Hindawi BioMed Research International Volume 2021, Article ID 6640652, 16 pages https://doi.org/10.1155/2021/6640652

Research Article Genome-Wide Screening Identifies Prognostic Long Noncoding RNAs in Hepatocellular Carcinoma

Yujie Feng, Xiao Hu, Kai Ma, Bingyuan Zhang , and Chuandong Sun

Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province 266003, China

Correspondence should be addressed to Bingyuan Zhang; [email protected] and Chuandong Sun; [email protected]

Received 19 October 2020; Revised 18 April 2021; Accepted 23 April 2021; Published 21 May 2021

Academic Editor: Min Tang

Copyright © 2021 Yujie Feng 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.

Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis. Therefore, there is an urgent call for the investigation of novel biomarkers in HCC. In the present study, we identified 6 upregulated lncRNAs in HCC, including LINC01134, RHPN1-AS1, NRAV, CMB9-22P13.1, MKLN1-AS, and MAPKAPK5-AS1. Higher expression of these lncRNAs was correlated to a more advanced cancer stage and a poorer prognosis in HCC patients. Enrichment analysis revealed that these lncRNAs played a crucial role in HCC progression, possibly through a series of cancer-related biological processes, such as cell cycle, DNA replication, histone acetyltransferase complex, fatty acid oxidation, and lipid modification. Moreover, competing endogenous RNA (ceRNA) network analysis revealed that these lncRNAs could bind to certain miRNAs to promote HCC progression. Loss-of-function assays indicated that silencing of RHPN1-AS1 significantly suppressed HCC proliferation and migration. Though further validations are still needed, these identified lncRNAs could serve as valuable potential biomarkers for HCC prognosis.

1. Introduction link to the regulation of HCC growth and metastasis. Specif- ically, lncRNA XIST regulates HCC tumor growth and Hepatocellular carcinoma (HCC) has ranked as the second migration by sponging miR-497-5p [9]. DANCR retained leading cause of cancer-related deaths worldwide [1], as more HCC stemness by suppressing CTNNB1 expression [11]. than 782,000 HCC-associated deaths are predicted to occur The LINC01138 drives carcinogenesis in HCC via activating annually [2]. Previous studies have demonstrated that the arginine methyltransferase 5 [10]. Meanwhile, several upregulation of oncogenes and downregulation of tumor lncRNAs have been demonstrated as potential prognostic suppressors were related to HCC tumorigenesis. For exam- biomarkers in HCC. For example, overexpression of lncRNA ple, ID1 enhanced tumor growth in HCC patients [3], while ENST00000429227.1, LINC00511, SNHG16, and AK001796 FABP4 inhibited tumor growth and invasion [4]. However, were associated with worse prognosis in HCC patients [12] . there is still an urgent call to explore new biomarkers in A further appreciation of the molecular functions and HCC, as novel therapeutic targets are needed for establishing expression patterns of lncRNAs could help reveal novel bio- future molecular therapies, in the hope of improving progno- markers for HCC. ses of HCC patients. In this study, using gene expression data of HCC patients Noncoding RNAs (ncRNAs) have emerged as a very and healthy controls, we screened possible prognostic promising resource for the identification of prognostic bio- lncRNAs in HCC. Additionally, we utilized bioinformatic markers. ncRNAs longer than 200 bps are regarded as long approaches to explore the potential functions of such noncoding RNAs (lncRNAs) [5]. Growing evidence has con- lncRNAs and constructed competing endogenous RNA firmed the association between HCC and lncRNAs [6]. For (ceRNA) networks to further demonstrate potential mecha- example, XIST [7–9] and LINC01138 [10] were reported to nisms concerning those identified lncRNAs in HCC 2 BioMed Research International

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−3 Tissue WI2−89031B12.1 SCRT1 HIST1H1B RPL39P3 MED7 TBRG4 RHOT2 C12orf43 UTP6 EFTUD2 UTP18 SRP68 NUP85 RAD51C RP13−516M14.1 TRIM45 NR2C1 PRR3 POLR3F PCNT FAM216A UBE2S NRM PHF19 RBM28 STX1A WDR4 KPNA2 CCNB1 PTTG1 KIF2C STMN1 CENPH NASP MCM3 LMNB1 CHAF1B CHEK1 MCM6 ZWINT SNHG20 RP5−1074L1.4 BRD8 C5orf45 GPSM2 NUP205 MYO19 BRCA1 DHX57 XPO5 CAD AP000695.4 SLC25A19 RNFT2 SGOL1 EME1 PARPBP MKI67 EZH2 SPATA2 DDAH2 BFSP1 C2orf66 RP11−709D24.8 LCN6 CCZ1B HEATR5A LRP11 PSMC3IP ZNF131 STAG3 OMG XCR1 CLEC3B DNASE1L3 RP11−197P3.5 RP11−932O9.10 ATRIP SPEF1 RPL14P3 ADH4 AXIN1 OLFM1 IGJ LINC01268 RP11−225B17.2 CTGLF10P RP4−620E11.8 RP11−127B20.3 WBP2NL RP5−967N21.11 AC010761.8 CTD−2349P21.9 RP11−218F10.3 CELSR3 CDC25C E2F8 CENPK MCM10 RAD54L

Tissue Non-tumor

HCC (a) Disease-free survival Overall survival Gene symbol CTB−147N14.6 RP11−286H15.1 RP11−295D4.1 RHPN1−AS1 SNRPEP2 LINC01134 NRAV NAP1L1P1 CMB9−22P13.1 MKLN1−AS RP5−864K19.4 CTC−297N7.9 MAPKAPK5−AS1 MIR210HG AP001469.9

