Hindawi BioMed Research International Volume 2021, Article ID 4873678, 12 pages https://doi.org/10.1155/2021/4873678

Research Article Study of the G Nucleolar 2 Value in Liver Hepatocellular Carcinoma Treatment and Prognosis

Yiwei Dong,1 Qianqian Cai,2 Lisheng Fu,1 Haojie Liu,1 Mingzhe Ma,3 and Xingzhong Wu 1

1Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Key Lab of Glycoconjugate Research, Ministry of Public Health, Shanghai 200032, China 2Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China 3Department of Gastric Surgery, Fudan University Shanghai Cancer Center, Shanghai 200025, China

Correspondence should be addressed to Xingzhong Wu; [email protected]

Received 10 May 2021; Accepted 29 June 2021; Published 20 July 2021

Academic Editor: Tao Huang

Copyright © 2021 Yiwei 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.

LIHC (liver hepatocellular carcinoma) mostly occurs in patients with chronic liver disease. It is primarily induced by a vicious cycle of liver injury, inflammation, and regeneration that usually last for decades. The G protein nucleolar 2 (GNL2), as a protein- encoding , is also known as NGP1, Nog2, Nug2, Ngp-1, and HUMAUANTIG. Few reports are shown towards the specific biological function of GNL2. Meanwhile, it is still unclear whether it is related to the pathogenesis of carcinoma up to date. Here, our study attempts to validate the role and function of GNL2 in LIHC via multiple databases and functional assays. After analysis of gene expression profile from The Cancer Genome Atlas (TCGA) database, GNL2 was largely heightened in LIHC, and its overexpression displayed a close relationship with different stages and poor prognosis of carcinoma. After enrichment analysis, the data revealed that the coexpressed with GNL2 probably participated in ribosome biosynthesis which was essential for unrestricted growth of carcinoma. Cell functional assays presented that GNL2 knockdown by siRNA in LIHC cells MHCC97-H and SMCC-7721 greatly reduced cell proliferation, migration, and invasion ability. All in all, these findings capitulated that GNL2 could be a promising treatment target and prognosis biomarker for LIHC.

1. Background been the most commonly used biomarker in LIHC man- agement [4, 13, 14]. However, LIHC is classified as a Liver cancer is composed of primary carcinoma and meta- complicated disease accompanied by several risk factors static carcinoma [1, 2]. As far as the most common caused pathogenic mechanisms. Thus, to characterize inducers of carcinoma-associated mortality in the world, LIHC only by a single biomarker is not feasible. To explore primary liver carcinoma ranks second, thus becoming a the pathogenesis of LIHC and uncover the candidate bio- great public health challenge [3–5]. Primary liver carci- markers become urgently needed. noma consists of liver hepatocellular carcinoma (LIHC), As a protein-encoding gene, the G protein nucleolar 2 intrahepatic cholangiocarcinoma (iCCA), and other rarely (GNL2) is also named by nucleostemin (NS), which is essen- seen neoplasms [6, 7]. Mostly, LIHC occurs in patients tial for stem cell growth and development [15, 16]. GNL2 is with chronic liver disease, and it is generally caused by a highly expressed in tissues, such as testis and bone marrow vicious cycle of liver damage, inflammation, and regenera- [17, 18]. Presently, there are no researches on the particular tion that usually span several decades [8–11]. Like other biological function of GNL2, and little is known about its malignant neoplasms, the widely generated biological alter- relationship with carcinoma pathogenesis. ation in LIHC pathogenesis comprises activated oncogene Here, our study is aimed at investigating whether there is and inactivated neoplasm inhibitor genes [12]. Since its an association of GNL2 expression with the prognosis in discovery over 60 years ago, alpha-fetoprotein (AFP) has LIHC patients. In addition, our study also wants to validate 2 BioMed Research International

