Hindawi Disease Markers Volume 2020, Article ID 6723487, 13 pages https://doi.org/10.1155/2020/6723487

Research Article Association between lncRNA GAS5, MEG3, and PCAT-1 Polymorphisms and Risk: A Meta-Analysis

Xiaoyan Dong ,1 Wenyan Gao,2 Xiaoling LV ,3 Yazhen Wang ,3 Qing Wu ,3 Zhouxin Yang ,3 Genxiang Mao ,3 and Wenmin Xing 3

1Department of Pharmacy and Medicine Pharmacy, Jiang Su College of Nursing, Huian, 223003, China 2Institute of Materia Medica, Zhejiang Academy of Medical Sciences, Hangzhou 310013, China 3Zhejiang Provincial Key Lab of Geriatrics, Department of Geriatrics, Zhejiang Hospital, Hangzhou 310013, China

Correspondence should be addressed to Genxiang Mao; [email protected] and Wenmin Xing; [email protected]

Received 27 June 2019; Revised 15 October 2019; Accepted 28 November 2019; Published 28 March 2020

Guest Editor: Ioannis Kosmas

Copyright © 2020 Xiaoyan Dong 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.

Purpose. Long noncoding (lncRNAs) have been widely studied, and single nucleotide polymorphisms (SNPs) in lncRNAs are considered to be genetic factors that influence cancer susceptibility. The lncRNA GAS5, MEG3, and PCAT-1 polymorphisms are shown to be possibly associated with cancer risk. The aim of this meta-analysis was to systematically evaluate this association. Methods. Studies were selected from PubMed, Web of Science, Embase, Google Scholar, Cochrane Library, the Chinese National Knowledge Infrastructure (CNKI), and the Chinese Biomedical Literature Database (CBM) through inclusion and exclusion criteria. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using the random-effects model or fixed-effects model to assess the association between lncRNA polymorphisms and cancer susceptibility. Metaregression and publication bias analyses were also conducted. All analyses were performed using the Stata 12.0 software. Results. Sixteen articles (covering 13750 cases and 17194 controls) were included in this meta-analysis. A significant association between SNP rs145204276 and gastric cancer risk was observed (del vs. ins: OR = 0:79, 95%CI = 0:72‐0:86; del/del vs. ins/ins+del/ins: OR = 0:74, 95%CI = 0:59‐0:91; del/ins vs. ins/ins: OR = 0:84, 95%CI = 0:67‐1:05). For rs16901904, a decreased cancer risk was observed in three genetic models (C vs. T: OR = 0:79, 95%CI = 0:70‐0:90; CC vs. CT+TT: OR = 0:49, 95%CI = 0:37‐0:65; CC vs. TT: OR = 0:49, 95%CI = 0:37‐0:66). No statistical significance was found in the metaregression analysis. For all of the included SNPs, no publication bias was found in all genotype models. Conclusions. The rs145204276 SNP in lncRNA GAS5 is likely to be associated with gastric cancer risk, whereas the rs16901904 SNP in lncRNA PCAT-1 bears association with a decreased cancer risk.

1. Introduction cal mechanisms of cancer development, such as genetic bio- markers which can be used as prognostic factors for cancer Cancer has become a leading cause of death worldwide not patients [4]. only in high-income but also in middle-income countries High-throughput sequencing technologies have identi- [1, 2]. Due to the growth of the aging population, the cancer fied a great number of noncoding RNAs in the genome, burden is expanding in many countries; there were about which could be classified into small (18-200 nts) and long 18.1 million new cancer cases and 9.6 million cancer deaths (200 nts to >100 kb) noncoding RNAs (lncRNAs) [5]. in 2018 [3]. Many investigators have devoted much effort Although lncRNAs have once been viewed as “transcrip- to reducing cancer mortality and morbidity. For example, tional noise” or “dark matter” in the genome, an abnormal in clinical therapy, surgical resection is an effective treatment expression of lncRNAs virtually plays a vital role in cancer for localized tumors, but the disease could still exhibit poten- pathogenesis, such as in cancer initiation, progression, and tial regional or distant metastasis, or high resistance toward metastasis [6]. Studies suggested that genetic variation also conventional chemotherapy and radiotherapy. Therefore, played important roles in the development of many types of researchers have focused on the potential molecular biologi- cancer. Single nucleotide polymorphisms (SNPs), a common 2 Disease Markers type of genetic variation, present in lncRNAs have been in order to summarize all eligible studies and evaluate the found to be able to affect the structure, expression level, overall relation between cancer risk and lncRNA (GAS5, and function of lncRNAs by interfering with the correspond- PCAT-1, and MEG3) polymorphisms. ing target mRNAs [7]. Evidence has demonstrated that SNPs in lncRNA were related to the occurrence of various 2. Material and Methods diseases, particularly cancer [8]. Therefore, lncRNA poly- morphisms are considered to be potentially associated with 2.1. Search Strategy. Two investigators independently con- the mechanism underlying cancer susceptibility. ducted an electronic literature search (published before April Recently, research showed that GAS5 overexpression 20, 2019) using PubMed, Web of Science, Embase, Google could inhibit the invasive ability of hepatocarcinoma cells Scholar, Cochrane Library, the Chinese National Knowledge affecting the epithelial-mesenchymal transition (EMT) pro- Infrastructure (CNKI), and the Chinese Biomedical Literature cess, which is very important in early events of the metastatic Database (CBM). The restriction in publication languages spread of tumor cells and can make cells more active and inva- was selected as English and Chinese. During our search pro- sive [9]. Meanwhile, colorectal cancer (CRC) cell growth and cess, the following keywords were used: “lncRNA GAS5,” colony formation were also inhibited by the induction of cell “lncRNA MEG3,”“lncRNA PCAT-1,”“polymorphism,” cycle G0/G1 arrest and apoptosis [10], increasing casp9 “Single nucleotide polymorphism,”“genetic variant,”“can- mRNA and pho-Casp9 protein and decreasing extracellular- cer,”“tumor,”“malignancy,”“carcinoma,” and “neoplasm.” regulated protein kinases (ERK), Casp3 mRNA, p-Akt, All clearly irrelevant studies such as case reports and review p-ERK, and pho-Casp3 proteins [11]. However, the down- articles were excluded. The relevant studies cited in the regulation of GAS5 expression accelerates depletion of the references of review articles were also manually searched YBX1 protein and decreases the expression of p21, thereby for additional eligible studies. eliminating G1 arrest to control the proliferation of gastric cancer [12]. A functional 5- (AGGCA/-) insertion/- 2.2. Study Selection. Two researchers independently evalu- deletion (indel) polymorphism (rs145204276) that exists in ated the titles and abstracts of the identified articles to decide the promoter region of lncRNA growth arrest-specific5 whether they met the study criteria. Differences were solved (lncRNA GAS5) has been investigated in multiple cancer by consensus. Both cross-sectional and longitudinal studies types [13–23]. A deletion (del) allele appears to enhance on the relationship between SNPs in lncRNAs GAS5, transcription activity when compared to an insertion (ins) MEG3, and PCAT-1 and cancer risk were included. Studies allele [15]. Zheng et al. performed a two-stage, case-control were included in this meta-analysis only when they met the study to investigate the association between lncRNA GAS5 following criteria: (1) the study evaluated the association of polymorphisms (rs145204276) and CRC risk, and the results SNPs in lncRNAs GAS5, MEG3, and PCAT-1 with cancer indicated that the del allele of rs145204276 was significantly risk; (2) the study was published in Chinese or English lan- associated with a 21% decreased risk of CRC [21]. However, guage; (3) the study had an original case-control design or logistic regression analysis showed that the deletion allele of was a cohort-designed study in humans; (4) the study pro- rs145204276 significantly increased the risk of hepatocellular vided sufficient genotyping data to estimate ORs and 95% carcinoma (HCC) in two independent case control sets CIs; (5) genotype frequencies of subjects in controls were in (1034 HCCs and 1054 controls) [18]. accordance with the Hardy-Weinberg equilibrium (HWE); Furthermore, lncRNAs such as GAS5, H19, MEG3, and (6) all cancer cases were diagnosed at any stage by pathology, TUSC7 play oncogenic or tumor suppressor roles in corre- but only primary tumor could be involved. If there was a lation with tumor suppressor protein P53 or oncoprotein parallel publication, we selected the study with a larger c-Myc, respectively [24]. Balci et al. discovered that PCAT-1, sample size. ANRIL, and H19 could inhibit glioblastoma (GBM) cell Studies were excluded if (1) they were not case-control proliferation, invasion, and migration. Conversely, primary studies; (2) they were case reports, comments, meta-analyses, tumors in GBMs displaying tumor progression were charac- or review articles; (3) they were a previous study that was terized by increased MEG3 and HOTAIR expression levels duplicated; (4) they were the control groups that did not con- [25]. SNPs in lncRNA prostate cancer-associated lncRNA form to HWE; (5) there were not enough genotyping data to transcript 1 (PCAT-1), such as rs1902432, rs16901904, calculate the ORs and 95% CIs; and (6) they were studies on rs4871771, and rs710886, have also been investigated in cell lines and gene expression. If there was only one study cancer development [26–28]. Yuan et al. investigated the asso- investigating one particular lncRNA gene which was not suit- ciation between lncRNA PCAT-1 rs1902432, rs16901904, able for meta-analysis, it was also excluded. Additionally, rs4871771, and rs710886 and prostate cancer risk in the when researchers did not report detailed information regard- Chinese population, but only rs1902432 was found to be ing the genotype distribution in each group, the correspond- associated with an increased risk of prostate cancer [28]. In ing authors of the study were contacted for unpublished data. contrast, there was no association between lncRNA PCAT-1 rs1902432 and bladder cancer in the two-stage, case-control 2.3. Data Extraction. Data extraction was performed inde- study conducted by Lin et al. [27]. Similarly, the published pendently by two researchers, including the following infor- results on the relation between lncRNA maternally expressed mation from each study: first author, year of publication, gene 3 (lncRNA MEG3) polymorphisms and cancer risk were ethnicity, cancer type, source of controls, number of cases also diverse [29–32]. Therefore, we conducted a meta-analysis and controls, genotype distribution of cases and controls, Disease Markers 3