01234 0 0.5 1 1.5 2 2.5 3 3.5 4

Log2 Hazard ratio Log2 Hazard ratio (b)

Figure 1: LINC01134, RHPN1-AS1, NRAV, CMB9-22P13.1, MKLN1-AS, and MAPKAPK5-AS1 were associated with both disease-free survival and overall survival time in HCC. (a) The expression profiles of the 100 differentially expressed lncRNAs in HCC and nontumor tissues. (b) The two forest plots of disease-free survival and overall survival for the 15 genes with significant association with overall survival. The gene symbols with red color are associated with both disease-free survival and overall survival. development and progression. Loss-of-function assays were 2.2. Data Preparation and Processing. The gene list of differ- performed to investigate potential functions of key lncRNAs ently expressed lncRNAs in HCC was downloaded from the in HCC. We believe that this study could provide valuable GEPIA dataset (http://gepia.cancer-pku.cn/). GEPIA is a markers in HCC and inspire further researches. newly developed interactive web server for analyzing the RNA sequencing expression data of 9,736 tumors and 8,587 2. Materials and Methods normal samples from the TCGA and the GTEx projects using a standard processing pipeline, according to a previous 2.1. Clinical Samples. From April 2016 to March 2020, 6 report [13]. Specifically, GEPIA normalized gene expression paired HCC and adjacent normal tissues were collected at by transcript per million (TPM), employed the R limma the Affiliated Hospital of Qingdao University. The ethics package to conduct differential expression analysis, and pro- committee of this hospital approved this study before the vided a list of differentially expressed genes ranked by the P enrollment of patients. All patients or their parents signed values. lncRNAs with log 2 ∣ FC ∣ >2:0 and P value < 0.001 informed consent. were considered as differentially expressed lncRNAs, and BioMed Research International 3

Disease-free survival Disease-free survival Disease-free survival 1.0 1.0 1.0

0.8 0.8 0.8 Logrank P = 0.0034 Logrank P = 0.0014 Logrank P = 0.0014 0.6 0.6 0.6

0.4 0.4 0.4

Percent survival 0.2 Percent survival 0.2 Percent survival 0.2

0.0 0.0 0.0 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120

Months Months Months Low LINC01134 Low RHPN1−AS1 Low NRAV High LINC01134 High RHPN1−AS1 High NRAV

Disease-free survival Disease-free survival Disease-free survival 1.0 1.0 1.0

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0.6 Logrank P = 0.002 0.6 Logrank P = 0.01 0.6 Logrank P = 0.037

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Percent survival 0.2 Percent survival 0.2 Percent survival 0.2

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Months Months Months Low CMB9−22P13.1 Low MKLN1−AS Low MAPKAPK5−AS1 High CMB9−22P13.1 High MKLN1−AS High MAPKAPK5−AS1 (a)

Figure 2: Continued. 4 BioMed Research International

Overall survival Overall survival Overall survival 1.0 1.0 1.0

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Percent survival 0.2 Percent survival 0.2 Percent survival 0.2

0.0 Logrank P = 7.9e−06 0.0 Logrank P = 3.5e−06 0.0 Logrank P = 7.1e−06 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120

Months Months Months Low LINC01134 Low RHPN1−AS1 Low NRAV High LINC01134 High RHPN1−AS1 High NRAV

Overall survival Overall survival Overall survival 1.0 1.0 1.0

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Percent survival 0.2 Percent survival 0.2 Percent survival 0.2 Logrank P = 2.4e−05 Logrank P = 3.9e−05 Logrank P = 3.6e−05 0.0 0.0 0.0 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120

Months Months Months Low CMB9−22P13.1 Low MKLN1−AS Low MAPKAPK5−AS1 High CMB9−22P13.1 High MKLN1−AS High MAPKAPK5−AS1 (b)

Figure 2: LINC01134, RHPN1-AS1, NRAV, CMB9-22P13.1, MKLN1-AS, and MAPKAPK5-AS1 were related to disease-free and overall survival time in HCC. The Kaplan-Meier (KM) curves of disease-free survival (a) and overall survival (b) for six prognostic lncRNAs in HCC. the top 100 lncRNAs ranked by the P values were selected for HCC. The hypergeometric test was employed to determine downstream analysis. Moreover, those lncRNAs associated the statistical significance. with survival time in HCC were also downloaded from the GEPIA website [13]. The median expression of each gene 2.5. Loss-of-Function Assays. HepG2 and Huh7 were was considered as the cutoff for high and low expression. obtained from Shanghai Institutes for Biological Sciences The Cox hazard regression analysis was conducted to test (Shanghai, China) and cultured according to the manufac- the association between the expression of any given gene ’ P turer s instruction. The following siRNAs were utilized in this and the survival time. The values for survival analysis were ′ calculated by log-rank test. study: si-RHPN1-AS1: 5 -ACAGCTATATCAGCCAACCAG AGT-3′;si-NC:5′-GTTTACAACACGCTTCCTCTGA-3′. Transfection was performed using a Lipofectamine 2000 2.3. Construction of lncRNA-miRNA-mRNA Network and Reagent (Invitrogen). The real-time PCR was conducted Further Analysis. The lncRNA-miRNA-mRNA network according to published procedures in a previous report [17]. was built following several steps. First, the lncRNA-mRNA The following primers were utilized: RHPN1-AS1 5′-CTAG coexpression patterns were obtained from the GEPIA data- CCAGGAGGTTTCGC-3′ and 5′-TCCGCAACAAGCAC base [13]. The top 200 coexpressing genes by correlation ′ ′ analysis were selected as the potential targets of each ACA-3 , LINC01134 5 -GGAGTTGGCTCCATCCTGAG- ′ ′ ′ ′ lncRNA. Second, the miRNA-lncRNA and miRNA-mRNA 3 and 5 -CTGGCATAGGGGTAACCTCA-3 ,NRAV5 ′ ′ interactions were predicted using the STARBASE database -TCACTACTGCCCCAGGATCA-3 and 5 -GGTGGTCAC [14] . Finally, Cytoscape [15] software was used to visualize AGGACTCATGG-3′,CMB9-22P13.15′-AAGGCCCATGT such lncRNA-miRNA-mRNA networks. AGCATCCC-3′ and 5′-TCTGTAAGGGAGAACCTGCCA- 3′, MKLN-AS 5′-TCTGAAAGCAGCGCTTGGTA-3′ and ′ ′ 2.4. Pathway and Function Enrichment Analysis. Enrichment 5 -GCGGAGTCCTCAAGGTATGG-3 ,MAPKAPK5-AS1 ′ ′ ′ analyses were performed using the ClueGO plug-in [16], in 5 -TCCCTAAGACACGCCGCATA-3 and 5 -CGTGAA order to investigate biological functions of six lncRNAs in TCTCCGCAGAGTGG-3′,ACTB5′-CATGTACGTTGCTA BioMed Research International 5

