OR Oncology Reports 1021-335X 1791-2431 D.A. Spandidos 10.3892/or.2018.6410 or-40-01-0226 Articles Microarray-based bioinformatics analysis of the prospective target gene network of key miRNAs influenced by long non-coding RNA PVT1 in HCC ZhangYu 1 * MoWei-Jia 1 * WangXiao 2 ZhangTong-Tong 1 QinYuan 1 WangHan-Lin 1 ChenGang 1 WeiDan-Ming 1 DangYi-Wu 1 Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China Department of Orthopedics, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250012, P.R. China Correspondence to: Dr Dan-Ming Wei or Dr Yi-Wu Dang, Department of Pathology, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China, E-mail: danmingwei08@163.com, E-mail: dangyiwu@126.com

Contributed equally

 072018 02052018 40 1 226 240 26092017 17042018 Copyright: © Zhang et al. 2018 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

The long non-coding RNA (lncRNA) PVT1 plays vital roles in the tumorigenesis and development of various types of cancer. However, the potential expression profiling, functions and pathways of PVT1 in HCC remain unknown. PVT1 was knocked down in SMMC-7721 cells, and a miRNA microarray analysis was performed to detect the differentially expressed miRNAs. Twelve target prediction algorithms were used to predict the underlying targets of these differentially expressed miRNAs. Bioinformatics analysis was performed to explore the underlying functions, pathways and networks of the targeted genes. Furthermore, the relationship between PVT1 and the clinical parameters in HCC was confirmed based on the original data in the TCGA database. Among the differentially expressed miRNAs, the top two upregulated and downregulated miRNAs were selected for further analysis based on the false discovery rate (FDR), fold-change (FC) and P-values. Based on the TCGA database, PVT1 was obviously highly expressed in HCC, and a statistically higher PVT1 expression was found for sex (male), ethnicity (Asian) and pathological grade (G3+G4) compared to the control groups (P<0.05). Furthermore, Gene Ontology (GO) analysis revealed that the target genes were involved in complex cellular pathways, such as the macromolecule biosynthetic process, compound metabolic process, and transcription. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the MAPK and Wnt signaling pathways may be correlated with the regulation of the four candidate miRNAs. The results therefore provide significant information on the differentially expressed miRNAs associated with PVT1 in HCC, and we hypothesized that PVT1 may play vital roles in HCC by regulating different miRNAs or target gene expression (particularly MAPK8) via the MAPK or Wnt signaling pathways. Thus, further investigation of the molecular mechanism of PVT1 in HCC is needed.

 PVT1 HCC miRNAs GO KEGG PPI Introduction

Hepatocellular carcinoma (HCC) remains one of the main malignancies worldwide with a poor 5-year survival rate (1–4). Generally, patients are diagnosed with HCC at an advanced stage, and a large number of HCC patients show intrahepatic metastasis and postoperative recurrence (5). In the Chinese population, the development of HCC has been associated with the hepatitis B virus (HBV) and hepatitis C virus (HCV) infections in most patients (6). The long-term symptoms of inflammation, chronic hepatitis and cirrhosis contribute to the virus-initiated tumorigenic process (7,8). For the treatment of HCC, liver transplantation or tumor resection is always the most effective treatment. Furthermore, the high rate of metastasis or postsurgical recurrence remains an obstacle to a better prognosis of HCC patients (9,10). Thus, it is imperative to explore the mechanism of HCC, which may lead to novel insights for the diagnosis and treatment of HCC patients.

Long non-coding RNAs (lncRNAs) include the recently identified class of non-protein coding RNA transcripts of 200 nucleotides to 100 kb in length (11–13). Accumulating evidence has demonstrated that lncRNAs may contribute to various biological processes, including proliferation, apoptosis, invasion and metastasis (14–16). However, the particular mechanisms of many lncRNAs remain vague. lncRNA PVT1 is located on chromosomal region 8q24, which is a well-known cancer-related region (17). Previous studies have confirmed that the overexpression of PVT1 accelerates the development and progression of cancer and reduces the chemosensitivity of cancer patients. Although, compared with normal liver tissues, PVT1 showed a high expression in HCC, improved proliferation and predicted recurrence, the precise functions and mechanism of PVT1 in HCC remain to be elucidated (18–20).

miRNAs refer to small non-coding RNAs with nearly 20 nucleotides. Recent studies have confirmed that lncRNAs can affect HCC via combining the expression of miRNAs (21,22). For example, Liu et al (21) found that lncRNA FTX inhibited the proliferation and metastasis of HCC by binding to miR-374a. Zhu et al (22) revealed that lncRNA LINC00052 inhibited the invasion and migration of HCC by binding to miR-452-5p. Therefore, it is of great significance to further explore the miRNA expression profile associated with lncRNA in HCC in order to identify novel therapeutic strategies.

In the present study, we validated the differential PVT1 expression in normal liver and HCC. Furthermore, we combined the miRNA expression profile after silencing PVT1 expression and miRNA target prediction algorithms to explore the underlying target genes related to PVT1 in HCC. Bioinformatics analysis, involving Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interactions (PPIs) and network analyses, was utilized to explore the underlying functions, pathways and networks of the target genes (23–26). Furthermore, the relationship between PVT1 and the clinical parameters in HCC was confirmed based on the original data in the TCGA database. A flow chart of the present study is shown in Fig. 1.