10

8

6 P value 10 4 –Log 2

0 –4 –2 0 2 4 Log2 fold change

Figure 1: Volcano plot illustrated the DEGs existing in liver carcinoma samples and normal ones based on TCGA dataset. Genes with upregulation were presented as the red dot, while those with downregulation were shown as the blue dot. its inhibitory effect on LIHC cell proliferation, migration, and annotation enrichment, we utilized the online tool Enrichr invasion by in vitro experiments. (https://maayanlab.cloud/Enrichr/). Enrichment annota- tions were presented as Kyoto Encyclopedia of Genes and 2. Material and Methods Genomes (KEGG) pathway and (GO) bio- logical process. Data were plotted and visualized using an fi ff 2.1. Identi cation of the Di erentially Expressed Genes online platform http://www.bioinformatics.com.cn. (DEGs) in TCGA Database. The Cancer Genome Atlas (TCGA) database, as the National Cancer Institute (NCI) 2.5. Cell Culture and Transfection. The human LIHC cell and the National Institute (NHGRI) sup- lines MHCC97-H and SMCC-7721 were obtained from the ported the project, was employed to obtain mRNA expres- China Center for Type Culture Collection in 2020 and sion profile of liver carcinoma and normal samples. It could authenticated by short tandem repeat analysis. All cells were offer all-inclusive maps of the main alterations of genomic routinely cultured in DMEM medium (Thermo Fisher Scien- in multiple sorts of carcinomas. ∣log fold change ðFCÞ ∣ >1 2 tific, Inc., USA) supplemented with 10% FBS (Thermo Fisher and P value <0.05 were considered to be parameters for iden- Scientific, Inc., USA) in an incubator with 5% CO at 37°C. tifying the DEGs. The protein-protein network (PPI) of the 2 SiRNA targeting GNL2 (si-GNL2) and a negative control selected DEGs was constructed by the Search Tool for the siRNA (si-NC) were acquired from GeneCopoeia, Inc. Retrieval of Interacting Genes (STRING) database. (USA). When MHCC97-H and SMCC-7721 cells confluence 2.2. Gene Expression Profiling Interactive Analysis (GEPIA) reached up to 80% on the following day, they were trans- Database. GEPIA is an interacting online server for the anal- fected with indicated siRNAs utilizing Lipofectamine 2000 ffi ysis of RNA sequencing data of tumorous and healthy sam- (Invitrogen, USA) as the manual described. Reduced e - ples from the Genotype-Tissue Expression (GTEx) and ciency of GNL2 was determined by qRT-PCR at 48 h post- TCGA datasets. The URL of GEPIA is http://gepia.cancer- transfection. The siRNAs used were as follows: si-GNL2-1, ′ ′ pku.cn. In this study, we used GEPIA to determine GNL2 5 -CACGTGTGATTAAGCAGTCATCATT-3 ; si-GNL2-2, expression in different stages of liver carcinoma and then 5′-CCATACAAAGTTGTCATGAAGCAAA-3′; si-NC, 5′ perform patient survival analysis and identify similar genes. -UUCUCCGAACGUGUCACGUTT-3′. 2.3. Kaplan-Meier Plotter Database. The Kaplan-Meier plot- 2.6. Quantitative Reverse-Transcription PCR (qRT-PCR) ter (https://kmplot.com/analysis/), functioning as a web tool, Assay. Overall RNA was harvested from indicated cells utiliz- is used to validate survival biomarkers in the light of meta- ing TRIzol reagent (Invitrogen, USA). qRT-PCR was carried analysis. The information of gene expression profile and out on a Bio-Rad Single Color Real-Time PCR system (Bio- various survival data comprising overall survival (OS), Rad Laboratories, Inc., USA) with SYBR-Green Real-Time relapse-free survival (RFS), and disease-specific survival PCR Master Mix (Toyobo Life Science, Japan). β-Actin was (DSS) come from the well-known public databases, including used as an internal control. GEO (only Affymetrix microarrays), EGA, and TCGA. Kaplan-Meier plotter was taken to generate the survival curves of GNL2, and log rank P values, as well as hazard ratio 2.7. Cell Proliferation Assay. 2,000 of si-NC- and si-GNL2- (HR) with 95% confidence intervals, were calculated. transfected cells were plated in per well of 96-well plates, followed by maintaining 0, 24, 48, and 72 h at 37°C. 10 μlof 2.4. Biological Process and Pathway Enrichment Analysis. The CCK-8 reagent was added into each well at the indicated top 1000 genes coexpressed with GNL2 (sorted by Pearson time, followed by another 2 h incubation. The wavelength correlation coefficient) in LICH-tumor and LICH-normal at 450 nm was read in a Varioskan Flash spectrophotometer dataset were identified by GEPIA. To assess the gene- (Thermo Fisher Scientific, Inc., USA). BioMed Research International 3