2.5 2.5 ⁎⁎⁎ ⁎⁎⁎ ⁎ ⁎ 2.0 ⁎⁎⁎ 2.0 ⁎⁎⁎ ⁎ ⁎ 1.5 1.5 levels levels 1.0 1.0

0.5 0.5 Relative MKLN1-AS

Relative MAPKAPK5-AS1 0.0 0.0 Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 (a) (b) 8 5 ⁎⁎⁎ ⁎⁎⁎ 4 6 ⁎ 3 ⁎ 4 levels ⁎ 2 ⁎ ⁎ ⁎ ⁎ 2 1 Relative NRAV levels Relative CMB9-22P13.1 0 0 Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 (c) (d)

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4 4 ⁎⁎⁎ ⁎ ⁎ ⁎ levels ⁎ ⁎ 2 ⁎ 2 Relative RHPN1-AS1

0 Relative LINC01134 levels 0 Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6

Normal Tumor (e) (f)

Figure 3: Upregulation of MAPKAPK5-AS1, MKLN1-AS, CMB9-22P13.1, NRAV, RHPN1-AS1, and LINC01134 in HCC and adjacent tissue samples. (a–f) MAPKAPK5-AS1 (a), MKLN1-AS (b), CMB9-22P13.1 (c), NRAV (d), RHPN1-AS1 (e), and LINC01134 (f) were upregulated in six pairs of HCC and adjacent normal tissues. The symbols of ∗, ∗∗, and ∗∗∗ indicate the P values < 0.05, 0.01, and 0.001, respectively.

TCCAGGC-3′ and 5′-CTCCTTAATGTCACGCACGAT-3′, ential expression analysis on the TCGA cohort using the fi and GAPDH 5′-CACCCACTCCTCCACCTTTG-3′ and 5′ GEPIA webserver. Speci cally, we selected the top 100 upreg- P -CCACCACCCTGTTGCTGTAG-3′. In order to specify the ulated lncRNAs ranked by the values for downstream anal- effect of RHPN1-AS1 on cell viability, CCK-8 kit was utilized ysis (See Materials and Methods). As shown in Figure 1 (a), fi [18]. Meanwhile, a transwell assay was applied to detect cell these lncRNAs had signi cantly higher expression levels in migratory ability [17]. HCC tissues than in nontumor tissues. Survival analysis of these lncRNAs revealed that 15 lncRNAs, including CTB- 2.6. Statistical Analysis. The data analyses were conducted 147N14.6, RP11-286H15.1, RP11-295D4.1, RHPN1-AS1, using GraphPad Prism 6.0 software (GraphPad Software Inc., SNRPEP2, LINC01134, NRAV, NAP1L1P1, CMB9- USA). All values were denoted as the mean ± SD.Student’s t 22P13.1, MKLN1-AS, RP5-864K19.4, CTC-297N7.9, MAP- testwasusedtoevaluatedifferences between two groups, while KAPK5-AS1, MIR210HG, and AP001469.9, were closely the statistical significance from multiple comparisons was associated with overall survival (OS) time of patients with assessed with one-way analysis of variance (ANOVA), and P hepatocellular carcinoma (Figure 1(b), log-rank test, P value value < 0.05 was regarded as statistically significant. < 0.05). Similarly, survival analysis was also conducted to identify lncRNAs associated with disease-free survival 3. Results (DFS). Consequently, only 6 lncRNAs were found to be sig- nificantly associated with shorter DFS in patients in HCC 3.1. Screening of Prognostic lncRNAs in HCC. To identify the (Figures 1(b) and 2(a)), suggesting that these lncRNAs played differentially expressed genes in HCC, we conducted differ- more critical roles in the progression of HCC. It should be 6 BioMed Research International

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0.5 0.5 Normalized NRAV 1 Normalized LINC01134 Normalized RHPN1-AS1 expression Log2 (TPM+1) expression Log2 (TPM+1) expression Log2 (TPM+1) 0.0 0.0 LIHC Normal LIHC Normal LIHC Normal (a) (b) (c)

6 3.0 6 5 2.5 5 4 2.0

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2 1.0 3 1 0.5 Normalized MKLN1-AS expression Log2 (TPM+1) expression Log2 (TPM+1) expression Log2 (TPM+1)

