, pp. 955–967, 2017. vol. 41, no. 2, pp. 635–644, 2017. [115] Y. Li, X. Wen, L. Wang et al., “LncRNA ZEB1-AS1 predicts [101] H. W. Ma, M. Xie, M. Sun et al., “The derived unfavorable prognosis in gastric cancer,” Surgical Oncology, long noncoding RNA DUXAP8 promotes gastric cancer cell vol. 26, no. 4, pp. 527–534, 2017. proliferation and migration via epigenetically silencing PLE- [116] M. Huang, J. Hou, Y. Wang et al., “Long noncoding RNA KHO1 expression,” Oncotarget, vol. 8, no. 32, pp. 52211– LINC00673 is activated by SP1 and exerts oncogenic proper- 52224, 2017. ties by interacting with LSD1 and EZH2 in gastric cancer,” [102] H.-b. Hu, H.-Y. Jie, and X.-X. Zheng, “Three circulating Molecular Therapy, vol. 25, no. 4, pp. 1014–1026, 2017. LncRNA predict early progress of esophageal squamous cell [117] P.-D. Li, J.-L. Hu, C. Ma et al., “Upregulation of the long carcinoma,” Cellular Physiology and Biochemistry, vol. 40, non-coding RNA PVT1 promotes esophageal squamous cell no. 1-2, pp. 117–125, 2016. carcinoma progression by acting as a molecular sponge of [103] Z. H. Fei, X. J. Yu, M. Zhou, H. F. Su, Z. Zheng, and C. Y. miR-203 and LASP1,” Oncotarget, vol. 8, no. 21, pp. 34164– Xie, “Upregulated expression of long non-coding RNA 34176, 2017. LINC00982 regulates cell proliferation and its clinical rele- [118] H. Shi, Z. Liu, D. Pei, Y. Jiang, H. Zhu, and B. Chen, “Devel- vance in patients with gastric cancer,” Tumour Biology, opment and validation of nomogram based on lncRNA vol. 37, no. 2, pp. 1983–1993, 2016. ZFAS1 for predicting survival in lymph node-negative esoph- [104] S. Chen, J. F. Yin, B. C. Lin et al., “Upregulated expression of ageal squamous cell carcinoma patients,” Oncotarget, vol. 8, long noncoding RNA SNHG15 promotes cell proliferation no. 35, pp. 59048–59057, 2017. and invasion through regulates MMP2/MMP9 in patients [119] X. Wu, X. Dinglin, X. Wang et al., “Long noncoding RNA with GC,” Tumor Biology, vol. 37, no. 5, pp. 6801–6812, 2016. XIST promotes malignancies of esophageal squamous cell [105] W. Tan, Z. Z. Song, Q. Xu et al., “Up-regulated expression of carcinoma via regulation of miR-101/EZH2,” Oncotarget, SPRY4-IT1 predicts poor prognosis in colorectal cancer,” vol. 8, no. 44, pp. 76015–76028, 2017. – Medical Science Monitor, vol. 23, pp. 309 314, 2017. [120] M. C. Ba, H. Long, S. Z. Cui et al., “Long noncoding RNA [106] W. Wang and C. Xing, “Upregulation of long noncoding LINC00673 epigenetically suppresses KLF4 by interacting RNA ZFAS1 predicts poor prognosis and prompts invasion with EZH2 and DNMT1 in gastric cancer,” Oncotarget, and metastasis in colorectal cancer,” Pathology - Research vol. 8, no. 56, pp. 95542–95553, 2017. – and Practice, vol. 212, no. 8, pp. 690 695, 2016. [121] Y. Zhu, B. Li, Z. Liu et al., “Up-regulation of lncRNA SNHG1 [107] W. Yao, Y. Bai, Y. Li et al., “Upregulation of MALAT-1 and indicates poor prognosis and promotes cell proliferation and its association with survival rate and the effect on cell cycle metastasis of colorectal cancer by activation of the Wnt/β- and migration in patients with esophageal squamous cell car- catenin signaling pathway,” Oncotarget, vol. 8, no. 67, cinoma,” Tumor Biology, vol. 37, no. 4, pp. 4305–4312, 2016. pp. 111715–111727, 2017. [108] Z. Liu, T. Yang, Z. Xu, and X. Cao, “Upregulation of [122] X.-Z. Yang, Q.-J. He, T.-T. Cheng et al., “Predictive value of the long non-coding RNA BANCR correlates with tumor LINC01133 for unfavorable prognosis was impacted by alco- progression and poor prognosis in esophageal squamous hol in esophageal squamous cell carcinoma,” Cellular Physi- cell carcinoma,” Biomedicine & Pharmacotherapy, vol. 82, ology and Biochemistry, vol. 48, no. 1, pp. 251–262, 2018. – pp. 406 412, 2016. [123] T. Xu, W. Y. Wang, P. Ma et al., “Upregulation of the long [109] F. J. Chen, M. Sun, S. Q. Li et al., “Upregulation of the long noncoding RNA FOXD2-AS1 promotes carcinogenesis by non-coding RNA HOTAIR promotes esophageal squamous epigenetically silencing EphB3 through EZH2 and LSD1, cell carcinoma metastasis and poor prognosis,” Molecular and predicts poor prognosis in gastric cancer,” Oncogene, Carcinogenesis, vol. 52, no. 11, pp. 908–915, 2013. vol. 37, no. 36, pp. 5020–5036, 2018. [110] X. Yu, Z. Yuan, Z. Yang et al., “The novel long noncoding RNA U50535 promotes colorectal cancer growth and metas- tasis by regulating CCL20,” Cell Death & Disease, vol. 9, no. 7, p. 751, 2018. M EDIATORSof INFLAMMATION

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