GPM6A

ICM5

TBXA2R CLEC4M GRPR DKKL1 CHRM3 MARCO SGCD ADRA1A STAB2 WDFY2 CDC37L1 TSPO PVALB DBH SP2 ADRA2B CRHBP TUBDNASE1L3 NAAA ECM1 UCN2 TRIB1 NTF3 MAT1A CIDEB GNL2 MAP2K1 INS-IGF2 SPN ALB ZFP36 CXCL12 MMP14 HIP1 TBX21 PLG IL7R ETS2 EGR1 LGALS1ADAMTS13 POSTN LCAT CCL23 DUSP3 APOF CSRNP1 FCN2 TSLP DSG1 RCAN1 FCN3 CFP LIFR C1RL MED1 C1R CSTA MASP1 C8A IL5RA GHR COLEC10 C6 GYS2

CPED1

CYP4A11 ALDH8A1

TTC36 FMO1 CYP2C8 ALDH2 ALDH6A1 ADH4 ACADSB UGT3A1 CNDP1 LRAT GLYATL1 ACADS ERLIN1 ETFDH

NAT2 BCO2

MRO

DCDC2 MAP7D2 GPR20 MRPL43 VIPR1

KIF13B GDPD3 RPL27A BMPER PTH1R

KLC3 RPS25 GDF2 PNPT1

CLEC4G SENP7 PFIA1

CLEC1B PIAS1 CASKIN1

CPEB3

Figure 2: PPI network of the DEGs. Nodes displayed , and edges exhibited the interactions between proteins.

2.8. Transwell Assay. For cell migration assay, Matrigel needed with 10% FBS, followed by maintaining 24 h at 37°C. Then, not to be precoated in the Transwell chamber (Corning, NY). migrated cells underside were rinsed by PBS, fixed by metha- The Transwell upper chamber was added 50,000 si-NC- or si- nol, and stained by DAPI. Positive cells were photographed GNL2-transfected cells suspended in serum-free DMEM and counted under a microscope. Five random fields were medium, and the lower chamber was added DMEM medium selected and counted the average. For cell invasion assay, 4 BioMed Research International

⁎⁎⁎ 8 F value = 5.19 50 Pr(>F) = 0.00163

7 40

30 6

20 5 GNL2 gene expression 10 4

0 Normal Tumor Stage I Stage II Stage III Stage IV (n = 50) (n = 371) TCGA samples (a) (b)

Figure 3: Highly expressed GNL2 was in LIHC, and its higher expression was associated with carcinoma stages. (a) To analyze GNL2 expression level in normal and tumor tissues of the liver based on TCGA dataset. ∗∗∗P <0:001. (b) Analysis of GNL2 expression level in different liver carcinoma stages based on GEPIA dataset.

Overall survival Disease free survival 1.0 1.0

Logrank P=9.2e–06 Logrank P = 0.011 HR(high) = 1.5 0.8 HR(high) = 2.2 0.8 P(HR) = 1.5e–05 P(HR) = 0.011 n(high) = 182 n(high) = 182 n(low) = 182 n(low) = 182 0.6 0.6

0.4 0.4 Percent survival Percent survival

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0.0 0.0 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Months Months Low GNL2 Group Low GNL2 Group High GNL2 Group High GNL2 Group (a) (b)

Figure 4: GNL2 high expression was a poor prognosis factor for LIHC. (a) GEPIA analysis of the OS curves of LIHC patients with highly expressed or lowly expressed GNL2. (b) GEPIA analysis of the DFS curves of LIHC patients with highly expressed or lowly expressed GNL2.