Normalized CMB9-22P13.1 2 0 0.0 Normalized MAPKAPK5-AS1 LIHC Normal LIHC Normal LIHC Normal (d) (e) (f)

Figure 4: LINC01134, RHPN1-AS1, NRAV, CMB9-22P13.1, MKLN1-AS, and MAPKAPK5-AS1 were upregulated in HCC. (a–f) LINC01134 (a), RHPN1-AS1 (b), NRAV (c), CMB9-22P13.1 (d), MKLN1-AS (e), and MAPKAPK5-AS1 (f) were upregulated in HCC. noted that higher expression of these six lncRNAs was asso- (Figures 4(a)–4(f)). Moreover, we also noticed that the expres- ciated with shorter OS and DFS, indicating that they might sions of these lncRNAs were higher in samples of more function as cancer-promoting regulators in HCC. In addi- advanced HCC stages. Specifically, compared with stage I tion, we also investigated the prognostic values of top 100 samples, LINC01134, RHPN1-AS1, NRAV, CMB9-22P13.1, downregulated lncRNAs in HCC and found that three MKLN1-AS, and MAPKAPK5-AS1 were expressed higher in lncRNAs, including CTD-2284J15.1, RP11-612B6.2, and II/III stage samples (P value < 0.05, Figures 5(a)–5(f)). How- RP4-601P9.2, were weakly associated with HCC overall sur- ever, differences in the expression of the above lncRNAs were vival (P value < 0.1, Supplementary Table (S1). These results not observed between the stage I and IV samples, mostly due to indicated that the upregulated lncRNAs might play impor- the small sample size of the stage IV group (n =5). These tant roles in tumorigenesis or progression. results indicated that these prognostic lncRNAs were highly expressed in HCC with advanced stages. 3.2. Validating the Upregulation of Prognostic lncRNAs in HCC Tissues. To validate the upregulation of prognostic 3.4. Biological Functions of the Potential Tumor-Promoting lncRNAs in HCC tissues, we collected 6 pairs of HCC and lncRNAs in HCC. To elucidate the biological functions of adjacent normal tissues. Specifically, the six prognostic these six prognostic lncRNAs, we conducted both correlation lncRNAs, including LINC01134, RHPN1-AS1, NRAV, analysis and gene set enrichment analysis. Overall, the six CMB9-22P13.1, MKLN1-AS, and MAPKAPK5-AS1, were prognostic lncRNAs were highly associated with genes confirmed to be upregulated in most of HCC samples involved in the cancer-associated pathways. Specifically, we (Figure 3, Supplementary S2 ), suggesting that the upregula- observed that CMB9-22P13.1 was involved in regulating cell tion of the six prognostic lncRNAs could also be observed in cycle DNA replication, cell cycle, unwinding of DNA, mitotic our tissue samples. G1-G1/S phases, mismatch repair, and chromosome organi- zation (Figure 6(a)). Moreover, LINC01134 was involved in 3.3. The Expression Patterns of Potential Tumor-Promoting regulating histone acetyltransferase complex through lncRNAs in HCC and Nontumor Tissues. To examine the ATXN7L3, BRD8, and MRGBP and might regulate N- expression patterns of these six potential tumor-promoting acetyltransferase activity through MED24, HCFC1, NAA40, lncRNAs in HCC, we further evaluated their differential and NAT9 (Figure 6(b)). MAPKAPK5-AS1 was associated expression levels between HCC and nontumor tissues. As illus- with the regulation of metabolism of nucleotides, mRNA trated in Figure 4, expressions of LINC01134, RHPN1-AS1, splicing, RNA processing, ribonucleoprotein complex assem- NRAV, CMB9-22P13.1, MKLN1-AS, and MAPKAPK5-AS1 bly, and establishment of protein localization to the telomere were remarkably higher in HCC than in normal samples (Figure 6(c)). NRAV was predicted to be involved in the BioMed Research International 7

2.0 2.5 2.0 1.5 1.5 1.0 1.0

0.5 0.5 Normalized LINC01134 Normalized RHPN1-AS1 expression Log2 (TPM+1) 0.0 expression Log2 (TPM+1) 0.0 Stage I Stage II Stage III Stage IV Stage I Stage II Stage III Stage IV (a) (b) 4 6 5 3 4

2 3 2 1 Normalized NARV 1 expression Log2 (TPM+1) expression Log2 (TPM+1) Normalized CMB9-22P13.1 0 Stage I Stage II Stage III Stage IV Stage I Stage II Stage III Stage IV (c) (d) 3.0 6 2.5 2.0 5 1.5 4 1.0 0.5 3 Normalized MKLN1-AS expression Log2 (TPM+1) 0.0 expression Log2 (TPM+1) Normalized MAPKAPK5-AS1 Stage I Stage II Stage III Stage IV Stage I Stage II Stage III Stage IV (e) (f)

