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Prognostic value of long noncoding RNAs in gastric cancer: a meta-analysis
Authors Gao S, Zhao ZY, Wu R, Zhang Y , Zhang ZY
Received 31 March 2018
Accepted for publication 29 May 2018
Published 14 August 2018 Volume 2018:11 Pages 4877—4891
DOI https://doi.org/10.2147/OTT.S169823
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Sanjeev K. Srivastava
Song Gao,1 Zhi-Ying Zhao,2 Rong Wu,1 Yue Zhang,3 Zhen-Yong Zhang1
1The Second Department of Clinical Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China; 2Division of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China; 3First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, People’s Republic of China
Background: In the last few years, accumulating evidence has indicated that numerous long noncoding RNAs (lncRNAs) are abnormally expressed in gastric cancer (GC) and are associated with the survival of GC patients. This study aimed to conduct a meta-analysis on 19 lncRNAs (AFAP1 antisense RNA 1 [AFAP1-AS1], CDKN2B antisense RNA 1 [ANRIL], cancer susceptibility 15 [CASC15], colon cancer associated transcript 2 [CCAT2], gastric adenocarcinoma associated, positive CD44 regulator, long intergenic noncoding RNA [GAPLINC], H19, imprinted maternally expressed transcript [H19], HOX transcript antisense RNA [HOTAIR], HOXA distal transcript antisense RNA [HOTTIP], long intergenic non-protein coding RNA 673 [LINC00673], metastasis-associated lung adenocarcinoma transcript 1 [MALAT1], maternally expressed 3 [MEG3], promoter of CDKN1A antisense DNA damage activated RNA [PANDAR], Pvt1 oncogene [PVT1], SOX2 overlapping transcript [Sox2ot], SPRY4 intronic transcript 1 [SPRY4-IT1], urothelial cancer associated 1 [UCA1], X inactive specific transcript [XIST], ZEB1 antisense RNA 1 [ZEB1-AS1] and ZNFX1 antisense RNA 1 [ZFAS1]) to systematically estimate their prognostic value in GC.
Methods: The qualified literature was systematically searched in PubMed, Web of Science, Embase and Cochrane Database of Systematic Reviews (up to March 16, 2018), and one meta-analysis relating to the relationship between lncRNA expression and overall survival (OS) of GC patients was performed. The only evaluation criterion of survival results was OS.
Results: A total of 6,095 GC patients and 19 lncRNAs from 51 articles were included in the present study. Among the listed 19 lncRNAs, 18 lncRNAs (other than SPRY4-IT1) showed a significantly prognostic value (P<0.05).
Conclusion: This meta-analysis suggested that the abnormally expressed lncRNAs (AFAP1-AS1, ANRIL, CASC15, CCAT2, GAPLINC, H19, HOTAIR, HOTTIP, LINC00673, MALAT1, MEG3, PANDAR, PVT1, Sox2ot, UCA1, XIST, ZEB1-AS1 and ZFAS1) were significantly associated with the survival of GC patients, among which AFAP1-AS1, CCAT2, LINC00673, PANDAR, PVT1, Sox2ot, ZEB1-AS1 and ZFAS1 were strong candidates in predicting the prognosis of GC patients.
Keywords: long noncoding RNA, gastric cancer, prognosis, meta-analysis
Introduction
In the last few years, accumulating evidence has indicated that numerous long noncoding RNAs (lncRNAs) are abnormally expressed in gastric cancer (GC) and are associated with the survival of GC patients.1–113 GC is the fourth most diagnosed tumor type and the third most common origin of tumor-related death all over the world.114,115 Although the incidence and mortality of GC are declining, >24,590 individuals are diagnosed with GC per year, of which 10,720 die from GC in the USA.116 Although diagnosis and treatment strategies have been improved, the number of surviving cases remains low, since diagnosis often occurs in the late stages.116,117 Thus, the molecular characteristics about the carcinogenesis of GC and the recognition of new biomarkers for GC are urgently needed.