2 mg/mL Matrigel was precoated in the upper chamber. Meier analysis and log-rank test. The Student t-test was The remaining procedures were the same as the cell migra- employed to compare the difference of the si-GNL2- tion assay. transfected group with the si-NC-transfected group. The great statistical difference indicated P <0:05. 2.9. Statistical Analysis. Our data were analyzed by the SPSS software (IBM Corp., USA) and were represented as the 3. Results mean ± SD. The Student t-test was employed to analyze the difference in mRNA expression from TCGA database. The 3.1. Identification of the DEGs and Overexpressed Gene GNL2 correlation existing in expression and pathological stage of in LIHC. As indicated before, to define the DEGs in liver car- carcinoma was assessed by one-way ANOVA. The differ- cinoma and normal samples, we obtained gene expression ences in survival ratio between highly expressed GNL2 and information from TCGA database. Totally, 361 DEGs were lowly expressed GNL2 groups were detected by Kaplan- selected, comprising 283 genes with increased expression iMdRsac International Research BioMed ih52 High Probability 121 High Probability Low Low 1.0 0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6 0.8 1.0 GNL2 expression Number atrisk GNL2 expression Number atrisk 243 98 0 0 High Low High Low Overall survival(hepatitisvirus-positive) 132 20 20 50 24 55 40 40 22 62 12 30 Overall survival Time (months) Time (months) (a) (c) 60 60 35 16 7 3 HR =2.28(1.19–4.35) HR =1.89(1.33–2.68) Logrank P=0.00032 80 80 16 Logrank P=0.011 3 1 5 100 100 1 5 0 1 Figure 120 120 0 1 0 1 :Continued. 5: ih177 High Probability 43 High Probability Low Low 1.0 0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6 0.8 1.0 GNL2 expression GNL2 expression Number atrisk Number atrisk 193 124 0 0 High Low High Low Overall survival(hepatitisvirus-negative) 20 41 69 20 11 68 Relapse-free survival 40 19 28 40 26 5 Time (months) Time (months) (d) (b) 60 13 7 60 13 3 HR =2.43(1.51–3.91) HR =1.56(1.16–2.09) Logrank P=0.00015 80 Logrank P=0.003 3 3 80 2 8 100 1 2 100 1 3 120 0 1 120 0 1 5 6

ih74 High Probability Low 1.0 0.0 0.2 0.4 0.6 0.8

ih130 High Probability Low 1.0 0.0 0.2 0.4 0.6 0.8 GNL2 expression Number atrisk 69 0 GNL2 expression Number atrisk 232 0 Relapse-free survival(hepatitisvirus-negative) High Low High Low 20 16 28 125 20 55 Disease-specific survival 40 40 8 9 Time (months) 26 57 Time (months) (e) (g) 60 60 34 4 6 8 HR =2.14(1.37–3.34) HR =2(1.19–3.35) Logrank P=0.0077 Logrank P=6e–04 80 80 16 3 1 3 100 100 1 5 1 1 Figure 120 120 0 1 0 0 :Continued. 5: ih58 High Probability 70 High Probability Low Low 1.0 0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6 0.8 1.0 GNL2 expression GNL2 expression Number atrisk Number atrisk 107 69 Disease-specific survival(hepatitisvirus-negative) 0 0 Relapse-free survival(hepatitisvirus-positive) High Low High Low 20 18 32 20 20 58 40 17 9 40 24 7 Time (months) Time (months) (f) (h) 60 2 7 60 13 3 iMdRsac International Research BioMed HR =1.93(1.16–3.2) HR =2.31(1.3–4.09) Logrank P=0.0097 Logrank P=0.0032 80 1 2 80 2 8 100 0 1 100 1 3 120 0 1 120 0 0 BioMed Research International 7