Figure 5: LINC01134, RHPN1-AS1, NRAV, CMB9-22P13.1, MKLN1-AS, and MAPKAPK5-AS1 were upregulated in advanced stage HCC. (a–f) LINC01134 (a), RHPN1-AS1 (b), NRAV (c), CMB9-22P13.1 (d), MKLN1-AS (e), and MAPKAPK5-AS1 (f) were upregulated in advanced stage HCC compared to low grade HCC. regulation of acetyltransferase activity, mRNA binding, 214-3p, miR-488-5p, miR-125b-5p, miR-370-3p, miR-22- Golgi-to-ER retrograde transport, vasopressin-regulated 3p, miR-125a-5p, miR-4782-3p, miR-3619-5p, miR-425-5p, water reabsorption, mitotic spindle organization, cilium miR-340-3p, miR-873-5p, miR-223-3p, and miR-510-5p) organization, and translocation of GLUT4 to the plasma and 142 mRNAs (Figure 7(a)). MAPKAPK5-AS1-mediated membrane (Figure 6(d)). RHPN1-AS1 was associated with ceRNA network consisted of 12 miRNAs (including miR- DNA replication, snRNP assembly, mRNA metabolic pro- 4306, miR-342-3p, miR-154-5p, miR-512-5p, miR-124-3p, cess, mRNA splicing, and processing of capped intron- miR-185-5p, miR-1271-5p, miR-4644, miR-182-5p, miR- containing pre-mRNA (Figure 6(e)). MKLN1-AS was 96-5p, miR-362-5p, and miR-506-3p) and 55 mRNAs involved in the VEGFA-VEGFR2 pathway, endocytosis, (Figure 7(b)). RHPN1-AS1-mediated ceRNA network con- Golgi-to-ER retrograde transport, RNA degradation, and sisted of 8 miRNAs (including miR-196a-5p, miR-342-3p, regulation of RUNX2 expression and activity (Figure 6(e)). miR-345-5p, miR-377-3p, miR-339-5p, miR-196b-5p, miR- These results indicated that these lncRNAs might interact 182-3p, and miR-486-5p) and 54 mRNAs (Figure 7(c)). with genes involved in cancer-associated pathways listed Furthermore, LINC01134 was predicted to bind with miR- above, thereby regulating the activities of these pathways. 17-3p, miR-494-3p, miR-211-5p, miR-324-5p, miR-324-3p, miR-338-3p, miR-216b-5p, and miR-204-5p, thereby pro- 3.5. The lncRNA-Mediated Competing Endogenous RNA moting the expression of 39 mRNAs (Figure 7(d)). The Network in HCC. We constructed a series of competing CMB9-22P13.1-mediated ceRNA network consisted of 2 endogenous RNA (ceRNA) networks in HCC using these miRNAs (including miR-299-3p and miR-522-3p) and 6 six prognostic lncRNAs. The MKLN1-AS-mediated ceRNA mRNAs (including MCM4, MELK, TP73, CKAP2L, TUB, network consisted of 14 miRNAs (including miR-761, miR- and MBOAT1) (Figure 7(e)). These results indicated that 8 BioMed Research International

Cell cycle DNA CHAF1B replication

RMI1 RFC4 RFC5 Mismatch repair DNA replication ATRIP RMI2 E2F8 chromosome FEN1 CDY2A CENPL BRCA1 organization NUF2 RRM1 RAD9B MSH2 Cell cycle GINS1 RNF40 MSH6 RBL1 PCNA SLBP HLTF MCM4 NCAPG2 DMC1 Unwinding of DNA MCM5 SGO1 MCM6 CDC25C ANP32E NUSAP1 CDC6 NAP1L4 Centrosome Cell cycle process MCM8 NDE1 SMC1B organization MCM2 PRIM1 CEP85 Mitotic G1-G1/S CCNE2 DSN1 phases CENPQ CENPJ CCNF PSMC3 E2F2 STIL CENPE NCAPG Meiotic cell cycle LMNB1 RAD51C process HMMR CDCA5 TPX2 ZNF367 Cell cycle phase ENSA TUBG1 EZH2 transition Cell cycle TP73 MELK checkpoints Cell cycle, mitotic

Mitotic cell cycle (a)

ATXN7L3

BRD8

Histone acetyltransferase complex

HCFC1

MRGBP Peptide-lysine-N-acetyltransferase activity

N-Acetyltransferase activity NAA40 MED24

NAT9 (b)

Figure 6: Continued. BioMed Research International 9

ATP5G2 NUDT1 DTYMK ATIC NME1-NME2 DYNLRB1 PPP2R1A SSNA1 NT5C Metabolism of CEP131 MINOS1 2'-Deoxyribo nucleotide nucleotides metabolic process ITPA DGUOK ATP5B ACD TSEN54 SMUG1 Organelle METTL1 SARNP DCTPP1 MKKS biogenesis POP5 and maintenance CEP89 NUP85 DYNLL1 EXOSC1 WDR46 RBM42 DDX49 NUP93 SUPV3L1 DDX54 BUD31 NSUN5 TRMT112 NOC4L NOL12 POLR2G Viral messenger DROSHA UBL5 RP9 LSM7 RNA synthesis POLR2H RNA processing PSMD13 NABP2 DDX56 ARL6IP4 Establishment of GARS Metabolism of RNA CWC15 POLR2J protein localization RPL6 RPS15 RALY to telomere RPL24 TXNL4A TARBP2 CWC27 PRPF19 CCT4 TIMM50 CCT2 RPL38 SNRPF mRNA splicing SNRNP35 AATF Ribonucleo protein SART1 complex biogenesis EIF3D PES1 PQBP1 PPIH PRPF31 SNRPA EIF3B SNRPD1 Ribonucleo protein Ribonucleo protein EIF3G complex assembly complex binding

PYM1 EIF3K ERAL1 ERGIC3 (c)

ARPC2 WIPF2 RHO GTPases SLC38A1 NAA15 activate WASPs and WAVEs NAA40 ARPC4 ABI2 MED22 Acetyltransferase HNRNPD activity RBMX SRSF1 HCFC1 MCRS1 PAFAH1B2 TP53BP1 CPSF6 KLC2 mRNA binding GRSF1 DCTN5