Related studies were obtained from PubMed, Web of Science, Embase, Google Scholar, CNKI, and so on

365 related studies were obtained from PubMed, Web of Science, Embase, Google Scholar, CNKI, and so on

Potentially relevant records by examining titles and abstracts

25 studies were potentially included in the present analysis

Studies were excluded for the following condition: (i) Absence of control group (n = 2) (ii) Absence of detailed data (n = 3) (iii) Did not confirm to HWE (n = 3) (iv) Only one study about rs55829688 and rs1951625

16 studies were finally included in the present analysis

Figure 1: A flowchart of the selection process for the included studies. genotyping method, and P value of HWE in controls. Any a systematic publication search. After excluding duplicate lit- inconsistencies were resolved by a third researcher until a eratures, we independently read the article abstracts and their consensus was reached. references to assess their eligibility for the meta-analysis, as well as the published meta-analyses of the relevant genes. 2.4. Statistical Analysis. The present meta-analysis investi- Subsequently, 25 studies were potentially included in the gates the relationship between lncRNA GAS5, lncRNA present analysis for further evaluation. After considering MEG3, and lncRNA PCAT-1 SNPs and various types of can- the full texts of these articles, nine studies were excluded for cers with ORs and 95% CIs. For each SNP, five genetic models the following reasons: two articles did not present control of ORs and 95% CIs (additive model, dominant model, and groups; three articles did not have sufficient data; three arti- recessive model as well as homozygous and heterozygous cles did not conform to HWE; there was only one study about models) were estimated. Subgroup analysis was conducted rs55829688 and rs1951625. Finally, 16 articles (13750 cases according to ethnicity, source of controls, and cancer types. and 17194 controls) were included in the quantitative syn- Chi-square test (P >0:05) was applied to calculate the thesis. A flowchart of the study selection process is shown P value of HWE in control groups. The associations in Figure 1. between each SNP and cancer susceptibility were estimated The characteristics of the studies included in this meta- by pooled odds ratios (ORs) and 95% confidence intervals analysis are presented in Table 1. Among these articles, 11 (CIs) under five different genetic models. The heterogeneity involved rs145204276 in lncRNA GAS5 [13–23]. Within 2 of results throughout the studies was assessed using the I these 11 articles, 10 investigated Asian and one investigated statistic, which describes the percentage of total variation Caucasian (Iran) ethnicity, and the source of controls was across the studies that was attributable to heterogeneity mainly population-based (9 studies). Three studies regarded rather than to chance. The I2 values of 25% and 75% gastric cancer, and three were involved in CRC. Moreover, corresponded to cutoff points for low and high degrees of Zheng et al. [21] investigated rs145204276 in two independent heterogeneity. The pooled effect was calculated using the stages, so we treated the study as two independent investiga- random-effects model when the I2 value was >75%. Other- tions. Four articles considered not only rs7158663 but also wise, a fixed-effects model was used in case of significant rs4081134 in lncRNA MEG3 [29–32]. Two articles involved heterogeneity across studies. A sensitivity analysis was per- rs1902432, rs16901904, rs4871771, and rs710886 in lncRNA formed to evaluate the influence of each individual study on PCAT-1 [27, 28]. The majority of these studies were case- overall estimates. Begg and Egger’s tests were used to assess control studies. A variety of genotyping methods, such as potential publication bias. All statistical analyses were con- polymerase chain reaction-restriction fragment length poly- ducted by Stata software version 12.0 (Stata Corp LP, TX, morphism (PCR-RFLP), real-time PCR, TaqMan assay, and USA). A P value < 0.05 was considered statistically significant. MALDI-TOF mass spectrometry, were used in these studies.

3. Results 3.2. Meta-Analysis Results 3.1. Characteristics of the Included Studies and Subjects. A 3.2.1. The rs145204276 SNP in lncRNA GAS5 and Cancer total of 365 related studies were preliminarily retrieved after Susceptibility. The relationship between the rs145204276 4 Disease Markers

Table 1: Characteristics of the studies included in this meta-analysis.