 Materials and methods <sec> <title>Cell culture and siRNA transfection

Human HCC cells (SMMC-7721) were obtained from the American Type Culture Collection (ATCC), and the SMMC-7721 cells were cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 1% penicillin/streptomycin and 10% fetal bovine serum (FBS) at 37°C in a humidified incubator with 5% CO2. The Lenti-siRNA vector of PVT1 was produced by GeneChem (Shanghai, China) (sense, 5′-CCCAACAGGAGGACAGCUUTT-3′ and antisense, 5′-AAGCUGUCCUCCUGUUGGGTT-3′). siRNA vectors of PVT1 were transfected into HCC cells according to the manufacturer's protocol.

 miRNA microarray analysis

The sample analysis and miRNA microarray hybridization were completed by Kangchen Bio-tech (Shanghai, China). Briefly, miRNA labeling was performed using the miRCURY™ Array Power Labeling kit (cat. no. 208032-A; Exiqon, Vedbaek, Denmark). Then, the labeled sample was combined with 2X Hybridization buffer (Phalanx Hyb). Assembly and miRCURY™ Array, was used for miRNA array hybridization. miRNA array scanning and analysis were applied via Axon GenePix 4000B microarray scanner and GenePix pro V6.0 software (Molecular Devices, LLC, Sunnyvale, CA, USA). Differentially expressed miRNAs between PVT1 RNAi and the control groups were identified when fold-change (FC) was ≥2 or ≤0.5, and false discovery rate (FDR) <1 and P<0.05.

 Validation of the expression of PVT1 in HCC

The TCGA database (http://cancergenome.nih.gov/) is a collection of DNA methylation, RNA-Seq, miRNA-seq, SNP array and exome sequencing (27,28). TCGA can also be used to further explore the expression of complicated cancer genomics and clinical parameters. In the present study, RNA-Seq data of HCC cases, which were calculated on the IlluminaHiSeq RNA-Seq platform, were obtained from the TCGA data portal (https://tcga-data.nci.nih.gov/tcga/), containing 374 HCC cases and 50 adjacent normal liver cases up to July 10, 2017. The original expression data of PVT1 were exhibited as reads per million (RPM) and the expression level of PVT1 was normalized by the Deseq package of R language. Prior to applying further analyses, we log transformed the original expression data for PVT1. The difference expression of PVT1 in various clinicopathological parameters in HCC was acknowledged based on the data from the TCGA database. The diagnostic value of PVT1 was evaluated using the receiver operating characteristic (ROC) curve. Additionally, the genetic alteration of PVT1 in HCC was investigated based on TCGA. Furthermore, Oncomine (https://www.oncomine.org/) and GEPIA (http://gepia.cancer-pku.cn/) databases were assisted to validate PVT1 expression in HCC (29,30).

 Target prediction and functional analysis

Twelve target prediction algorithms were used to predict the probable target genes of miRNAs. The 12 corresponding prediction algorithms were miRWalk (http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2/), miRanda (http://www.microrna.org), mirBridge (http://mirsystem.cgm.ntu.edu.tw/), DIANA microT v4 (http://diana.imis.athena-innovation.gr/), miRMap (http://mirmap.ezlab.org/), miRDB (http://www.mirdb.org/), miRNAMap (http://mirnamap.mbc.nctu.edu.tw/), RNA22 (https://cm.jefferson.edu/), Pictar2 (https://www.mdc-berlin.de/), RNAhybrid (https://bibiserv.cebitec.uni-bielefeld.de/), PITA (https://genie.weizmann.ac.il/), and TargetScan (http://www.targetscan.org/), and the overlapping target genes were identified via Venn diagrams (http://bioinformatics.psb.ugent.be/webtools/Venn/). In addition, the HPA (http://www.proteinatlas.org) was used to explore the protein expression of target genes in HCC and normal liver tissues.

To further consider the potential functions, pathways and networks of these target genes, bioinformatics analyses (GO, KEGG and network analyses) were performed (31,32). In this process, Database for Annotation, Visualization and Integrated Discovery (DAVID: available online: http://david.abcc.ncifcrf.gov/) was utilized to perform GO and KEGG analyses, and biological process (BP), cellular component (CC) and molecular function (MF) categories were derived from the GO analysis. Additionally, Cytoscape (version 2.8, http://cytoscape.org) was applied to construct the functional network.

 Construction of protein-protein interaction (PPI) network

The interaction pairs of the overlapped target genes were researched by Search Tool for the Retrieval of Interacting Genes (STRING; version 9.0, http://string-db.org) (33). The STRING database provides a worldwide perspective for as many animals and mammals as feasible. The predicted and acknowledged interactions are unified and scored. The interaction pairs in PPI network were selected when the combined score was >0.4.

 Statistical analysis

SPSS 22.0 software (IBM Corp., Armonk, NY, USA) was used for the statistical analysis. Data were expressed as mean ± standard deviation (SD). Differences in the expression of PVT1 in HCC and normal liver and various clinicopathological parameters were estimated by the Student's t-test. The comparison between different subgroups was performed by one-way analysis of variance (ANOVA). Kaplan-Meier curves were used to detect the relationship between the PVT1 expression and patient survival in HCC. In addition, the ROC curve was used to predict the clinical diagnostic value of PVT1, which was statistically significant when P<0.05 (two-sided).