lncRNA is a new type of noncoding RNA that has a length of >200 nucleotides (nt) and lacks important open reading frameworks and can be divided into five main categories (sense, antisense, bidirectional, intronic and intergenic).118 Abundant evidence has demonstrated that lncRNAs play significant regulatory roles in tumor biology via various mechanisms affecting transcriptional and posttranscriptional levels.118–120 Currently, for both cell behavior and clinicopathological factors, significant advances with respect to lncRNA effects on GC have been discovered.121
On account of the obvious expression differences between normal and malignant tissues as well as causal roles of lncRNAs in cancer development, lncRNAs are now attracting increasing attention, which has led to numerous investigations of the correlation between lncRNA states and clinical results in GC. Nevertheless, most of these studies were performed with small samples, and there were inconsistently observed connections. Consequently, we conducted a meta-analysis to determine the accurate role of lncRNAs in the prognosis of GC patients, which possibly supplied us with new insights into the clinical value of combined detection in forecasting prognostic results and determining promising biomarkers in GC treatment strategies.
Methods
Literature search strategy
We basically performed a systematic selection of papers published in English from four databases (PubMed, Web of Science, Embase and Cochrane Database of Systematic Reviews). A comprehensive search was conducted using the subject term: lncRNA and gastric cancer. Two authors (Song Gao and Zhi-Ying Zhao) checked the titles and abstracts of the retrieved papers, and Yue Zhang reevaluated uncertain data. Figure 1 shows the flow diagram of the literature search and selection.
 | Figure 1 Flow diagram of the literature search and selection. |
Inclusion criteria
We set up inclusion criteria for qualified papers, which were analyzed using our full-text assessment: 1) articles concerning the pertinence between lncRNA level in cancer tissues and prognosis of GC patients; 2) the survival results were estimated using overall survival (OS) and 3) full-text papers published in English.
Exclusion criteria
Articles that did not meet the abovementioned inclusion criteria, reviews, letters and laboratory studies without raw data were excluded. Articles of non-dichotomous lncRNA expression levels and frequency of studies evaluating prognostic value of lncRNAs equal to 1 were also excluded. If more than one paper had been published on the identical study cohort, only the most well-rounded investigation was selected for this research. In addition, if both of the univariate and multivariate outcomes were covered, only the latter were chosen, since they were adjusted for confounding factors.
Research frequency
Table 1 gives the frequency of investigations reporting prognosis of GC patients, which included the lncRNA name, frequency of researched lncRNA and reference.
 | Table 1 Research frequency of lncRNAs in GC |
Data extraction
The survival data were recovered from qualified articles independently by two authors (Song Gao and Zhi-Ying Zhao). Data extracted from them are as follows: researched lncRNA, first author’s name, paper publication year, reference, patient’s nationality, study design, histological type, patient number, neoplasm staging, cutoff value, detected method, follow-up period, survival analysis type, HRs and 95% CIs. The detailed data are shown in Table 2. If HR and 95% CI were not directly shown in the paper, data from survival curve were extracted. Disagreements were discussed with the third investigator (Yue Zhang).
 | Table 2 Basic information of included articles |
Statistical analysis
Stata version 13.0 (StataCorp LP, College Station, TX, USA) was used for the whole meta-analysis. HR and 95% CI from GC patients were calculated on the basis of survival curve and patient number using Engauge Digitizer version 4.1 and Tierney’s method.122 The random-effect model was used in the whole article because different histological type (frozen, formalin-fixed paraffin-embedded or undefined) from GC patients at different neoplasm staging, cutoff value and lncRNA detected method was used in the single study. The HR was considered significant if its P-value was <0.05 and 95% CI did not contain the value 1. Furthermore, the lncRNA was considered as a strong biomarker of prognosis, if its HR was >2. The Begg’s funnel plot was used to estimate publication bias, and a two-tailed P-value <0.05 was considered as significant. The sensitivity analysis was performed to examine how sensitive the merged HR was if the single study was removed, and if the point of evaluation was outside the 95% CI after it was removed from the whole analysis, a single research was considered as excessive influence.
Results
Meta-analysis
Table 3 gives the basic information of the merged meta-analysis for researched lncRNAs.
 | Table 3 HR with 95% CI of lncRNA expression in GC |
AFAP1 antisense RNA 1 (AFAP1-AS1), CDKN2B antisense RNA 1 (ANRIL), cancer susceptibility 15 (CASC15), colon cancer-associated transcript 2 (CCAT2), gastric adenocarcinoma associated, positive CD44 regulator, long intergenic noncoding RNA (GAPLINC) and H19, imprinted maternally expressed transcript (H19) demonstrated significantly prognostic value
Two articles3,4 reported the relationship between high AFAP1-AS1 expression and OS, indicating that GC patients with its high expression had significantly worse OS than those with its low expression (HR=2.47, 95% CI=1.41–4.30, P<0.01).