Disease-specific survival (hepatitis virus-positive) 1.0 HR = 2.91 (1.27 – 6.66) Logrank P = 0.008 0.8

0.6

0.4 Probability

0.2

0.0 0 20 40 60 80 100 120 Time (months) Number at risk Low 97 53 29 15 5 1 1 High 54 26 13 4 1 0 0

GNL2 expression Low High (i)

Figure 5: Prognosis value of GNL2 in LIHC was confirmed by the Kaplan-Meier plotter dataset. The impacts of GNL2 expression levels on the (a) OS, (d) RFS, and (g) DSS of LIHC patients. The impacts of GNL2 expression levels on the (b) OS, (e) RFS, and (h) DSS of LIHC patients without hepatitis virus infection. The impacts of GNL2 expression levels on the (c) OS, (f) RFS, and (i) DSS of LIHC patients with hepatitis virus infection. and 78 genes with decreasing expression (Figure 1 volcano GNL2 had shorter OS, RFS, and DSS compared to those with plot). Figure 2 displayed the PPI network, 110 nodes (pro- the lowly expressed group, in line with the GEPIA analysis. teins) and 159 edges (proteins interactions). Besides, it was Given the high incidence of hepatocellular carcinoma was observed that the degree of GNL2 was high which partly mainly due to the prevalence of hepatitis virus infection, we revealed the potential role of GNL2 as a candidate biomarker further evaluated the prognosis of LIHC patients stratified for LIHC. according to hepatitis virus infection. The results showed that the hepatitis virus did not affect the prognosis of GNL2 3.2. Validation of the Relationship between the Stages of Liver (Figures 5(b), 5(c), 5(e), 5(f), 5(h), and 5(i)). Carcinoma and the Expression of GNL2. TCGA database analysis showed that LIHC tumor tissues highly expressed 3.4. Biological Process and Pathway Enrichment Analysis. We GNL2 in comparison with normal tissues (Figure 3(a)). conducted KEGG and GO enrichment analyses of genes Then, GEPIA analysis discovered the associations between coexpressed with GNL2 in LIHC tumor and normal tissues various pathological carcinoma stages of LIHC patients and to validate the function of GNL2 in liver carcinoma. GNL2 expression level. It was found that GNL2 expression Figure 6(a) displayed the results of KEGG pathway enrich- was significantly associated with the pathological carcinoma ment, including cell cycle, mRNA surveillance pathway, pro- stages of LIHC (Figure 3(b), P =0:00163). Our results dem- teasome, ribosome, RNA transport, DNA replication, onstrated that GNL2 exhibited an important part in clinical Fanconi anemia pathway, ribosome biogenesis in eukaryotes, practice. spliceosome, and homologous recombination. Figure 6(b) showed the enriched biological processes of the genes coex- 3.3. Prognostic Value of GNL2 for LIHC Patients. Subse- pressed with GNL2 as the following: rRNA processing quently, we employed GEPIA to investigate the relationship of (GO:0006364), rRNA metabolic process (GO:0016072), GNL2 expression with LIHC patient’sprognosis.Figures4(a) ribosome biogenesis (GO:0042254), mRNA processing and4(b)demonstratedtheOSandDFScurvesofLIHCpatients (GO:0006397), translation (GO:0006412), RNA splicing via with different GNL2 expression levels. Kaplan-Meier analysis transesterification reactions with bulged adenosine as nucle- plus the log-rank test was taken to assess the impacts of the ophile (GO:0000377), mRNA splicing via spliceosome expression of GNL2 on OS and DFS. Compared to the lowly (GO:0000398), gene expression (GO:0010467), cellular mac- expressed GNL2 group, the highly expressed GNL2 group romolecule biosynthetic process (GO:0034645), and ncRNA exhibited a shorter OS and DFS (log rank P =9:2e − 06 and processing (GO:0034470). 0.011, respectively). Additionally, we further verified that high expression of GNL2 led to poor prognosis in LIHC patients 3.5. Cell Functional Results of GNL2 Downregulation in LIHC by Kaplan-Meier plotter database analysis. Figures 5(a), 5(d), Cells. We transfected siRNAs into MHCC97-H and SMCC- and 5(g) showed that LIHC patients with highly expressed 7721 cells to evaluate the effects of ablated GNL2 on cell 8 BioMed Research International