 Results <sec> <title>miRNA profiling associated with lncRNA PVT1

The transfection efficiency was ~90%, and the knockdown efficiency of PVT1 in SMMC-7721 cells was >75% as detected by RT-qPCR (data not shown). Next, a miRNA microarray assay was applied to detect the differentially expressed miRNAs between PVT1 RNAi and the control groups. We found that 2 miRNAs were upregulated, and 12 miRNAs were downregulated in response to PVT1 knockdown. A summary of the differentially expressed miRNAs is shown in Fig. 2 and Table I. The top 2 upregulated (miR-302b-5p, miR-5191) and downregulated miRNAs (miR-224-5p, miR-4289) were finally selected as the most significant differentially expressed miRNAs due to the FC, FDR and P-values. Significance was determined via an FC ≥2 or ≤0.5, FDR <1 and P<0.05 was applied (34). We focused on the top two upregulated and downregulated miRNAs to improve the accuracy and stability of the results. The targets of the top dysregulated miRNAs may play key regulation roles in PVT1-related HCC.

 Validation of the expression of PVT1 in HCC

Based on TCGA, 24% cases of PVT1 in HCC were found, which contained amplification, deep deletion and mRNA upregulation (Fig. 3A). To demonstrate the vital role of PVT1 in HCC, a clinical study was performed using the original data in TCGA. The results showed the obvious high expression of PVT1 in HCC compared to that in normal liver tissues (P<0.001, Fig. 3B). Moreover, a statistically significant higher PVT1 expression was observed in sex (male), ethnicity (Asian) and pathological grade (G3+G4) compared with that in the control groups (P<0.05; Fig. 3C-E and Table II). Moreover, the area under curve (AUC) of PVT1 was 0.822 (95% CI, 0.780–0.863), indicating a moderate diagnostic value of the PVT1 expression in HCC (Fig. 3F). Furthermore, we investigated a different PVT1 expression in other clinical parameters of HCC, but no positive results were found based on the TCGA database. In addition, we investigated the relationship between the PVT1 expression and patient survival. A low PVT1 expression was correlated with improved survival (64.31±5.17 months) compared to the high PVT1 expression group (59.59±4.75 months, P=0.241; Fig. 3G) in HCC.

Moreover, the Oncomine and GEPIA databases confirmed the high expression of PVT1 in HCC (Fig. 4A-C). Furthermore, GEPIA demonstrated that patients with a low PVT1 expression have improved overall and disease-free survival, consistent with the results in TCGA (Fig. 4D and E).

 Target prediction and functional analysis

In the present study, 12 miRNA target prediction algorithms were utilized to predict the potential target genes of the four miRNAs. The genes predicted by >6 algorithms were selected as the final target genes. Among these target genes, 696 genes were predicted by >2 miRNAs, and these 696 genes were used for the GO and pathway analyses (Fig. 5). The GO analysis indicated that the target genes were involved in complex cellular pathways, such as macromolecule biosynthetic process, compound metabolic process and transcription (Fig. 6 and Table III). The KEGG pathway analysis revealed that the MAPK and Wnt signaling pathways may be associated with regulation of the four candidate miRNAs (Table IV). To better identify the relationships between PVT1, miRNAs and target genes, a network was constructed via Cytoscape, and the genes were easily observed from the network (Fig. 7).

 PPI network analysis

The STRING database was applied to construct the PPI network and 1,420 PPI pairs with a combined score of <0.4 were selected. PHLPP2 (degree, 42) and MAPK8 (degree, 26) had the highest degree and interactions in the PPI network. Then, a sub-network of 269 PPI pairs with >20 connectivity degrees was constructed for further analysis (Fig. 8). The number of nodes was 96, accounting for 13.79% of all the target genes. The clustering coefficient of PPI network was 0.634, which indicates that the PPI network had high cluster properties.

In addition, the genes associated with the MAPK and Wnt signaling pathways were selected based on the KEGG pathway analysis. Five genes (PRKCA, MAPK8, PPP3R2, MAP3K7 and PRKX) were overlapped based on the Venn diagrams. According to the degree of hub genes in PPI network, MAPK8 had a high degree (degree, 26). Next, we investigated the preliminary expression level of the five genes based on TCGA, and the original expression data of PPP3R2 was censored. Thus, based on TCGA, MAPK8 and PRKX were downregulated, whereas PRKCA and MAP3K7 were upregulated in HCC compared to that in normal liver tissues (both P<0.05, Fig. 9A-D). Furthermore, negative correlations were found between PVT1 and MAPK8 (r=−0.289, P<0.001, Fig. 9E), PRKCA (r=−0.140, P=0.007, Fig. 9F) and MAP3K7 (r=−0.084, P=0.106, Fig. 9G), whereas a positive correlation was found between PVT1 and PRKX (r=0.154, P=0.003, Fig. 9H). Moreover, based on HPA, weak staining in HCC was observed for MAPK8, whereas moderate staining was observed for PRKCA and PPP3R2 (Fig. 10A-F). Negative staining in both HCC and normal liver tissues was observed for MAP3K7, in contrast with its upregulated expression in TCGA (Fig. 10G and H), whereas moderate staining for PRKX was observed in HCC, inconsistent with its downregulated expression in TCGA (Fig. 10I and J). Based on these results, PRKCA and MAPK8 were all negatively correlated with PVT1, whereas PRKCA was overexpressed in HCC, in contrast with the correlation of PVT1. Thus, only MAPK8 was selected. We hypothesized that PVT1 may influence MAPK8 expression in the MAPK or Wnt signaling pathways to participate in the different biological processes of HCC. However, the precise molecular mechanism of PVT1 in HCC needs further experimental investigation.

 Discussion

Previous studies have demonstrated that lncRNAs participates in different biological processes, such as transcription, chromosome remodeling and post-transcriptional processing (35–37). Many studies have verified that lncRNAs are associated with the tumorigenesis and development of various types of cancer through various pathways, including the regulation of cell proliferation, metastasis and invasion (38–40). Thus, lncRNAs have opened an avenue of cancer genomics.