Source of Genotyping Control HWE lncRNA First author Year Ethnicity SNPs Cancer type Case Control control method P value MassArray Jupeng Yuan 2018 Asian HB rs145204276 Glioma 404 820 0.14 system Keyvan Aminian 2018 Caucasian PB rs145204276 PCR Gastric cancer 130 230 0.27 Qianjun Li a 2018 Asian PB rs145204276 RT-PCR Gastric cancer 853 954 0.47 Qianjun Li b 2018 Asian PB rs145204276 RT-PCR Gastric cancer 1253 1354 0.37 Hepatocellular Ruiyang Tao 2015 Asian PB rs145204276 RT-PCR 1034 1054 0.07 carcinoma Yiyin Tang 2018 Asian HB rs145204276 PCR Breast cancer 575 602 0.93 GAS5 Yongbin Zheng I 2016 Asian PB rs145204276 PCR Colorectal cancer 600 600 0.76 Yongbin ZhengII 2016 Asian PB rs145204276 PCR Colorectal cancer 800 800 >0.05 Weihao Li 2017 Asian PB rs145204276 RT-PCR Lung cancer 600 600 0.07 Leilei Xu 2018 Asian PB rs145204276 RT-PCR Osteosarcoma 132 1270 0.56 Zhansheng Zhu a 2016 Asian PB rs145204276 RT-PCR Colorectal cancer 813 926 0.11 Cervical Zhansheng Zhu b 2017 Asian PB rs145204276 RT-PCR squamous cell 920 1018 0.17 carcinoma Qinbo Yuan 2018 Asian HB rs1902432 T>C TaqMan Prostate cancer 850 860 0.75 rs16901904 T>C 0.01 rs4871771 T>A 0.32 rs710886 A>G 0.49 PCAT1 Yadi Lin I 2017 Asian HB rs1902432 T>C RT-PCR Bladder cancer 578 1006 1.00 rs16901904 T>C 0.60 rs4871771 T>A 0.02 rs710886 A>G 0.12 Yadi Lin II 2017 Asian HB rs710886 A>G RT-PCR Bladder cancer 1028 1381 0.77 Zhenjian Zhuo 2018 Asian PB rs7158663 G>A RT-PCR Neuroblastoma 392 783 0.72 rs4081134 G>A 0.76 Zitai Yang 2018 Asian PB rs7158663 G>A RT-PCR Lung cancer 526 526 0.71 rs4081134 G>A 0.59 MEG3 Qi Zhang 2018 Asian PB rs7158663 G>A TaqMan Gastric cancer 172 224 0.91 rs4081134 G>A 0.17 MALDI-TOF Xiangming Cao 2016 Asian HB rs7158663 G>A mass Colorectal cancer 518 517 0.81 spectrometry Notes: PB: population based; HB: hospital based.

SNP in the lncRNA GAS5 gene and the risk for all types of +del/ins vs. ins/ins genetic models (del vs. ins: OR = 0:96, is shown in Table 2. In the overall analysis, we could 95%CI = 0:65‐2:48; del/del+del/ins vs. ins/ins: OR = 0:94), not obtain an association between the rs145204276 polymor- as shown in Table 2 and Figure 2(a). Nevertheless, we phism and cancer risk in none of the five genetic models. did not detect an association between the rs145204276 However, the stratified analysis by cancer type revealed polymorphism and risks of lung cancer, cervical squamous that rs145204276 was associated with a decreased risk of cell carcinoma, glioma, hepatocellular carcinoma, breast gastric cancer in the del vs. ins genetic model (del vs. ins: cancer, or osteosarcoma. When the analysis was performed OR = 0:79, 95%CI = 0:72‐0:86; del/del vs. ins/ins+del/ins: by ethnicity, no significant correlation was found in Asian OR = 0:74, 95%CI = 0:59‐0:91; del/ins vs. ins/ins: OR = 0:84, and Caucasian ethnicities in all genetic models (Table 2, 95%CI = 0:77‐0:98) (Table 2, Figure 2). A similar result was supplementary material). Moreover, we further performed obtained using the del/del vs. del/ins+ins/ins genetic model subgroup analysis by the source of controls, and the result and the del/ins vs. ins/ins genetic model (Table 2). Although indicated that there was also no association between results showed no significant association in any genetic rs145204276 and cancer risk in either the PB or the HB model between rs145204276 and CRC risk, a decreased subgroups. However, the function of rs145204276 poly- risk trend could still be seen in del vs. ins and del/del morphism was diametrically different in cancer Stage I+II ies Markers Disease

Table 2: Meta-analysis results of the association between rs145204276 and cancer risk.

del vs. ins del/del vs. ins/ins del/del+del/ins vs. ins/ins del/del vs. ins/ins+del/ins del/ins vs. ins/ins Variables Number of studies OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P Overall 12 0.95 (0.80, 1.13)r 0.54 1.09 (0.75, 1.57)r 0.67 0.97 (0.79, 1.19)b 0.78 1.02 (0.76, 1.38)r 0.88 0.94 (0.80,1.100)r 0.43 Cancer type Gastric cancer 3 0.79 (0.72, 0.86)r 0.00 0.90 (0.35, 2.31)r 0.82 0.88 (0.53, 1.44)r 0.60 0.74 (0.59, 0.91)a 0.005 0.84 (0.77, 0.98)r 0.03 Colorectal cancer 3 0.96 (0.65, 1.40)r 0.82 0.96 (0.43, 2.16)r 0.93 0.94 (0.60, 1.45)r 0.77 0.99 (0.52, 1.87)r 0.98 0.93 (0.65, 1.34)r 0.71 Others 6 1.06 (0.82, 1.36)r 0.68 1.24 (0.72, 2.14)r 0.93 1.03 (0.76, 1.40)r 0.84 1.25 (0.81, 1.91)r 0.31 1.07 (0.97, 1.17)r 0.16 Ethnicity Chinese 11 0.98 (0.82, 1.17)r 0.79 1.15 (0.79, 1.68)r 0.47 1.01 (0.82, 1.24)r 0.94 1.06 (0.79, 1.44)r 0.69 0.96 (0.82, 1.13)r 0.63 Iran 1 0.61 (0.42, 0.89)r — 0.43 (0.17, 1.11)r — 0.58 (0.37, 3.91)r — 0.51 (0.20, 1.30)r — 0.61 (0.38, 0.99)r — Source of controls PB 10 0.92 (0.76, 1.11)r 0.38 1.05 (0.70, 1.57)r 0.80 0.95 (0.76, 1.17)r 0.62 0.99 (0.71, 1.36)r 0.93 0.91 (0.78, 1.08)r 0.28 HB 2 1.10 (0.57, 2.15)r 0.77 1.30 (0.34, 4.97)r 0.71 1.12 (0.49, 2.53)r 0.79 1.23 (0.45, 3.36)r 0.68 1.09 (0.54, 2.20)r 0.82 Cancer stage Stage I+II 7 1.05 (0.86, 1.28)r 0.62 1.16 (0.80, 1.66)r 0.44 1.06 (0.81,1.37)r 0.68 1.18 (0.89, 1.56)r 0.24 1.01 (0.75 1.36)vr 0.94 Stage III+IV 7 0.75 (0.50, 1.11)r 0.15 0.76 (0.35, 1.61)r 0.47 0.70 (0.44, 1.13)r 0.14 0.94 (0.54, 1.63)r 0.82 0.68 (0.43,1.08)r 0.10 Notes: r: calculating based on a random-effects model. 5 6 Disease Markers