ARF3 RNPS1 TARDBP CREB3 Golgi-to-ER retrograde transport NCBP2 COPZ1 TAF3 Vasopressin-regulated water reabsorption DYNC1I2 Transcription of the DCTN2 HIV genome DYNC2LI1 KIF3A TAF6 CHMP4B RAB11A PRKAG1 RAN YWHAQ BAK1 C2CD3 RALA MAPRE1 FNBP1L Translocation of GLUT4 to the Mitotic spindle BBS12 KPNB1 IFT22 plasma membrane organization YWHAB ZNF207 RPGRIP1L Positive regulation TCTN2 MAP4 DYX1C1 of mitochondrial outer membrane GSK3A IFT52 Cilium organization CEP89 DNAAF5 permeabilization Inner dynein arm involved in WDR35 IFT140 CCDC103 assembly apoptotic TMEM237 TTC26 signaling pathway (d)

Figure 6: Continued. 10 BioMed Research International

NBN TIPIN

CENPS GEMIN5

DSCC1 TGS1 DNA-dependent HMGA1 DNA replication DDX20 snRNP assembly TONSL MCM4

SRSF12 NUP37 POLR2K NUP155 PRPF38A RBM15B mRNA splicing, via NDC1 spliceosome YBX1 PABPC1 PUF60

AGO2 EFTUD2 SF3B4 SF3A3 Processing of MAGOH capped POLR2G intron-containing Regulation of mRNA HSF1 pre-mRNA metabolic process PEX2 (e)

RASA1

ITGAV BECN1 VEGFA-VEGFR2 HNRNPK CTNNA1 AHCYL1 pathway Receptor metabolic process WDR35 NSF TNPO1 G3BP2 WASF2 AP2B1 NCKAP1 CHMP5 EPS15 SH3GLB1 PAFAH1B2 FBXW11 TTC26 COPG2 Protein complex localization PDCD6IP KIF3A BBS12 NDC1 Golgi-to-ER IQCETRAF3IP1 RAB10 RHO GTPases retrograde transportKIF2A activate WASPs RAB18 and WAVEs WIPF2 Endocytosis CAPZA1 SMAD2 ACTR2 CSNK1A1 EFCAB7 RAB5C ARF3 CNOT6 CUL1 Activation of SMO

XRN2 TTC37 CBFB RNA degradation Regulation of DDX6 CNOT8 RUNX2 expression PSME3 and activity CNOT4 (f)

Figure 6: Enrichment analysis of differentially expressed lncRNAs in HCC. (a–f) We predicted the potential functions of CMB9-22P13.1 (a), LINC01134 (b), MAPKAPK5-AS1 (c), NARV (d), RHPN1-AS1 (e), and MKLN1-AS (f) in HCC. these prognostic lncRNAs had the potential to sponge miR- (Figure 8(c)) cells. Subsequently, the CCK-8 assay showed NAs, thereby regulating the expression levels of related that the proliferation of both HepG2 (Figure 8(b)) and protein-coding mRNAs. Huh7 (Figure 8(d)) cells was suppressed through RHPN1- AS1 knockdown. The transwell assay was also utilized to 3.6. Inhibition of HCC Cell Proliferation and Migration In measure the migration ability of HCC cells. As illustrated in Vitro Caused by lncRNA RHPN1-AS1 Knockdown. As Figure 8, the number of migrating cells in the RHPN1-AS1 RHPN-AS1 achieved the most statistically significant associ- silencing group was remarkably reduced compared to that ation with both overall survival and disease-free survival, we in the control groups of both HepG2 (Figures 8(e)-8(f)) further explored its role in HCC progression. Functional pre- and Huh7 (Figures 8(g)-8(h)) cells. These results indicated diction revealed that this lncRNA was related to the regula- that RHPN1-AS1 silence could efficiently suppress HCC cell tion of DNA replication and mRNA splicing. Moreover, proliferation and migration. RHPN1-AS1 has been reported to act as an oncogene in mul- tiple human cancers [19–21], such as nonsmall cell lung can- 4. Discussion cer, uveal melanoma, and breast cancer. However, the functional roles of this lncRNA in HCC remained largely To date, a large number of long noncoding RNAs (lncRNAs) unknown. The knockdown efficiency assay showed that the have been found to participate in carcinogenesis of several expression of RHPN1-AS1 was suppressed compared to the malignant tumors. To explore the functional roles of control group in HepG2 (Figure 8(a)) and Huh7 lncRNAs in HCC, we conducted a systematic analysis to BioMed Research International 11

ZMPSTE24

SH3GLB1 CNOT4 KLHL12 NUB1 TTF2 DHX40 ATF1

DDX52

KIF3A ABCF2 CASP8 SF3A1 PTBP3 CAND1 hsa-miR-125b-5p SMAD2 BBX TAOK1 CTNNA1 USP14 DRAM2 STX7 ESYT2 CAB39 METAP1 MTF1 CSNK1A1 ATP2C1APPBP2 NARS ZKSCAN5 SF3A3 UBE3C CPSF6 CDC27 PTPN12 hsa-miR-425-5p SET DNAJB6 JAK1 NSUN4 YWHAG NAA15 CNOT6 RNF4 MPZL1 G3BP2 WDR35 AGGF1 NCKAP1 RUNDC1 EPS15 TNPO3 hsa-miR-125a-5p GLG1 CANT1 ARF3 MATR3 TAX1BP1 ANKRD40 EIF4G2 C11orf58 NSF TNPO1 hsa-miR-214-3p HNRNPK GRSF1 AHCYL1 ATXN1L DUSP18 hsa-miR-223-3p USP46 hsa-miR-3619-5p FAM98A GEMIN5 hsa-miR-370-3p COPS2 RASA1 FAM114A2 ZNF746 CAP1 SYNCRIP CLOCK IPO7 MAFG CSDE1 hsa-miR-4782-3p GPR107 TARDBP hsa-miR-761 PLEKHA3 RAB10 CBFB PAFAH1B2 ASXL2 G3BP1 UBLCP1 MKLN1-AS EFCAB7 hsa-miR-873-5p SDAD1 RBM27 TWF1 CAPZA1 RAB18 UBE2Z CBX3 ZC3HAV1 GNB1 BECN1 AGFG1 TWISTNB MED1 PLEKHA8 WIPF2 MTPN LARP1 GSPT1 TTL CDYL SRPK2 AP2B1 hsa-miR-340-3p WASF2 KDM3B KIF2A SPTY2D1 YTHDF2 ZNF207 SRP72 FAM120A TM2D1 ACTR2 PSME3 hsa-miR-22-3p PCNPhsa-miR-510-5p ZFP91 DCAF7 RPE JKAMP CNOT8