To date, several studies have investigated the effect of PVT1 on various cancer types. Xu et al (41) demonstrated that PVT1 overexpression encouraged proliferation and invasion in gastric cancer cells via binding to FOXM1, and a high PVT1 expression was associated with the poor prognosis of gastric cancer patients. Chen et al (42) revealed that the overexpression of PVT1 promoted the invasion of non-small cell lung cancer cells. Additionally, PVT1 functioned as a competitive endogenous RNA to regulate the expression of MMP9 via competitively binding to microRNAs. Liu et al (43) showed that PVT1 was an oncogene in prostate cancer by activating miR-146a methylation to improve tumor growth. Nevertheless, the detailed roles for PVT1 in HCC remain undefinable. In the present study, we combined miRNA microarray analysis and TCGA, as well as Oncomine and GEPIA databases to explore the potential biological functions of PVT1 in HCC. We confirmed that PVT1 was an oncogene and highly expressed in HCC, consistent with Yu et al and Ding et al (18,19). Moreover, Yu et al (18) revealed that the combined upregulation of two lncRNAs (PVT1 and uc002 mbe.2) offered a new method for the diagnosis of HCC, and the expression of these two lncRNAs was positively correlated with tumor size and clinical stage in HCC patients. Furthermore, Ding et al (19) revealed that the overexpression of PVT1 was strongly associated with the AFP level and could predict recurrence. By comparison, the present study showed that PVT1 expression was positively correlated with sex, ethnicity and pathological grade. The AUC of PVT1 indicated a moderate diagnostic value of PVT1 expression in HCC. Furthermore, the genetic alterations of PVT1 were observed in HCC based on TCGA, which may be correlated with the pathogenesis of HCC.

To the best of our knowledge, the present study was the first to identify the differentially expressed miRNAs associated with PVT1 based on miRNA microarray analysis, and 12 miRNA target prediction algorithms were used to predict the underlying target genes of the differentially expressed miRNAs. According to GO analysis, the target genes were involved in complex cellular pathways, such as macromolecule biosynthetic process, compound metabolism, and transcription. The KEGG pathway analysis revealed that the MAPK and Wnt signaling pathways are potentially correlated with the regulation of the four candidate miRNAs. As reported, the MAPK and Wnt signaling pathways were all associated with proliferation, migration, invasion and prognosis and HCC (44–48). Consequently, we hypothesized that PVT1 plays a vital role in HCC by regulating the expression of the four miRNAs via the MAPK or Wnt signaling pathways, which requires further investigation on the precise molecular mechanism of PVT1 in HCC. We also investigated the genes from the MAPK and Wnt signaling pathways and the hub genes from PPI. We hypothesized that PVT1 may influence MAPK8 expression to contribute to different biological processes of HCC. Various in vitro and in vivo experiments, including cell proliferation, invasion and metastasis assays, and animal models, are needed to verify this hypothesis. The clinical significance and molecular mechanism of PVT1 in the biological function of HCC are to be further researched at the molecular, cellular, tissue and animal levels. Focusing on the new insight of PVT1 in HCC, the present study aimed to provide a potential biomarker or therapeutic target for HCC.

 Acknowledgements

Not applicable.

 Funding

The present study was financially supported through grants from the National Natural Science Foundation of China (no. NSFC81560489), the Natural Science Foundation of Guangxi, China (nos. 2016GXNSFBA380039 and 2017GXNSFAA198017) and the Promoting Project of Basic Capacity for Young and Middle-aged University Teachers in Guangxi (no. KY2016LX031) and Medical Excellence Award funded by the Creative Research Development grant from the First Affiliated Hospital of Guangxi Medical University.

 Availability of data and materials

Data used in this study are available upon request to the corresponding author.

 Authors' contributions

YZ and WM conceived and designed the study. XW, TZ and YQ performed the experiments. YZ and GC wrote the paper. DW and YD reviewed and edited the manuscript. All authors read and approved the manuscript and agree to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.

 Ethics approval and consent to participate

All experimental protocols were approved by the First Affiliated Hospital of Guangxi Medical University (Nanning, China).

 Consent for publication

Not applicable.

 Competing interests

The authors DEC that they have no competing interests.