Study % ID OR (95% CI) Weight

Gastric cancer Keyvan Aminian (2018) 0.61 (0.42, 0.89) 6.47 Qianjun Li a (2018) 0.81 (0.70, 0.94) 8.69 Qianjun Li b (2018) 0.79 (0.70, 0.90) 8.85 Subtotal (I2 = 0.0%, P = 0.392) 0.79 (0.72, 0.86) 24.01 . Colorectal cancer Yongbin Zheng I (2016) 0.81 (0.68, 0.96) 8.45 Yongbin Zheng II (2016) 0.78 (0.67, 0.91) 8.64 Zhansheng Zhu a (2016) 1.38 (1.20, 1.60) 8.71 Subtotal (I2 = 94.5%, P < 0.01) 0.96 (0.65, 1.40) 25.81 . Other Jupeng Yuan (2018) 1.55 (1.30, 1.85) 8.42 Ruiyang Tao (2015) 1.36 (1.19, 1.54) 8.80 Yiyin Tang (2018) 0.79 (0.66, 0.94) 8.43 Weihao Li (2017) 0.79 (0.67, 0.94) 8.48 Leilei Xu (2018) 0.71 (0.53, 0.96) 7.29 Zhansheng Zhu b (2017) 1.38 (1.21, 1.58) 8.76 Subtotal (I2 = 92.8%, P < 0.01) 1.06 (0.82, 1.36) 50.18 . Overall (I2 = 92.7%, P < 0.01) 0.95 (0.80, 1.13) 100.00

Note: weights are from random-effects analysis

.422 1 2.37 (a)

Study % ID OR (95% CI) Weight

Gastric cancer Keyvan Aminian (2018) 0.51 (0.20, 1.30) 4.99 Qianjun Li a (2018) 0.75 (0.53, 1.06) 8.85 Qianjun Li b (2018) 0.76 (0.57, 1.01) 9.22 Subtotal (I2 = 0.0%, P = 0.727) 0.74 (0.59, 0.91) 23.06 . Colorectal cancer Yongbin Zheng I (2016) 0.78 (0.53, 1.15) 8.58 Yongbin Zheng II (2016) 0.68 (0.48, 0.96) 8.89 Zhansheng Zhu a (2016) 1.81 (1.32, 2.47) 9.06 Subtotal (I2 = 90.1%, P < 0.01) 0.99 (0.52, 1.87) 26.53 . Other Jupeng Yuan (2018) 2.05 (1.38, 3.06) 8.51 Ruiyang Tao (2015) 1.86 (1.39, 2.47) 9.21 Yiyin Tang (2018) 0.74 (0.49, 1.11) 8.50 Weihao Li (2017) 0.67 (0.45, 1.01) 8.47 Leilei Xu (2018) 0.86 (0.44, 1.68) 6.61 Zhansheng Zhu b (2017) 2.00 (1.47, 2.71) 9.11 Subtotal (I2 = 86.4%, P < 0.01) 1.25 (0.81, 1.91) 50.41 . Overall (I2 = 86.5%, P < 0.01) 1.02 (0.76, 1.38) 100.00

Note: weights are from random-effects analysis

.199 1 5.03 (b)

Figure 2: Continued. Disease Markers 7

Study % ID OR (95% CI) Weight

Gastric cancer Keyvan Aminian (2018) 0.61 (0.38, 0.99) 5.43 Qianjun Li a (2018) 0.79 (0.65, 0.96) 9.05 Qianjun Li b (2018) 0.99 (0.83, 1.18) 9.26 Subtotal (I2 = 60.4%, P = 0.080) 0.84 (0.67, 1.05) 23.74 . Colorectal cancer Yongbin Zheng I (2016) 0.78 (0.61, 0.99) 8.46 Yongbin Zheng II (2016) 0.78 (0.64, 0.96) 8.89 Zhansheng Zhu a (2016) 1.33 (1.08, 1.62) 8.97 Subtotal (I2 = 88.0%, P < 0.01) 0.93 (0.65, 1.34) 26.32 . Other Jupeng Yuan (2018) 1.56 (1.21, 2.01) 8.26 Ruiyang Tao (2015) 1.25 (1.04, 1.50) 9.20 Yiyin Tang (2018) 0.76 (0.60, 0.97) 8.43 Weihao Li (2017) 0.79 (0.63, 1.00) 8.49 Leilei Xu (2018) 0.60 (0.40, 0.88) 6.44 Zhansheng Zhu b (2017) 1.26 (1.04, 1.52) 9.11 Subtotal (I2 = 86.3%, P < 0.01) 1.00 (0.77, 1.29) 49.94 . Overall (I2 = 83.1%, P < 0.01) 0.94 (0.80, 1.10) 100.00

Note: weights are from random-effects analysis

.381 1 2.62 (c) Begg’s funnel plot with pseudo 95% confidence limits .5

0 logor

–.5

0.1.2 s.e. of: logor (d)