Two researches9,10 covered the connections between high ANRIL expression and OS, suggesting that GC patients with its high expression had significantly poorer OS than those with its low expression (HR=1.68, 95% CI=1.16–2.43, P<0.01).
Two investigations18,19 analyzed the associations between high CASC15 expression and OS, showing that GC patients with its high expression had significantly shorter OS than those with its low expression (HR=1.99, 95% CI=1.21–3.28, P<0.01).
Two studies21,22 focused on the correlation between high CCAT2 expression and OS, manifesting that GC patients with its high expression had significantly worse OS than those with its low expression (HR=2.17, 95% CI=1.53–3.09, P<0.01).
Two papers32,33 paid attention to the pertinence between high GAPLINC expression and OS, demonstrating that GC patients with its high expression had significantly poorer OS than those with its low expression (HR=1.49, 95% CI=1.18–1.89, P<0.01).
Four literature29,36–38 described the relativity between high H19 expression and OS, proving that GC patients with its high expression had significantly shorter OS than those with its low expression (HR=1.51, 95% CI=1.05–2.17, P=0.03; Figure 2).
 | Figure 2 Forest plot of pooled analyses of OS in association with high AFAP1-AS1, ANRIL, CASC15, CCAT2, GAPLINC and H19 expression levels. |
HOX transcript antisense RNA (HOTAIR) demonstrated significantly prognostic value
Nine essays41–49 discussed the relation between high HOTAIR expression and OS, illuminating that GC patients with its high expression had significantly worse OS than those with its low expression (HR=1.93, 95% CI=1.53–2.43, P<0.01; Figure 3).
 | Figure 3 Forest plot of pooled analysis of OS in association with high HOTAIR expression levels. |
Publication bias
The Begg’s funnel plot was used to estimate publication bias, and its P-value was 0.20, so there was no significant publication bias in the pooled analysis of OS about high HOTAIR expression (Figure 4).
 | Figure 4 Beggs’s funnel plot of publication bias for pooled analysis of OS in association with high HOTAIR expression levels. |
Sensitivity analysis
The sensitivity analysis was performed to examine how sensitive the merged HR was if the single study was removed. After this process, no individual study significantly affected the combined HR with 95% CI (Figure 5).
 | Figure 5 Sensitivity analysis of pooled analysis of OS in association with high HOTAIR expression levels. |
HOXA distal transcript antisense RNA (HOTTIP), long intergenic non-protein coding RNA 673 (LINC00673), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), maternally expressed 3 (MEG3), promoter of CDKN1A antisense DNA damage activated RNA (PANDAR) and Pvt1 oncogene (PVT1) demonstrated significantly prognostic value
The details are shown in Table 3 and Figure 6.
 | Figure 6 Forest plot of pooled analyses of OS in association with high HOTTIP, LINC00673, MALAT1, PANDAR, PVT1 expression levels, or low MEG3 expression levels. |
SOX2 overlapping transcript (Sox2ot), urothelial cancer-associated 1 (UCA1), X inactive specific transcript (XIST), ZEB1 antisense RNA 1 (ZEB1-AS1) and ZNFX1 antisense RNA 1 (ZFAS1) demonstrated significantly prognostic value
The details are shown in Table 3 and Figure 7.
 | Figure 7 Forest plot of pooled analyses of OS in association with high Sox2ot, UCA1, XIST, ZEB1-AS1, ZFAS1 expression levels, or low SPRY4-IT1 expression levels. |
Discussion
Current situation
So far, the clinical treatment of GC remains limited. In the past score years, there has been little progress in both traditional and new treatment methods. Therefore, novel biomarkers that can improve the prognosis of GC patients are in need. Recently, there is an increasing evidence that lncRNAs can hinder the growth and metastasis of cancer. For example, Xu et al123 reported that upregulating long stress-induced noncoding 5 (LSINCT5) significantly promoted the growth of the GC cell, while downregulating LSINCT5 suppressed its growth. Dan et al124 conducted the cancer model experiments using mice, proving that MEG3 overexpression could suppress GC growth and metastasis in vivo by suppressing miR-21 expression. More importantly, several abnormally expressed lncRNAs have been discovered to touch upon the development of GC and perhaps possess prognostic potency in this illness. In view of the above consequences, we conducted this meta-analysis about the prognostic value of lncRNAs in GC.