TOP1 hsa-miR-488-5p SWAP70 TMEM209

DDX6 PACRGL BTBD10 RNF141 FAF2 RAB5C

KPNB1 ZMYM4 VAPA GNAI3

(a)

ITPA UBL7 MARS C19orf52 PKMYT1 TFPT

COG1 SARNP METTL1 POLR2G

DYNLL1 hsa-miR-185-5p POLR2H BUD31 MCRS1

ATP6V1F hsa-miR-4306 SAP30BP ATP5B hsa-miR-512-5p hsa-miR-362-5p SNRPD1 hsa-miR-4644 RNF7 VPS11 PSMD13 MED22

DYNLRB1 ERAL1 EIF3B CCDC137 DDX54 hsa-miR-124-3p hsa-miR-154-5p POP5

CWC15 hsa-miR-1271-5p TSEN54 hsa-miR-506-3p MYL6 TMSB10 MRPS34 hsa-miR-96-5p CAPNS1

HN1 AATF ZNF580 ALDOA hsa-miR-182-5p PRPF31 MANBAL PTOV1 NME1-NME2 WIBG MFSD5 TIMM50 PFDN5

hsa-miR-342-3p DDX49 PHF14 ROMO1

MRPL27

MYEOV2 SNRPA

UNC45A PPIA SEC61G

(b)

Figure 7: Continued. 12 BioMed Research International

PRPF38A OSGIN2 SERBP1

NCSTN LRRC42 KIAA1429 CYHR1 VSIG10 TMEM68 UBAP2L YWHAZ MANBAL NDRG1 TRAM1 hsa-miR-345-5p KTI12 CCDC51 C8orf33 hsa-miR-339-5p hsa-miR-377-3p UTP23 SLC26A11 ILF2 STK3 APITD1 NME6 hsa-miR-342-3p ATP6V1C1 MED8 TAF6 RHPN1-AS1 USP1 UBAC2 FAM49B OTUD6B C8orf37 RAD21 hsa-miR-486-5p NOMO1 MPZL1 MCM4 NRAS PIGU hsa-miR-196a-5p hsa-miR-182-3p LAPTM4B PTK2 RNF220 hsa-miR-196b-5p ZHX2 PTDSS1 ZNF706 DFFA COG5 GRPEL2 SMARCC1 DPY19L4 UBR5 SYNGR2 TRIM65

HMGA1 PARS2 E2F5

(c)

UNC119B ZBTB40

LUC7L3 PDDC1 HPS4 SLC26A6 FUBP1 SLC30A5 hsa-miR-324-3p GAN

hsa-miR-494-3p RNF44 PRR14L TP73 MBOAT1

hsa-miR-324-5p NARFL MELK

EED ZNF12 hsa-miR-17-3p hsa-miR-522-3p UBFD1 hsa-miR-299-3p CMB9-22P13.1 LINC01134 AKT1S1 GPR107 MCM4 FAM192A TUB DNAJC5 E2F4 TARDBP hsa-miR-216b-5p IP6K1 CKAP2L MLLT6 MAFG hsa-miR-204-5p

hsa-miR-338-3p NAA40 KIAA0907 SOCS7 hsa-miR-211-5p SMYD5

JRK ATXN7L3 FBXL19 DAZAP1 ABCC5

CHORDC1 ANKRD52 MYPOP TRIM65 COPS7B CRTC2

(d) (e)

Figure 7: Construction of prognostic lncRNA-mediated competing endogenous RNA (ceRNA) network in HCC. The MKLN1-AS-, MAPKAPK5-AS1-, RHPN1-AS1-, LINC01134-, and CMB9-22P13.1-mediated ceRNA networks in HCC are displayed in (a–e). The red, green, and purple nodes represent the protein-coding mRNAs, miRNAs, and lncRNAs, respectively. screen out prognostic lncRNAs. Specifically, we identified 6 ing cell cycle, DNA replication, histone acetyltransferase upregulated lncRNAs in HCC samples, including LINC01134, complex, fatty acid oxidation, and lipid modification. Further- RHPN1-AS1, NRAV, CMB9-22P13.1, MKLN1-AS, and more, ceRNA network analysis depicted that these lncRNAs MAPKAPK5-AS1. Higher expression levels of these lncRNAs could bind to a series of miRNAs to promote HCC were associated with advanced cancer stages and worse prog- progression. nosis in HCC. Enrichment analysis revealed that these lncRNAs have been reported to be key regulators in lncRNAs played a crucial role in HCC progression through HCC. For example, Lnc-UCID enhanced cell cycle and affecting multiple cancer-related biological processes, includ- growth [18]. HAND2-AS1 induced cancer stem cell self- BioMed Research International 13