Figure 2: Forest plots of the association between rs145204276 and cancer risk in subgroup analysis by cancer types under three genetic models. (a) del vs. ins genetic model; (b) del/del vs. del/ins+ins/ins genetic model; (c) del/ins vs. ins/ins genetic model; (d) Begg’s funnel plot of rs145204276 in del vs. ins genetic model. OR: odds ratios; 95% CI: 95% confidence intervals. and Stage III+IV. In detail, an increased risk trend could 3.2.2. rs7158663 and rs4081134 SNPs in the lncRNA MEG3 be seen in all genetic models in cancer Stage I+II, and a Gene and Cancer Susceptibility. Four of the included studies decreased risk trend could be seen in all genetic models were evaluated to determine the association between in cancer Stage III+IV (Table 2, supplementary material). rs7158663 and cancer risk. However, the results showed 8 Disease Markers no significant association in any genetic model between article, we selected all published articles in PubMed, Web of rs7158663 and cancer risk by random-effects models Science, Embase, Google Scholar, Cochrane Library, CNKI, (Figure 3(b)). Three studies were used to detect the associa- and CBM and included polymorphisms of the lncRNAs tion between rs4081134 and cancer risk. Results showed that GAS5, MEG3, and PCAT-1 in our meta-analysis. The results no significant association was found in any genetic model indicated that two of these lncRNA polymorphisms (GAS5 between rs4081134 and cancer risk by random-effects rs145204276 and PCAT1 rs16901904) may contribute to a models. However, a reduced risk trend could be verified decreased cancer risk. (A vs. G: OR = 0:97, 95%CI = 0:71‐1:31; AA vs. GG: OR = Recently, lncRNA GAS5 has attracted attention as a 0:86, 95%CI 0:46‐1:63; AG vs. GG: OR = 0:98, 95%CI = 0:73 new type of lncRNA which plays a key role in cancer ‐1:33; AA+AG vs. GG: OR = 0:97, 95%CI = 0:69‐1:36; AA vs. development. Moreover, the 5 bp indel polymorphism AG+GG: OR = 0:87, 95%CI = 0:51‐1:47) (Figure 3(a)). (rs145204276) in the lncRNA GAS5 promoter region has also been studied in detail in different types of tumors. How- 3.2.3. rs1902432, rs16901904, rs4871771, and rs710886 ever, we could not obtain an association between the Variations of the lncRNA PCAT-1 Gene and Cancer rs145204276 polymorphism and cancer risk in none of the Susceptibility. For lncRNA PCAT-1 polymorphisms, we five genetic models in the overall analysis. The results of the ff mainly focused on investigating the e ects of four lncRNA subgroup analysis by cancer type revealed that the polymorphisms (rs16901904, rs710886, rs4871771, and rs145204276 del allele was associated with a decreased risk rs1902432). As shown in Figure 4(a), for rs16901904, of gastric cancer (del vs. ins: OR = 0:79, 95%CI = 0:72‐0:86; decreased cancer risks were observed in three genetic models del/del vs. ins/ins+del/ins: OR = 0:74, 95%CI = 0:59‐0:91; fi ff OR = 0:79 95%CI = 0:70‐ by xed-e ects models (C vs. T: , del/ins vs. ins/ins: OR = 0:84, 95%CI = 0:77‐0:98). Neverthe- 0:90 OR = 0:49 95%CI = 0:37‐0:65 ; CC vs. CT+TT: , ; CC vs. less, we did not detect an association of the rs145204276 OR = 0:49 95%CI = 0:37‐0:66 TT: , ). Similar associations polymorphism with risks for other cancers. Interestingly, fi were found in rs710886, although not statistically signi cant the mechanism of action of the rs145204276 polymorphism (GG vs. AA: OR = 0.99, 95% CI = 0.66-1.47; GA vs. AA: in cancer cells has also not yet been completely elucidated. OR = 0.94, 95% CI = 0.82-1.07; GG + AG vs. AA: OR = 0.96, On the one hand, researchers observed that the del polymor- 95% CI = 0.79-1.15), shown as Figure 4(b). However, there phism rs145204276 may influence GAS5 transcriptional fi was no signi cant association between rs4871771 or activity by affecting the methylation status of a CpG island fi rs1902432 and cancer risk in any genetic models by xed- in the promoter region of GAS5, which affects its tumor- ff ff e ects or random-e ects models (shown in supplementary suppressing function [34]. The rs145204276 del allele could material (available here)). also induce promoter activity by binding to SP1 and enhanc- ing the expression level of lncRNA GAS5, which results in a 3.3. Metaregression Analysis. We observed a significant het- higher risk factor for the development of breast cancer [17]. erogeneity among studies in the meta-analysis on the On the other hand, further studies showed that the rs145204276 polymorphism. We hence performed a metare- overexpression of GAS5 induced by indel polymorphism gression analysis to assess the source of heterogeneity in the rs145204276 can inhibit the expression of miR-221 and del vs. ins genetic model (Table 3). The cancer type, source miR-182-5p, thereby reducing the proliferation, migration, of controls, ethnicity, and genotyping method were detected, and invasion properties of cancer [19]. Altogether, these but no statistical significance was found. Therefore, when 2 conflicting results may be explained by the fact that the obvious heterogeneity (I value > 75%) was observed in the susceptibility loci are different in different cancer types. The overall or subgroup analyses, the random-effects model was mechanism of action of GAS5 in cancer is still poorly under- used to make stable confidence intervals. Otherwise, the stood, which also requires further studies. fixed-effects model was selected to conduct data analysis. lncRNA MEG3 has been demonstrated to be abnormally 3.4. Publication Bias. Begg’s funnel plot and Egger’s test were expressed in various human cancers, such as bladder cancer, performed to examine publication bias in this meta-analysis. glioma, and gastric cancer [31]. Previous researches revealed For all included SNPs, no evidence of publication bias was that MEG3 markedly inhibited cell growth via the induction found in all genotype models. One of the publication bias test of G2/M cell cycle arrest, cell apoptosis, and the reduction of results is shown as Figure 2(d); in the del vs. ins genetic miR-21-5p content in cervical cancer [35]. Braconi et al. model, the P value of Begg’s funnel plot test was 0.78. found that abnormal expression of MEG3 induced apoptosis in hepatocellular cancer PRC/PRF/5 cells [36]. Another study 4. Discussion result showed that the downregulation of lncRNA MEG3 induced nickel malignant transformation of human bron- In recent years, lncRNAs have been confirmed to play impor- chial epithelial cells through PHLPP1 transcription and tant regulatory roles in gene expression, and their aberrant HIF-1α translation [37]. Moreover, the SNPs in MEG3 also expression has been recognized as a hallmark feature of can- participate in the development of different types of cancer. cer [33]. SNPs have a wide distribution and can be found in For example, Cao et al. discovered that rs7158663 in MEG3 any gene or mRNA regions, and researchers are increasingly had a strong association with an increased risk of CRC [29]. paying attention to the important role of lncRNA SNPs in Studies also demonstrated that rs4081134 in MEG3 was cancer [6]. Thus, it is necessary to identify the association associated with lung cancer susceptibility in a hospital-based between lncRNA polymorphisms and cancer risk. In this case-control study [30]. However, associations between the Disease Markers 9

Study % ID OR (95% CI) Weight

A vs. G ZhenJian Zhuo (2018) 1.18 (0.98, 1.44) 36.36 Zitai Yang (2018) 0.75 (0.61, 0.93) 35.32 Qi Zhang (2018) 1.02 (0.73, 1.42) 28.33 Subtotal (I2 = 79.4%, P = 0.008) 0.97 (0.71, 1.31) 100.00 . AA+AG vs. GG ZhenJian Zhuo (2018) 1.25 (0.98, 1.60) 36.52 Zitai Yang (2018) 0.75 (0.58, 0.97) 36.07 Qi Zhang (2018) 0.96 (0.64, 1.44) 27.41 Subtotal (I2 = 75.5%, P = 0.017) 0.97 (0.69, 1.36) 100.00 . AA vs. AG+GG ZhenJian Zhuo (2018) 1.19 (0.73, 1.94) 40.21 Zitai Yang (2018) 0.52 (0.28, 0.96) 33.37 Qi Zhang (2018) 1.03 (0.47, 2.23) 26.42 Subtotal (I2 = 54.1%, P = 0.113) 0.87 (0.51, 1.47) 100.00 . AA vs. GG ZhenJian Zhuo (2018) 1.30 (0.79, 2.15) 38.14 Zitai Yang (2018) 0.48 (0.26, 0.90) 33.72 Qi Zhang (2018) 1.01 (0.46, 2.22) 28.14 Subtotal (I2= 66.8%, P = 0.049) 0.86 (0.46, 1.63) 100.00 . AG vs. GG ZhenJian Zhuo (2018) 1.24 (0.96, 1.60) 37.78 Zitai Yang (2018) 0.80 (0.61, 1.03) 37.16 Qi Zhang (2018) 0.95 (0.62, 1.46) 25.06 Subtotal (I2 = 65.6%, P = 0.055) 0.98 (0.73, 1.33) 100.00 Note: weights are from random-effects analysis

.257 1 3.9 (a)