 Abbreviations HCC

hepatocellular carcinoma

 lncRNAs

long non-coding RNAs

 TCGA

The Cancer Genome Atlas

 MEM

Multi Experiment Matrix

 GO

Gene Ontology

 KEGG

Kyoto Encyclopedia of Genes and Genomes

 PPI

protein-protein interactions

 ROC

receiver operating characteristic

 BP

biological process

 CC

cellular component

 MF

molecular function

 FDR

false discovery rate

 AUC

area under curve

 HBV

hepatitis B virus

 HCV

hepatitis C virus

 DMEM

Dulbecco's modified Eagle's medium

 FBS

fetal bovine serum

 DAVID

Database for Annotation, Visualization and Integrated Discovery

 STRING

Search Tool for the Retrieval of Interacting Genes

 ANOVA

one-way analysis of variance

 mean ± SD

mean ± standard deviation

 FC

fold-change

 References LiCMiaoRLiuSWanYZhangSDengYBiJQuKZhangJLiuCDown-regulation of miR-146b-5p by long noncoding RNA MALAT1 in hepatocellular carcinoma promotes cancer growth and metastasisOncotarget828683286952017284049235438683 LuPHChenMBLiuYYWuMHLiWTWeiMXLiuCYQinSKIdentification of sphingosine kinase 1 (SphK1) as a primary target of icaritin in hepatocellular carcinoma cellsOncotarget822800228102017282069525410263 MaluccioMCoveyARecent progress in understanding, diagnosing, and treating hepatocellular carcinomaCA Cancer J Clin62394399201210.3322/caac.2116123070690 TrépoCChanHLLokAHepatitis B virus infectionLancet38420532063201410.1016/S0140-6736(14)60220-824954675 El-SeragHBHepatocellular carcinomaN Engl J Med36511181127201110.1056/NEJMra100168321992124 ZhangHZhaiYHuZWuCQianJJiaWMaFHuangWYuLYueWGenome-wide association study identifies 1p36.22 as a new susceptibility locus for hepatocellular carcinoma in chronic hepatitis B virus carriersNat Genet42755758201010.1038/ng.63820676096 BréchotCPathogenesis of hepatitis B virus-related hepatocellular carcinoma: Old and new paradigmsGastroenterology1275 Suppl 1S56S61200410.1053/j.gastro.2004.09.01615508104 XuYQiYLuoJYangJXieQDengCSuNWeiWShiDXuFHepatitis B virus X protein stimulates proliferation, wound closure and inhibits apoptosis of HuH-7 cells via CDC42Int J Mol Sci18E586201710.3390/ijms1803058628282856 MoriguchiMTakayamaTHigakiTKimuraYYamazakiSNakayamaHOhkuboTAramakiOEarly cancer-related death after resection of hepatocellular carcinomaSurgery151232237201210.1016/j.surg.2010.10.01721176935 FairmanJLiuKHMenneSPrevention of liver tumor formation in woodchucks with established hepatocellular carcinoma by treatment with cationic liposome-DNA complexesBMC Cancer17172201710.1186/s12885-017-3163-2282646665339946 BatistaPJChangHYLong noncoding RNAs: Cellular address codes in development and diseaseCell15212981307201310.1016/j.cell.2013.02.012234989383651923 XieXPanJWeiLWuSHouHLiXChenWGene expression profiling of microRNAs associated with UCA1 in bladder cancer cellsInt J Oncol4816171627201610.3892/ijo.2016.335726820254 SunXJWangQGuoBLiuXYWangBIdentification of skin-related lncRNAs as potential biomarkers that involved in Wnt pathways in keloidsOncotarget834236342442017284049555470963 ZhuPWangYWuJHuangGLiuBYeBDuYGaoGTianYHeLFanZLncBRM initiates YAP1 signalling activation to drive self-renewal of liver cancer stem cellsNat Commun713608201610.1038/ncomms13608279054005146280 CaoCSunJZhangDGuoXXieLLiXWuDLiuLThe long intergenic noncoding RNA UFC1, a target of MicroRNA 34a, interacts with the mRNA stabilizing protein HuR to increase levels of β-catenin in HCC cellsGastroenterology148415426.e18201510.1053/j.gastro.2014.10.01225449213 ZhouMZhangXYYuXOverexpression of the long non-coding RNA SPRY4-IT1 promotes tumor cell proliferation and invasion by activating EZH2 in hepatocellular carcinomaBiomed Pharmacother85348354201710.1016/j.biopha.2016.11.03527899259 ColomboTFarinaLMacinoGPaciPPVT1: A rising star among oncogenic long noncoding RNAsBiomed Res Int2015304208201510.1155/2015/304208258839514391155 YuJHanJZhangJLiGLiuHCuiXXuYLiTLiuJWangCThe long noncoding RNAs PVT1 and uc002mbe.2 in sera provide a new supplementary method for hepatocellular carcinoma diagnosisMedicine95e4436201610.1097/MD.0000000000004436274950684979822 DingCYangZLvZDUCXiaoHPengCChengSXieHZhouLWuJZhengSLong non-coding RNA PVT1 is associated with tumor progression and predicts recurrence in hepatocellular carcinoma patientsOncol Lett9955963201510.3892/ol.2014.273025624916 WangFYuanJHWangSBYangFYuanSXYeCYangNZhouWPLiWLLiWSunSHOncofetal long noncoding RNA PVT1 promotes proliferation and stem cell-like property of hepatocellular carcinoma cells by stabilizing NOP2Hepatology6012781290201410.1002/hep.2723925043274 LiuFYuanJHHuangJFYangFWangTTMaJZZhangLZhouCCWangFYuJLong noncoding RNA FTX inhibits hepatocellular carcinoma proliferation and metastasis by binding MCM2 and miR-374aOncogene3554225434201610.1038/onc.2016.8027065331 ZhuLYangNChenJZengTYanSLiuYYuGChenQDuGPanWLINC00052 upregulates EPB41L3 to inhibit migration and invasion of hepatocellular carcinoma by binding miR-452-5pOncotarget863724637372017289690245609956 XuXWangXFuBMengLLangBDifferentially expressed genes and microRNAs in bladder carcinoma cell line 5637 and T24 detected by RNA sequencingInt J Clin Exp Pathol812678126872015267224574680402 SubramanianYKaliyappanKRamakrishnanKSFacile hydrothermal synthesis