Research finding
In the present research, a total of 51 articles reporting 19 lncRNAs, which were latent prognostic biomarkers and 6,095 GC patients were included, among which 18 lncRNAs (except SPRY4 intronic transcript 1 [SPRY4-IT1]) manifested a significantly prognostic value. Meanwhile, strong heterogeneity was only shown in two (H19 and SPRY4-IT1) analyses about lncRNAs, during which there was no significant associations between SPRY4-IT1 expression and OS. Further analysis suggested that AFAP1-AS1, CCAT2, LINC00673, PANDAR, PVT1, Sox2ot, ZEB1-AS1 and ZFAS1 were strong candidates in predicting prognosis of GC patients.
Molecular mechanisms
Figure 8 shows the summary of lncRNAs with aberrant expression, potential targets and pathways included in this study. It is noteworthy that there existed inconsistent outcomes about expression of HOTTIP and SPRY4-IT1 compared with normal controls, so these two lncRNAs were not shown to be up or down expressed. Unexpected results were findings that CDKN1A was target of both CASC15 and PANDAR and KLF2 was target of both LINC00673 and ZFAS1. In addition, cell proliferation was the most related cell function of these lncRNAs.
 | Figure 8 Summary of lncRNAs with altered expression, potential targets and pathways entered in this study. |
Merits
The current study had several merits: 1) nearly all articles appraising the associations between OS of GC patients and lncRNA expression were searched and are clearly shown in Table 1; 2) most of our meta-analyses revealed no or low heterogeneity (I2≤50.0%), indicating relatively consistent results of the meta-analyses and 3) all the included studies had a relatively large sample size (≥30), decreasing the error of low sample size to some degree.
Limitations
However, the limitations of this work could not be ignored: 1) only English papers were included in the present research, which may exclude potentially relevant articles; 2) most of the patients were from China, which cannot adequately represent the prognosis of global patients; 3) only the meta-analysis of HOTAIR was composed of nine articles,41–49 and other merged analyses about lncRNAs were from relatively small article number (two to four) and 4) the papers omitted due to no mention of OS may provide a lot of information on which lncRNAs hold promise for a prognostic value.
Inspirations
This study left several inspirations for us: 1) lncRNAs were arranged in an alphabetical order as shown in Table 1, via which the recently research frequency could be distinctly seen by clinical workers and scientific researchers; 2) the detailed outcomes of OS from the pooled analyses are shown in Table 3, through which combined detection of lncRNAs might better predict the survival time of GC patients and 3) for the molecular mechanisms of the included lncRNAs, their connections are shown in Figure 8, which might play enlightening roles in future basic experiments on lncRNAs in GC.
Conclusion
This meta-analysis suggested that the abnormally expressed lncRNAs (AFAP1-AS1, ANRIL, CASC15, CCAT2, GAPLINC, H19, HOTAIR, HOTTIP, LINC00673, MALAT1, MEG3, PANDAR, PVT1, Sox2ot, UCA1, XIST, ZEB1-AS1 and ZFAS1) were significantly associated with the survival of GC patients, among which AFAP1-AS1, CCAT2, LINC00673, PANDAR, PVT1, Sox2ot, ZEB1-AS1 and ZFAS1 were strong candidates in predicting prognosis of GC patients.
Author contributions
Yue Zhang contributed toward study concept and design. Song Gao and Zhi-Ying Zhao were involved in acquisition of data. Song Gao, Zhi-Ying Zhao and Rong Wu carried out analysis and interpretation of data. Yue Zhang performed drafting of the manuscript. Song Gao, Zhi-Ying Zhao, Rong Wu, Yue Zhang and Zhen-Yong Zhang assisted with revision of manuscript. Yue Zhang and Zhen-Yong Zhang helped in supervision of work. All authors read and approved the final manuscript. All authors contributed toward data analysis, drafting and revising the paper and agree to be accountable for all aspects of the work.
Disclosure
The authors report no conflicts of interest in this work.
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