Study % ID OR (95% CI) Weight

A vs. G ZhenJian Zhuo (2018) 0.84 (0.69, 1.03) 26.23 Xiangming Cao (2016) 1.31 (1.08, 1.59) 26.54 Zitai Yang (2018) 1.14 (0.94, 1.38) 26.66 Qi Zhang (2018) 1.59 (1.15, 2.19) 20.56 Subtotal (I2 = 79.8%, P = 0.002) 1.17 (0.91, 1.49) 100.00 . AA vs. AG+GG ZhenJian Zhuo (2018) 0.65 (0.38, 1.12) 25.67 Xiangming Cao (2016) 1.76 (1.11, 2.79) 27.78 Zitai Yang (2018) 1.20 (0.74, 1.93) 27.35 Qi Zhang (2018) 0.49 (0.21, 1.12) 19.20 Subtotal (I2 = 73.4%, P = 0.010) 0.96 (0.56, 1.64) 100.00 . AA+AG vs. GG ZhenJian Zhuo (2018) 0.84 (0.66, 1.08) 26.45 Xiangming Cao (2016) 1.30 (1.02, 1.66) 26.47 Zitai Yang (2018) 1.17 (0.92, 1.50) 26.58 Qi Zhang (2018) 0.58 (0.39, 0.87) 20.50 Subtotal (I2 = 79.7%, P = 0.002) 0.96 (0.71, 1.30) 100.00 . AA vs. GG ZhenJian Zhuo (2018) 0.62 (0.36, 1.08) 25.66 Xiangming Cao (2016) 1.89 (1.18, 3.04) 27.12 Zitai Yang (2018) 1.27 (0.78, 2.09) 26.81 Qi Zhang (2018) 0.40 (0.17, 0.93) 20.40 Subtotal (I2 = 79.9%, P = 0.002) 0.93 (0.49, 1.76) 100.00 . AG vs. GG ZhenJian Zhuo (2018) 0.89 (0.69, 1.14) 27.21 Xiangming Cao (2016) 1.20 (0.93, 1.56) 27.04 Zitai Yang (2018) 1.16 (0.90, 1.49) 27.40 Qi Zhang (2018) 0.62 (0.41, 0.94) 18.35 Subtotal (I2 = 67.1%, P = 0.028) 0.97 (0.75, 1.24) 100.00 Note: weights are from random-effects analysis

.172 1 5.81 (b)

Figure 3: Forest plots of the associations between rs7158663 and rs4081134 and cancer risk in five genetic models. (a) Association between rs4081134 and cancer risk; (b) association between rs7158663 and cancer risk. OR: odds ratios; 95% CI: 95% confidence intervals. 10 Disease Markers

Study % ID OR (95% CI) Weight C vs. T Qinbo Yuan (2018) 0.85 (0.72, 1.01) 49.59 Yadi Lin (2017) 0.74 (0.62, 0.88) 50.41 Subtotal (I2 = 25.9%, P = 0.245) 0.79 (0.70, 0.90) 100.00 . CC vs. TT+CT Qinbo Yuan (2018) 0.57 (0.38, 0.85) 46.19 Yadi Lin (2017) 0.42 (0.27, 0.64) 53.81 Subtotal (I2 = 7.9%, P = 0.298) 0.49 (0.37, 0.65) 100.00 . CC+CT vs. TT Qinbo Yuan (2018) 0.92 (0.76, 1.13) 50.83 Yadi Lin (2017) 0.82 (0.67, 1.02) 49.17 Subtotal (I2 = 0.0%, P = 0.445) 0.88 (0.76, 1.01) 100.00 . CC vs. TT Qinbo Yuan (2018) 0.58 (0.39, 0.86) 46.32 Yadi Lin (2017) 0.42 (0.27, 0.64) 53.68 Subtotal (I2 = 13.9%, P = 0.281) 0.49 (0.37, 0.66) 100.00 . CT vs. TT Qinbo Yuan (2018) 1.03 (0.83, 1.28) 51.58 Yadi Lin (2017) 0.98 (0.78, 1.23) 48.42 Subtotal (I2= 0.0%, P = 0.755) 1.01 (0.86, 1.18) 100.00 .

.271 1 3.69 (a) Study % ID OR (95% CI) Weight

G vs. A Qinbo Yuan (2018) 1.06 (0.92, 1.21) 33.15 Yadi Lin Stage I (2017) 1.17 (1.01, 1.35) 32.39 Yadi Lin Stage II (2017) 0.83 (0.74, 0.93) 34.47 Subtotal (I2 = 86.8%, P = 0.001) 1.00 (0.82, 1.24) 100.00 . GG vs. AA+AG Qinbo Yuan (2018) 1.11 (0.89, 1.38) 33.03 Yadi Lin Stage I (2017) 1.33 (1.06, 1.67) 32.89 Yadi Lin Stage II (2017) 0.74 (0.61, 0.90) 34.08 Subtotal (I2 = 87.5%, P < 0.01) 1.03 (0.72, 1.46) 100.00 . GG+AG vs. AA Qinbo Yuan (2018) 1.04 (0.84, 1.30) 32.90 Yadi Lin Stage I (2017) 1.08 (0.84, 1.39) 28.73 Yadi Lin Stage II (2017) 0.81 (0.68, 0.97) 38.37 Subtotal (I2 = 55.7%, P = 0.105) 0.96 (0.79, 1.15) 100.00 . GG vs. AA Qinbo Yuan (2018) 1.12 (0.85, 1.46) 33.14 Yadi Lin Stage I (2017) 0.30 (0.97, 1.74) 32.28 Yadi Lin Stage II (2017) 0.68 (0.54, 0.86) 34.58 Subtotal (I2 = 85.3%, P = 0.001) 0.99 (0.66, 1.47) 100.00 . GA vs. AA Qinbo Yuan (2018) 1.01 (0.80, 1.27) 31.12 Yadi Lin Stage I (2017) 0.97 (0.74, 1.26) 23.43 Yadi Lin Stage II (2017) 0.88 (0.72, 1.06) 45.45 Subtotal (I2 = 0.0%, P = 0.636) 0.94 (0.82, 1.07) 100.00 Note: weights are from random-effects analysis

.541 1 1.85 (b)

Figure 4: Forest plots of the association between rs16901904 and rs710886 and cancer risk in five genetic models. (a) Association between rs16901904 and cancer risk; (b) association between rs710886 and cancer risk. OR: odds ratios; 95% CI: 95% confidence intervals. Disease Markers 11

Table 3: Metaregression analysis of the GAS5 rs145204276 on cancer risk (del vs. ins genetic model).

SNP Variables Coefficient Standard error 95% CI P Source of controls -0.10 0.34 -0.86-0.67 0.79 GAS5 Ethnicity 0.55 0.48 -0.49-1.59 0.27 rs145204276 Cancer type 0.14 0.15 -0.20-0.47 0.38 Genotyping method 0.11 0.20 -0.32-0.55 0.57