and characterization of Co2GeO4/r-GO@C ternary nanocomposite as negative electrode for Li-ion batteriesJ Colloid Interface Sci4987684201710.1016/j.jcis.2017.03.00528319843 WeiLMurphyBLWuGParkerMEastonJGilbertsonRJZhangJRousselMFExome sequencing analysis of murine medulloblastoma models identifies WDR11 as a potential tumor suppressor in Group 3 tumorsOncotarget86468564697201710.18632/oncotarget.19642290293865630286 ChenFShenCWangXWangHLiuYYuCLvJHeJWenZIdentification of genes and pathways in nasopharyngeal carcinoma by bioinformatics analysisOncotarget863738637492017289690255609957 BornsteinSSchmidtMChoonooGLevinTGrayJThomasCRJrWongMMcWeeneySIL-10 and integrin signaling pathways are associated with head and neck cancer progressionBMC Genomics1738201610.1186/s12864-015-2359-6267475254706689 ZengJHXiongDDPangYYZhangYTangRXLuoDZChenGIdentification of molecular targets for esophageal carcinoma diagnosis using miRNA-seq and RNA-seq data from The Cancer Genome Atlas: A study of 187 casesOncotarget835681356992017284156855482608 RhodesDRKalyana-SundaramSMahavisnoVVaramballyRYuJBriggsBBBarretteTRAnstetMJKincead-BealCKulkarniPOncomine 3.0: Genes, pathways, and networks in a collection of 18,000 cancer gene expression profilesNeoplasia9166180200710.1593/neo.07112173567131813932 TangZLiCKangBGaoGLiCZhangZGEPIA: A web server for cancer and normal gene expression profiling and interactive analysesNucleic Acids Res45W98W102201710.1093/nar/gkx247284071455570223 AshburnerMBallCABlakeJABotsteinDButlerHCherryJMDavisAPDolinskiKDwightSSEppigJTGene ontology: Tool for the unification of biology. The Gene Ontology ConsortiumNat Genet252529200010.1038/75556108026513037419 GeQMHuangCMZhuXYBianFPanSMDifferentially expressed miRNAs in sepsis-induced acute kidney injury target oxidative stress and mitochondrial dysfunction pathwaysPLoS One12e0173292201710.1371/journal.pone.0173292282969045351858 FranceschiniASzklarczykDFrankildSKuhnMSimonovicMRothALinJMinguezPBorkPvon MeringCJensenLJSTRING v9.1: Protein-protein interaction networks, with increased coverage and integrationNucleic Acids Res41D808D815201310.1093/nar/gks109423203871 QianYFengLWuWWengTHuCHongBWangFXShenLWangQJinXMicroRNA expression profiling of pancreatic cancer cell line L3.6p1 following B7-H4 knockdownCell Physiol Biochem44494504201710.1159/00048508629145206 ShiXSunMLiuHYaoYSongYLong non-coding RNAs: A new frontier in the study of human diseasesCancer Lett339159166201310.1016/j.canlet.2013.06.01323791884 XiongDDFengZBCenWLZengJJLiangLTangRXGanXNLiangHWLiZYChenGThe clinical value of lncRNA NEAT1 in digestive system malignancies: A comprehensive investigation based on 57 microarray and RNA-seq datasetsOncotarget817665176832017281186095392277 ChenYXieHGaoQZhanHXiaoHZouYZhangFLiuYLiJColon cancer associated transcripts in human cancersBiomed Pharmacother94531540201710.1016/j.biopha.2017.07.07328779711 ZhangZLiuTWangKQuXPangZLiuSLiuQDuJDown-regulation of long non-coding RNA MEG3 indicates an unfavorable prognosis in non-small cell lung cancer: Evidence from the GEO databaseGene6304958201710.1016/j.gene.2017.08.00128782577 GaoRZhangRZhangCZhaoLZhangYLong noncoding RNA CCAT1 promotes cell proliferation and metastasis in human medulloblastoma via MAPK pathwayTumori 02017doi: 10.5301/tj.5000662 JinLFuHQuanJPanXHeTHuJLiYLiHYangYYeJOverexpression of long non-coding RNA differentiation antagonizing non-protein coding RNA inhibits the proliferation, migration and invasion and promotes apoptosis of renal cell carcinomaMol Med Rep1644634468201710.3892/mmr.2017.713528765964 XuMDWangYWengWWeiPQiPZhangQTanCNiSJDongLYangYA positive feedback loop of lncRNA-PVT1 and FOXM1 facilitates gastric cancer growth and invasionClin Cancer Res2320712080201710.1158/1078-0432.CCR-16-074227756785 ChenWZhuHYinLWangTWuJXuJTaoHLiuJHeXlncRNA-PVT1 facilitates invasion through upregulation of MMP9 in nonsmall cell lung cancer cellDNA Cell Biol36787793201728731781 LiuHTFangLChengYXSunQLncRNA PVT1 regulates prostate cancer cell growth by inducing the methylation of miR-146aCancer Med535123519201610.1002/cam4.900277941845224852 ZengJLiuXLiXZhengYLiuBXiaoYDaucosterol inhibits the proliferation, migration, and invasion of hepatocellular carcinoma cells via Wnt/β-catenin signalingMolecules22E862201710.3390/molecules2206086228574485 ZhangJLaiWLiQYuYJinJGuoWZhouXLiuXWangYA novel oncolytic adenovirus targeting Wnt signaling effectively inhibits cancer-stem like cell growth via metastasis, apoptosis and autophagy in HCC modelsBiochem Biophys Res Commun491469477201710.1016/j.bbrc.2017.07.04128698142 OuWLvJZouXYaoYWuJYangJWangZMaYPropofol inhibits hepatocellular carcinoma growth and invasion through the HMGA2-mediated Wnt/beta-catenin pathwayExp Ther Med1325012506201710.3892/etm.2017.4253285658715443290 PengWFanHLong noncoding RNA CCHE1 indicates a poor prognosis of hepatocellular carcinoma and promotes carcinogenesis via activation of the ERK/MAPK pathwayBiomed Pharmacother83450455201610.1016/j.biopha.2016.06.05627427851 LinWZhongMYinHChenYCaoQWangCLingCEmodin induces hepatocellular carcinoma cell apoptosis through MAPK and PI3K/AKT signaling pathways in vitro and in vivoOncol Rep36961967201610.3892/or.2016.486127278720