KEGG pathway analysis –Log (P value) 10

Homologous recombination 15

Spliceosome

Ribosome biogenesis in eukaryotes 12 Fanconi anemia pathway

DNA replication

9 RNA transport

Ribosome

Proteasome 6

mRNA surveillance pathway

Cell cycle 3 0.16 0.20 0.24 Gene ratio

Count 25 10 15 30 20 35 (a)

Biological process analysis –Log (P value) 10 28 rRNA processing (GO:0006364)

rRNA metabolic process (GO:0016072)

Ribosome biogenesis (GO:0042254) 24 mRNA processing (GO:0006397)

Translation (GO:0006412)

RNA splicing, via transesterification reactions with bulged adenosine as nucleophile (GO:0000377) 20 mRNA splicing, via spliceosome (GO:0000398)

gene expression (GO:0010467)

Cellular macromolecule biosynthetic process (GO:0034645) 16 ncRNA processing (GO:0034470)

0.1 0.2 Gene ratio

Count 65 50 55 70 60 75 (b)

Figure 6: KEGG and GO analyses of genes coexpressed with GNL2 in LIHC. (a) Dot bubble chart of enriched GO biological process terms. (b) Dot bubble chart of enriched KEGG pathways. function (Figures 7(a) and 7(b)). CCK-8 assay data showed down largely curbed MHCC97-H and SMCC-7721 cell that si-GNL2-transfected cell proliferation was significantly proliferation, invasion, and migration. hindered in comparison with control si-NC-transfected cells after 24, 48, and 72 h treatment (Figures 7(c) and 7(d)). In 4. Discussion addition, Transwell assay data revealed that transfection of si-GNL2 in LIHC cells could suppress invasion and migra- LIHC is a malignant neoplasm that often occurs in the liver, tion abilities (Figures 8(a)–8(d)). Therefore, GNL2 knock- which is related to drinking, viral hepatitis, eating moldy BioMed Research International 9

1.5 MHCC97-H 1.5 SMCC-7721

1.0 1.0

⁎ 0.5 0.5 ⁎⁎ Relative mRNA expression Relative mRNA expression 0.0 0.0 si-NC si-GNL2 si-NC si-GNL2 (a) (b)

1.5 MHCC97-H 0.8 SMCC-7721

0.6 1.0

0.4 ⁎ OD 450 nm 0.5 ⁎ OD 450 nm 0.2

0.0 0.0 0 24 48 72 0 24 48 72 Time (h) Time (h) si-NC si-NC si-GNL2 si-GNL2 (c) (d)