MEG3 rs7158663 polymorphism and lung cancer susceptibil- populations. However, several limitations still exist in our ity have not been reported [30]. Neither rs7158663 nor analysis, so results should be regarded with caution. Larger rs4081134 significantly modifies the neuroblastoma risk and multiple ethnicity studies should be included in our [31]. As a result, only a reduced risk trend was observed further studies. between rs4081134 and cancer in our meta-analysis. lncRNA PCAT-1 is a long intergenic noncoding RNA Data Availability transcript from the 8q24.21 region [38]. It has also been suggested as related to various carcinomas, such as gastric All data generated or analyzed during this study are included bladder [38, 39]. Previous studies confirmed that lncRNA in this published article. PCAT-1 could promote cell proliferation through binding to polycomb repressive complex 2 (PCR2) as a transcrip- Conflicts of Interest tional repressor [40]. We also verified that the posttranscrip- tional silencing of PCAT-1 by miR-215 or PCAT-1 siRNAs We declared no conflicts of interest related to this study. significantly inhibited HCC cell proliferation and, con- versely, that the inhibition of endogenous miR-215 upregu- Authors’ Contributions lated PCAT-1 expression and promoted cell viability [41]. Additionally, the restoration of miR-145-5p attenuated the Xiaoyan Dong and Wenyan Gao contributed equally to induction effects of PCAT1 on prostate cancer progression this work. [42]. However, SNPs in lncRNA PCAT-1 are varied and also influence susceptibility to different types of cancer. As a Acknowledgments result of our analysis, an association with a decreased cancer risk of rs16901904 was observed in three genetic models This study was supported by the National Natural Science (C vs. T, CC vs. CT+TT, and CC vs. TT). Similar associations Foundation of China (81771520, 31201040, 31700307, and were observed in rs710886, although not statistically signifi- 31702144), the Science Technology Department of Zhejiang cant. This finding still needs to be further studied. Province (2016C34002), the Natural Science Foundation of Although significant associations between rs145204276 Zhejiang Province (LQ16H020006 and LY16H20002), funds and rs16901904 and cancer risk were observed in this meta- from the Health Bureau of Zhejiang Province (WKJ2011-2- analysis, our current work still presents several limitations. 014, 2015DTA001, 2015KYA001, 2019KY257, 2019RC091, Firstly, there is a high degree of heterogeneity among the 2019KY263, 2019RC093, 2017KY188, and 2017KY189), and included studies on rs145204276. Although we stratified the Technology Projects of Zhejiang Province (2017ZB002). our analysis by cancer type, ethnicity, and source of controls, Dr. Genxiang Mao was an Irma and Paul Milstein Program between-study heterogeneity was still obvious. Metaregres- for Senior Health fellow supported by the MMAAP Founda- sion also revealed that the source of heterogeneity was not tion (http://www.mmaapf.org). the ethnicity, genotyping method, cancer type, or source of controls. Secondly, due to lack of original data of the included Supplementary Materials studies, the evaluation of associations between age, gender, Supplement Figure 1: the forest plot of the association between family history, or environmental factors and cancer risk PCAT1 at rs1902432 and cancer risk in five genetic models. was not conducted. Thirdly, there was only one study in OR: odds ratios; 95% CI: 95% confidence intervals. Weights Caucasian population, and the results of this meta-analysis are from random-effects analysis. Supplementary Figure 2: could only be suitable for the Chinese population. Finally, the forest plot of the association between PCAT1 rs4871771 some sample sizes of the included studies were small, which and cancer risk in five genetic models. OR: odds ratios; 95% may influence the results’ stability. CI: 95% confidence intervals. Weights are from random- effects analysis. (Supplementary Materials) 5. Conclusions References Our results indicate that rs145204276 in lncRNA GAS5 is a protective factor in the development of gastric cancer, and [1] J. Ferlay, M. Colombet, I. Soerjomataram et al., “Cancer inci- the role of rs145204276 was also distinct in different cancer dence and mortality patterns in Europe: estimates for 40 coun- stages. In addition, rs16901904 in lncRNA PCAT-1 also plays tries and 25 major cancers in 2018,” European Journal of a protective role against cancer development in Chinese Cancer, vol. 103, pp. 356–387, 2018. 12 Disease Markers