A flow chart of the present study.

miRNA clip after PVT1 knock-down in HCC. (A) Volcano plots of miRNAs. Volcano plots were constructed by fold-change and P-values. The vertical lines correspond to P-values, and the horizontal lines represent log2 (fold-change) upregulation and downregulation. The red points indicate the significantly differentially expressed miRNAs. (B) Scatter plot of miRNAs. Scatter plots were generated to assess variations in miRNA expression. The values corresponding to the x-axes and y-axes are the normalized signal values.

Clinical significance of lncRNA PVT1 in HCC based on the TCGA database. (A) The genetic alteration of PVT1 in HCC, (B) differential expression of PVT1 between HCC and non-cancerous liver tissue, (C) differential expression of PVT1 in male vs. female, (D) white vs. yellow vs. black, (E) G1, G2 vs. G3, G4, (F) ROC curve of PVT1 in HCC, and (G) Kaplan-Meier curves of PVT1 expression in HCC.

Validation of PVT1 expression in HCC. (A) Validation of PVT1 expression in the cohort of Chen Liver from Oncomine. Normal liver tissues (n=73) and hepatocellular carcinoma tissues (n=97) were included. (B) Validation of PVT1 expression in the cohort of Wurmbach Liver from Oncomine. Normal liver tissues (n=10) and hepatocellular carcinoma tissues (n=35) were included. (C) Normal liver tissues (n=160) and HCC tissues (n=369) were included based on the GEPIA database. (D) Overall survival of PVT1 expression in HCC based on the GEPIA database. (E) Disease-free survival of PVT1 expression in HCC based on the GEPIA database.

The procedure to achieve 696 genes. a, miR-302b-5p; b, miR-5191; c, miR-224-5p; and d, miR-4289.

A functional network of Gene Ontology (GO) terms for the potential PVT1 genes in HCC. To further elucidate the functions of the overlapping genes, a function network was constructed according to Cytoscape.

MiRNA-mRNA network constructed by Cytoscape. Four miRNAs were selected to draw the miRNA-mRNA network. Red indicates upregulation and green indicates downregulation.

The PPI network of the target genes. The PPI network was constructed via STRING online and 269 PPI pairs were selected for further analysis.

Clinical significance of the hub genes in HCC based on the TCGA database. Differential expression of (A) MAPK8, (B) PRKX, (C) PRKCA, and (D) MAP3K7 between HCC and non-cancerous liver tissue. (E) Negative correlation between PVT1 and MAPK8, (F) PVT1 and PRKCA, (G) PVT1 and MAP3K7, and (H) PVT1 and PRKX.

Validation of hub gene expression based on the HPA database. (A) MAPK8 was weakly stained in HCC (magnification, ×100), (B) MAPK8 was moderately stained in normal liver (magnification, ×100), (C) PRKCA was moderately stained in HCC (magnification, ×100), (D) PRKCA was weakly stained in normal liver (magnification, ×100), (E) PPP3R2 was moderately stained in HCC (magnification, ×100), (F) PPP3R2 was negatively stained in normal liver (magnification, ×100), (G) MAP3K7 was negatively stained in HCC (magnification, ×100), (H) MAP3K7 was negatively stained in normal liver (magnification, ×100), (I) PRKX was moderately stained in HCC (magnification, ×100), and (J) PRKX was negatively stained in normal liver (magnification, ×100).

The top 2 upregulated and top 2 downregulated miRNAs.

Name Fold-change P-value FDR
Upregulated miRNAs
  miR-302b-5p 2.832 0.020 0.441
  miR-5191 2.477 0.013 0.409
Downregulated miRNAs
  miR-224-5p 0.372 0.009 0.398
  miR-4289 0.453 0.002 0.349
  miR-UL22A-5p 0.040 0.001 0.349
  miR-548aa/miR-548t-3p 0.455 0.003 0.371
  miR-544b 0.076 0.006 0.379
  miR-374c-3p 0.465 0.010 0.398
  miR-5009-5p 0.379 0.012 0.407
  miR-138-1-3p 0.392 0.033 0.441
  miR-154-5p 0.360 0.036 0.441
  miR-5003-5p 0.475 0.038 0.441
  miR-195-5p 0.421 0.043 0.441
  miR-3131 0.354 0.049 0.441

Differential expression of PVT1 of other clinicopathological parameters in HCC based on TCGA.

PVT1 expression
Clinicopathological parameters N Mean ± SD T P-value
Tissues
  Normal liver 50 5.489±0.095 12.43 <0.001
  HCC 374 7.044±0.082
Age (years)
  <60 169 7.078±1.493 0.305 0.761
  ≥60 201 7.027±1.651
Sex
  Male 250 7.206±1.624 2.631 0.009
  Female 121 6.749±1.447
Race
  White 184 6.811±1.525 F=5.436 0.005
  Black 17 6.642±1.575
  Asian 158 7.340±1.604
T (tumor)
  T1+T2 275 7.045±1.516 −0.308 0.758
  T3+T4 93 7.104±1.779
Stage
  I+II 257 7.083±1.534 0.106 0.916
  III+IV 90 7.062±1.750
Pathological grade
  G1+G2 232 6.874±1.491 −2.970 0.003
  G3+G4 134 7.382±1.708

Top 10 enrichment GO terms (BP, CC and MF) for the target genes of miRNAs.