1.5 1.5

0.8 ⁎⁎⁎ 1.5 ⁎⁎⁎ 1.0 1.0 0.6 ⁎⁎ ⁎⁎ 1.0 0.4 0.5 0.5 0.5 of RHPN1-AS1 0.2 of RHPN1-AS1 Relative expression Relative expression OD value (450nm) OD value (450nm) 0.0 0.0 0.0 0.0 Day 1 Day 2 Day 3 Day 4 Day 1 Day 2 Day 3 Day 4

NC NC si-RHPN1-AS1 si-RHPN1-AS1 (a) (b) (c) (d) 1.5 NC si-RHPN1-AS1

1.0

0.5 negative control Relative migration to 251 um 251 um

0.0 NC si-RHPN1-AS1 (e) (f) 1.5 NC si-RHPN1-AS1

1.0

0.5 negative control Relative migration to 251 um 251 um

0.0 NC si-RHPN1-AS1 (g) (h)

Figure 8: The proliferation and migration of HCC cells in vitro were inhibited by lncRNA RHPN1-AS1 knockdown. (a, c) The knockdown efficiency assay showed that the levels of RHPN1-AS1 were suppressed in HepG2 (a) and Huh7 cells (c) when compared to the siNC group. (b, d) RHPN1-AS1 knockdown could suppress both the HepG2 (b) and Huh7 (d) cell proliferation. (e, f) RHPN1-AS1 knockdown could suppress both the HepG2 cell migrations. (f–h) RHPN1-AS1 knockdown could suppress both the Huh7 cell migrations. The symbols of ∗, ∗∗, and ∗∗∗ indicate the P values < 0.05, 0.01, and 0.001, respectively. renewal in HCC [22]. The abnormal expression of lncRNAs LINC01134, NRAV, CMB9-22P13.1, MKLN1-AS, and could help predict the progression and prognosis of HCC, MAPKAPK5-AS1 were related to shorter OS and DFS time and ncRNA CDKN2BAS is indicative of poor prognosis of among HCC patients. Our study showed that these lncRNAs HCC as it could lead to metastasis [23]. However, the func- could serve as prognostic biomarkers for HCC. tions of most lncRNAs in HCC remain unclear. This study Among these lncRNAs, RHPN1-AS1 was reported to act for the first time showed that RHPN1-AS1, LINC01134, as an oncogene in multiple human cancers, including non- NRAV, CMB9-22P13.1, MKLN1-AS, and MAPKAPK5- small cell lung cancer [19], uveal melanoma [20], and breast AS1 were overexpressed in HCC, and a higher expression cancer [21]. LINC01134 and MKLN1-AS were found to pro- of these lncRNAs was observed in advanced-stage HCC sam- mote HCC progression or metastasis by previous studies ples as compared to early-stage HCC samples. Moreover, we [24–28], suggesting that the two lncRNAs might be more demonstrated that higher expression levels of RHPN1-AS1, specific in HCC, while NRAV and CMB9-22P13.1 were 14 BioMed Research International rarely reported as an oncogene in cancers. Similar with Data Availability RHPN1-AS1, MAPKAPK5-AS1 was frequently reported to act as tumor-promoting lncRNAs in multiple cancers like Previously reported gene expression and clinical data were thyroid cancer, colorectal cancer, glioma, lung cancer, and used to support this study and are available at TCGA data HCC [29–33]. However, little was known when it comes to portal (https://portal.gdc.cancer.gov/). These prior studies their roles in HCC. In this study, we conducted coexpression (and datasets) are cited at relevant places within the text as analysis and enrichment analysis of these lncRNAs in HCC. references. Very interestingly, we have found that these lncRNAs are important players in regulating HCC proliferation and Conflicts of Interest metabolisms. LINC01134 was involved in regulating histone fl acetyltransferase complex, while the ceRNA network analysis The authors declare that they have no con icts of interest. showed that LINC01134 could bind to miR-17-3p and miR- 494-3p, which had been key regulators in HCC progression. Authors’ Contributions For example, miR-17 could promote hepatocarcinogenesis [34], and miR-494 could promote the development of HCC C. S. and B. Z. designed this study. Y.F., X.H., and K.M. by targeting the SIRT3 and PI3K/AKT pathway [35]. conducted the data analysis, visualization, and experiments. RHPN1-AS1 was associated with the regulation of DNA rep- Y.F. and C. S. contributed to the writing of the paper and fi lication and mRNA splicing, and miR-196a-5p was consid- setting of gures. ered as a potential target of RHPN1-AS1. In HCC, microRNA-196a/-196b could affect the JAK/STAT pathway Supplementary Materials [36]. Meanwhile, CMB9-22P13.1 participated in regulating fi tumor-growth-related biological processes, including cell Supplementary Table S1: the statistical signi cance of sur- cycle and DNA replication. CMB9-22P13.1 also affected the vival analysis for the three downregulated lncRNAs. Supple- activity of miR-299-3p and miR-522-3p. miR-522 has been mentary Table S2: the expression levels of six prognostic reported to affect HCC proliferation by targeting DKK1 lncRNAs in the six pairs of HCC and nontumor tissues. and SFRP2 [37]. MKLN1-AS was involved in regulating the (Supplementary Materials) VEGFA-VEGFR2 pathway, endocytosis, Golgi-to-ER retro- grade transport, and RNA degradation. The ceRNA analysis References showed that MKLN1-AS could bind to 14 miRNAs. Among [1] Chinese Human Proteome Project (CNHPP) Consortium, them, miR-125b-5p could inhibit HCC cell growth and metas- Y. Jiang, A. 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