[2] S. Pilleron, D. Sarfati, M. Janssen-Heijnen et al., “Global cancer [17] Y. Tang, Y. Wang, X. Wang, Y. Liu, and K. Zheng, “A Genetic incidence in older adults, 2012 and 2035: a population-based Variant of rs145204276 in the Promoter Region of Long Non- study,” International Journal of Cancer, vol. 144, no. 1, coding RNA _GAS5_ Is Associated With a Reduced Risk of pp. 49–58, 2019. Breast Cancer,” Clinical Breast Cancer, vol. 19, no. 3, – [3] F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, pp. e415 e421, 2019. and A. Jemal, “Global cancer statistics 2018: GLOBOCAN esti- [18] R. Tao, S. Hu, S. Wang et al., “Association between indel poly- mates of incidence and mortality worldwide for 36 cancers in morphism in the promoter region of lncRNA GAS5 and the 185 countries,” CA: A Cancer Journal for Clinicians, vol. 68, risk of hepatocellular carcinoma,” Carcinogenesis, vol. 36, no. 6, pp. 394–424, 2018. no. 10, pp. 1136–1143, 2015. [4] Y. Hou, B. Zhang, L. Miao et al., “Association of long non‐ [19] L. Xu, C. Xia, B. Xue, F. Sheng, J. Xiong, and S. Wang, “A pro- coding RNA MEG3 polymorphisms with oral squamous cell moter variant of lncRNA GAS5 is functionally associated with carcinoma risk,” Oral Diseases, vol. 25, no. 5, pp. 1318– the development of osteosarcoma,” Journal of Bone Oncology, 1324, 2019. vol. 12, pp. 23–26, 2018. [5] S. Jathar, V. Kumar, J. Srivastava, and V. Tripathi, “Technolog- [20] J. Yuan, N. Zhang, Y. Zheng, Y. D. Chen, J. Liu, and M. Yang, ical developments in lncRNA biology,” Advances in Experi- “LncRNAGAS5Indel genetic polymorphism contributes to gli- mental Medicine and Biology, vol. 1008, pp. 283–323, 2017. oma risk through interfering binding of transcriptional factor ” – [6] S. Lu, Z. Su, W. Fu, Z. Cui, X. Jiang, and S. Tai, “Altered expres- TFAP2A, DNA and Cell Biology, vol. 37, no. 9, pp. 750 757, sion of long non-coding RNA GAS5 in digestive tumors,” Bio- 2018. science Reports, vol. 39, no. 1, 2019. [21] Y. Zheng, D. Song, K. Xiao et al., “LncRNA GAS5 contributes ” [7] L. Minotti, C. Agnoletto, F. Baldassari, F. Corrà, and S. Volinia, to lymphatic metastasis in colorectal cancer, Oncotarget, – “SNPs and somatic mutation on long non-coding RNA: new vol. 7, no. 50, pp. 83727 83734, 2016. frontier in the cancer studies?,” High Throughput, vol. 7, [22] Z. Zhu, L. Feng, F. Li, Y. Xue, C. Li, and H. Wang, “A novel no. 4, p. 34, 2018. functional indel polymorphism within long non-coding RNAs fi [8] V. Vaidyanathan, V. Naidu, N. Karunasinghe et al., “Effect of growth arrest speci c 5 conferred risk for cervical squamous ” ageing and single nucleotide polymorphisms associated with cell carcinoma in Chinese Han populations, Translational – the risk of aggressive prostate cancer in a New Zealand popu- Cancer Research, vol. 6, no. 2, pp. 424 431, 2017. lation,” Molecular BioSystems, vol. 13, no. 10, pp. 1967–1980, [23] Z. Zhu, Y. Xue, W. Fu et al., “Functional indel polymorphism 2017. within lncRNA GAS5 and colorectal carcinoma risk,” Interna- [9] K. Takahashi, I. Yan, H. Haga, and T. Patel, “Long noncoding tional Journal Of Clinical And Experimental Pathology, vol. 9, RNA in liver diseases,” Hepatology, vol. 60, no. 2, pp. 744–753, 2016. 2014. [24] T. Li, X. Mo, L. Fu, B. Xiao, and J. Guo, “Molecular mecha- ” [10] Y. Yang, Z. Shen, Y. Yan et al., “Long non-coding RNA GAS5 nisms of long noncoding RNAs on gastric cancer, Oncotarget, – inhibits cell proliferation, induces G0/G1 arrest and apoptosis, vol. 7, no. 8, pp. 8601 8612, 2016. and functions as a prognostic marker in colorectal cancer,” [25] T. Balci, S. Yilmaz Susluer, C. Kayabasi, B. Ozmen Yelken, Oncology Letters, vol. 13, no. 5, pp. 3151–3158, 2017. C. Biray Avci, and C. Gunduz, “Analysis of dysregulated long ” [11] J. Li, Y. Wang, C. G. Zhang et al., “Effect of long non-coding non-coding RNA expressions in glioblastoma cells, Gene, – RNA Gas5 on proliferation, migration, invasion and apoptosis vol. 590, no. 1, pp. 120 122, 2016. of colorectal cancer HT-29 cell line,” Cancer Cell International, [26] B. S. He, H. L. Sun, T. Xu et al., “Association of genetic vol. 18, 2018. polymorphisms in the lncRNAs with gastric cancer risk in a ” [12] Y. Liu, J. Zhao, W. Zhang et al., “lncRNA GAS5 enhances G1 Chinese population, Journal of Cancer, vol. 8, no. 4, – cell cycle arrest via binding to YBX1 to regulate p21 expression pp. 531 536, 2017. in stomach cancer,” Scientific Reports, vol. 5, 2015. [27] Y. Lin, Y. Ge, Y. Wang et al., “The association of rs710886 in “ lncRNA _PCAT1_ with bladder cancer risk in a Chinese pop- [13] K. Aminian, F. Mashayekhi, L. Mirzanejad, and Z. Salehi, A ” – functional genetic variant in GAS5 lncRNA (rs145204276) ulation, Gene, vol. 627, pp. 226 232, 2017. modulates p27Kip1expression and confers risk for gastric can- [28] Q. Yuan, H. Chu, Y. Ge et al., “LncRNAPCAT1and its genetic cer,” British Journal of Biomedical Science, vol. 76, no. 2, variant rs1902432 are associated with prostate cancer risk,” pp. 83–85, 2018. Journal of Cancer, vol. 9, no. 8, pp. 1414–1420, 2018. [14] Q. Li, G. Ma, S. Sun, Y. Xu, and B. Wang, “Polymorphism in [29] X. Cao, S. Zhuang, Y. Hu et al., “Associations between the promoter region of lncRNA GAS5 is functionally associ- polymorphisms of long non-coding RNA MEG3 and risk of ated with the risk of gastric cancer,” Clinics and Research in colorectal cancer in Chinese,” Oncotarget, vol. 7, no. 14, Hepatology and Gastroenterology, vol. 42, no. 5, pp. 478–482, pp. 19054–19059, 2016. 2018. [30] Z. Yang, H. Li, J. Li et al., “Association between long noncod- [15] Q. J. Li, G. Ma, H. M. Guo, S. H. Sun, Y. Xu, and B. J. Wang, ing RNAMEG3Polymorphisms and lung cancer susceptibility “The variant rs145204276 of GAS5 is associated with the in Chinese northeast population,” DNA and Cell Biology, development and prognosis of gastric cancer,” Journal of Gas- vol. 37, no. 10, pp. 812–820, 2018. trointestinal and Liver Diseases, vol. 27, no. 1, pp. 19–24, [31] Z. J. Zhuo, R. Zhang, J. Zhang et al., “Associations between 2018. lncRNA MEG3 polymorphisms and neuroblastoma [16] W. Li, K. Huang, F. Wen, G. Cui, H. Guo, and S. Zhao, risk in Chinese children,” Aging, vol. 10, no. 3, pp. 481–491, “Genetic variation of lncRNA GAS5 contributes to the devel- 2018. opment of lung cancer,” Oncotarget, vol. 8, no. 53, [32] Z. Qi, A. Liang, and D. Youguo, “Association between poly- pp. 91025–91029, 2017. morphism of long non-coding RNA maternally expressed Disease Markers 13

gene 3 and risk of gastric cancer,” Chinese Journal of Bases and Clinics in General Surgery, vol. 25, pp. 1323–1326, 2018. [33] K. S. Hung, C. C. Hsiao, T. W. Pai et al., “Functional enrich- ment analysis based on long noncoding RNA associations,” BMC Systems Biology, vol. 12, no. S4, p. 45, 2018. [34] S. Chen, Y. He, J. Ding et al., “An insertion/deletion polymor- phism in the 3′ untranslated region of β-transducin repeat- containing protein (β TrCP) is associated with susceptibility for hepatocellular carcinoma in Chinese,” Biochem Biophys Res Commun, vol. 391, no. 1, pp. 552–556, 2010. [35] L. Peng, X. Yuan, B. Jiang, Z. Tang, and G. C. Li, “LncRNAs: key players and novel insights into cervical cancer,” Tumor Biology, vol. 37, no. 3, pp. 2779–2788, 2016. [36] C. Braconi, T. Kogure, N. Valeri et al., “microRNA-29 can reg- ulate expression of the long non-coding RNA gene _MEG3_ in hepatocellular cancer,” Oncogene, vol. 30, no. 47, pp. 4750– 4756, 2011. [37] C. Zhou, C. Huang, J. Wang et al., “LncRNA _MEG3_ down- regulation mediated by DNMT3b contributes to nickel malig- nant transformation of human bronchial epithelial cells via modulating PHLPP1 transcription and HIF-1 α translation,” Oncogene, vol. 36, no. 27, pp. 3878–3889, 2017. [38] W. H. Shi, Q. Q. Wu, S. Q. Li et al., “Upregulation of the long noncoding RNA PCAT-1 correlates with advanced clinical stage and poor prognosis in esophageal squamous carcinoma,” Tumor Biology, vol. 36, no. 4, pp. 2501–2507, 2015. [39] W. C. Cui, Y. F. Wu, and H. M. Qu, “Up-regulation of long non-coding RNA PCAT-1 correlates with tumor progression and poor prognosis in gastric cancer,” European Review for Medical and Pharmacological Sciences, vol. 21, no. 13, pp. 3021–3027, 2017. [40] E. Saus, A. Brunet-Vega, S. Iraola-Guzman, C. Pegueroles, T. Gabaldon, and C. Pericay, “Long non-coding RNAs as potential novel prognostic biomarkers in colorectal cancer,” Frontiers in Genetics, vol. 7, 2016. [41] Y. Ren, J. Shang, J. Li et al., “The long noncoding RNAPCAT- 1links the microRNA miR-215 to oncogene CRKL-mediated signaling in hepatocellular carcinoma,” Journal of Biological Chemistry, vol. 292, no. 43, pp. 17939–17949, 2017. [42] W. Xu, J. Chang, X. Du, and J. Hou, “Long non-coding RNA PCAT-1 contributes to tumorigenesis by regulating FSCN1 via miR-145-5p in prostate cancer,” Biomedicine & Pharmaco- therapy, vol. 95, pp. 1112–1118, 2017.