GO ID Term Ontology Count Fold enrichment P-value
GO:0010557 Positive regulation of macromolecule biosynthetic process BP 53 2.197 1.24121E-07
GO:0051173 Positive regulation of nitrogen compound metabolic process BP 52 2.189 1.89479E-07
GO:0045941 Positive regulation of transcription BP 47 2.259 3.37814E-07
GO:0009891 Positive regulation of biosynthetic process BP 54 2.106 3.51213E-07
GO:0045893 Positive regulation of transcription, DNA-dependent BP 42 2.387 3.84168E-07
GO:0045935 Positive regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process BP 50 2.172 4.265E-07
GO:0051254 Positive regulation of RNA metabolic process BP 42 2.367 4.81533E-07
GO:0031328 Positive regulation of cellular biosynthetic process BP 53 2.098 5.24515E-07
GO:0010628 Positive regulation of gene expression BP 47 2.193 7.8235E-07
GO:0010604 Positive regulation of macromolecule metabolic process BP 60 1.898 2.2026E-06
GO:0005635 Nuclear envelope CC 21 2.976 2.61292E-05
GO:0030424 Axon CC 18 3.287 3.2788E-05
GO:0030426 Growth cone CC 10 5.380 8.24587E-05
GO:0030427 Site of polarized growth CC 10 5.282 9.56427E-05
GO:0045202 Synapse CC 28 2.291 9.57691E-05
GO:0043005 Neuron projection CC 27 2.293 0.0001
GO:0031965 Nuclear membrane CC 11 4.377 0.0002
GO:0016010 Dystrophin-associated glycoprotein complex CC 6 10.253 0.0002
GO:0031252 Cell leading edge CC 15 3.158 0.0003
GO:0044459 Plasma membrane part CC 105 1.385 0.0003
GO:0003700 Transcription factor activity MF 71 1.857 4.45E-07
GO:0030528 Transcription regulator activity MF 95 1.603 2.72E-06
GO:0043565 Sequence-specific DNA binding MF 45 1.891 5.87E-05
GO:0008092 Cytoskeletal protein binding MF 36 1.822 0.0007
GO:0016563 Transcription activator activity MF 31 1.929 0.0007
GO:0051015 Actin filament binding MF 9 4.331 0.0010
GO:0003779 Actin binding MF 26 2.034 0.0010
GO:0003702 RNA polymerase II transcription factor activity MF 20 2.091 0.0033
GO:0005127 Ciliary neurotrophic factor receptor binding MF 3 25.507 0.0045
GO:0019899 Enzyme binding MF 34 1.658 0.0047

GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function.

The top 10 KEGG pathways from the enrichment analysis of the target genes of miRNAs.

KEGG ID KEGG term Count Fold enrichment P-value Gene symbol
hsa04010 MAPK signaling pathway 22 2.253 0.0006 PRKCA, TAOK1, TGFBR1, PPP3R2, STK4, PRKX, TGFB2, ATF2, MAP3K7, MAPK1, DUSP4, RPS6KA3, RASGRF2, ELK4, ARRB1, MAP3K2, MAPT, PDGFRA, MAPK8, RAPGEF2, CACNA1B, RASA2
hsa04310 Wnt signaling pathway 15 2.716 0.0011 PRKCA, TBL1XR1, VANGL1, SMAD4, PPP3R2, SMAD3, FZD3, DAAM1, FZD5, PRKX, MAP3K7, CCND1, PSEN1, PRICKLE2, MAPK8
hsa04360 Axon guidance 13 2.755 0.0024 SEMA5A, ABLIM1, MAPK1, SEMA6A, PLXNA2, NTN4, PPP3R2, ROBO2, EFNA5, DPYSL2, SLIT1, SRGAP1, EPHA2
hsa05210 Colorectal cancer 10 3.255 0.0032 MAPK1, CCND1, TGFBR1, PDGFRA, SMAD4, SMAD3, FZD3, MAPK8, FZD5, TGFB2
hsa05212 Pancreatic cancer 9 3.417 0.0043 MAPK1, CCND1, TGFBR1, SMAD4, RALA, SMAD3, CDK6, MAPK8, TGFB2
hsa05200 Pathways in cancer 21 1.750 0.01500 PRKCA, XIAP, TGFBR1, MITF, SMAD4, RUNX1T1, SMAD3, CDK6, EGLN1, FZD3, FZD5, STK4, TPM3, TGFB2, MAPK1, CCND1, HDAC2, CDKN2B, PDGFRA, RALA, MAPK8
hsa05220 Chronic myeloid leukemia 8 2.916 0.01856 MAPK1, CCND1, HDAC2, TGFBR1, SMAD4, SMAD3, CDK6, TGFB2
hsa04520 Adherens junction 8 2.840 0.0212 MAP3K7, MAPK1, TGFBR1, SMAD4, SMAD3, PTPN1, MLLT4, VCL
hsa04120 Ubiquitin-mediated proteolysis 11 2.195 0.027 CUL3, UBE2D3, UBE4A, XIAP, NEDD4, UBE2K, UBE2G1, UBA2, UBE2J1, UBE2W, NEDD4L
hsa04144 Endocytosis 13 1.932 0.0350 DNM3, RAB31, RAB11FIP2, ERBB4, TFRC, NEDD4, ARRB1, TGFBR1, PSD3, PDGFRA, EEA1, NEDD4L, PIP4K2B

KEGG, Kyoto Encyclopedia of Genes and Genomes.