Figure 7: Evaluation of cell proliferation in si-GNL2-transfected LIHC cells. GNL2 expression was reduced in (a) MHCC97-H and (b) SMCC-7721 by si-GNL2. ∗P <0:05; ∗∗P <0:01. Detection of cell proliferation ability in si-GNL2-transfected (c) MHCC97-H and (d) SMCC-7721 cells by CCK-8 assay. ∗P <0:05. food, genetics, etc. [19]. Currently, the studies about LIHC and maintenance [15]. However, few pieces of research are are few. As for LIHC treatment, Kim et al. summarized and conducted to explore the relation between GNL2 and disease. discussed clinical researches towards systematical chemical Herein, our study plans to explore the role and function of therapy for advanced cancer [20]. Zhang and Zhang found GNL2 in LIHC. FoxP4 functioned as a tumor promoter in LIHC cells by tran- As far as gene expression information from the TCGA scriptionally regulating Slug and highlighted the potential database, GNL2 was highly expressed in LIHC tumor sam- effects of FoxP4 on the prognosis and treatment of LIHC ples compared to that in normal samples, implying GNL2 [21]. The early stage is often asymptomatic, and obvious was a possible oncogene in LIHC. After analysis of the asso- symptoms like liver area pain, fever, and fatigue usually ciations between various pathological carcinoma stages of appear in the advanced stage [22]. It is possible to cure LIHC LIHC patients and GNL2 expression, the data revealed that in its early stage, but the treatment is complicated in the mid- high expression of GNL2 was significantly associated with dle and advanced stages [23, 24]. Therefore, it is necessary to advanced cancer stages. Kaplan-Meier analysis based on have a better understanding of the molecular mechanism GEPIA and Kaplan-Meier plotter datasets consistently involved in liver carcinoma initiation and to screen novel verified that LIHC patients with highly expressed GNL2 biomarkers. In this study, we screened 361 DEGs and exhibited a shorter survival ratio. What was more, in vitro GNL2 was one of the significantly upregulated genes. Besides, knockdown functional experiments demonstrated that GNL2 was observed to have a high degree in the PPI network. reducing GNL2 by siRNA impeded LIHC cell proliferation, Thus, we considered GNL2 as candidate biomarkers for migration, and invasion abilities. Based on the above find- LIHC. ings, GNL2 was probably considered as a treatment target NS or GNL3 (nucleostemin) and GNL3-like (GNL3L) are and a biomarker for LIHC patients’ prognosis. two members of the GNL2 family [25]. The two both com- The alteration of ribosome biogenesis often occurs in car- prise an MMR_HSR1 domain, depicted by five GTP binding cinoma cells due to the rising need for protein synthesis in motifs formed as a cyclic order. The two have a common unrestricted cancer growth [26]. Ribosome biosynthesis is a Grnlp homologous sequence in yeast, highly similar to the complicated biological process required for the coordination sequence in vertebrates. GNL3L is the vertebrate aileron of of multiple factors and a huge cellular energy investment. NS, while GNL2 is single both in vertebrates and inverte- The ribosome is essential for protein production and there- brates. NS is a protein playing essentially in stem cell growth fore is essential for cell survival, growth, and proliferation. 10 BioMed Research International

si-NC si-GNL2 1.5 MHCC97-H

1.0

MHCC97-H 0.5 ⁎⁎

0.0

Relative cell number of invasion si-NC si-GNL2 (a) si-NC si-GNL2 1.5 SMCC-7721

1.0 SMCC-7721 ⁎ 0.5

0.0

Relative cell number of invasion si-NC si-GNL2 (b) si-NC si-GNL2 1.5 MHCC97-H

1.0 MHCC97-H 0.5 ⁎

0.0 si-NC si-GNL2 Relative cell number of migration (c) si-NC si-GNL2 1.5 SMCC-7721

1.0 SMCC-7721 0.5 ⁎⁎

0.0 si-NC si-GNL2 Relative cell number of migration (d)

Figure 8: Cell functional results of GNL2 downregulation in LIHC cells. Validation of cell invasion capability in si-GNL2-transfected (a) MHCC97-H and (b) SMCC-7721 cells by Transwell assay. ∗P <0:05; ∗∗P <0:01. Validation of cell migration capability in si-GNL2- transfected (c) MHCC97-H and (d) SMCC-7721 cells by Transwell assay. ∗P <0:05; ∗∗P <0:01.

Ribosomal biogenesis begins in the nucleolus, including ribo- tiated by overexpressing oncogenes or eliminating neoplasm somal RNA synthesis and processing, ribosomal protein suppressing genes. In our study, we found that GNL2 was assembly, and transport to the cytoplasm [27]. Interference largely associated with altered ribosome biogenesis in cancer in ribosome biogenesis can promote cell cycle arrest, apopto- cells through KEGG and GO enrichment analyses, thereby sis, or senescence and thus is usually associated with carci- playing a vital role in carcinoma initiation and progression. noma, aging, and some other related degenerative diseases This study has some limitations. First, we did not con- [28]. The hyperactivation of ribosome biogenesis usually struct an overexpression vector of GNL2, and we further occurs in tumor cells to cope with a rising need in protein explored the regulation of GNL2 overexpression on the pro- synthesis and maintain unrestricted growth [29]. Impor- liferation, invasion, and migration of LIIHC cells. Secondly, tantly, the hyperactivation of ribosome biogenesis can be ini- we have not used clinical samples to analyze GNL2 mRNA BioMed Research International 11 and protein levels. In future studies, we will collect more clin- [4] S. Parpart, S. Roessler, F. Dong et al., “Modulation of miR-29 ical samples for analysis of GNL2 mRNA and protein levels. expression by alpha-fetoprotein is linked to the hepatocellular carcinoma epigenome,” Hepatology, vol. 60, no. 3, pp. 872– 5. Conclusion 883, 2014. [5] V. Mazzaferro, Y. S. Chun, R. T. P. Poon et al., “Liver trans- ” To our knowledge, this study reported the function of GNL2 plantation for hepatocellular carcinoma, Annals of Surgical – in LIHC for the first time, and the results showed that GNL2 Oncology, vol. 15, no. 4, pp. 1001 1007, 2008. was a new biomarker for predicting the prognosis of LIHC [6] Y. Tang, Y. Zhang, and X. Hu, “Identification of potential hub patients. Bioinformatics analysis showed that GNL2 was genes related to diagnosis and prognosis of hepatitis B virus- greatly raised in LIHC, and its overexpression was closely related hepatocellular carcinoma via integrated bioinformatics analysis,” BioMed Research International, vol. 2020, Article ID related to cancer stage and poor prognosis. Enrichment anal- 4251761, 19 pages, 2020. ysis suggested that GNL2 was largely related to ribosome bio- “ synthesis which was essential for cancer unrestricted growth. [7] E. C. Lo, A. N. Rucker, and M. P. Federle, Hepatocellular car- cinoma and intrahepatic cholangiocarcinoma: imaging for Ablating GNL2 resulted in the reduction of cell proliferation, fi diagnosis, tumor response to treatment and liver response to migration, and invasion abilities. These ndings demon- radiation,” Seminars in Radiation Oncology, vol. 28, no. 4, strated that GNL2 could be a promising treatment target pp. 267–276, 2018. for LIHC. [8] J. Y. Zhang, W. Zhu, H. Imai, K. Kiyosawa, E. K. L. Chan, and E. M. Tan, “De-novo humoral immune responses to cancer- Data Availability associated autoantigens during transition from chronic liver disease to hepatocellular carcinoma,” Clinical and Experimen- The datasets used and/or analyzed during the current study tal Immunology, vol. 125, no. 1, pp. 3–9, 2001. are available from the corresponding author on reasonable [9] A. Nicoletti, F. R. Ponziani, M. Biolato et al., “Intestinal perme- request. ability in the pathogenesis of liver damage: from non-alcoholic fatty liver disease to liver transplantation,” World Journal of – Conflicts of Interest Gastroenterology, vol. 25, no. 33, pp. 4814 4834, 2019. [10] M. R. Alison, L. J. Nicholson, and W. R. Lin, “Chronic inflam- The authors have declared that no conflict of interest exists. mation and hepatocellular carcinoma,” Recent Results in Can- cer Research, vol. 185, pp. 135 –148, 2011. ’ [11] R. Dayoub, H. Wagner, F. Bataille et al., “Liver regeneration Authors Contributions associated protein (ALR) exhibits antimetastatic potential in ” Study concept and design was done by Yiwei Dong, Qianqian hepatocellular carcinoma, Molecular Medicine, vol. 17, no. 3-4, pp. 221–228, 2011. Cai, and Xingzhong Wu. Drafting of the manuscript was done “ by Yiwei Dong, Qianqian Cai, and Lisheng Fu. Acquisition of [12] T. Tu, M. Budzinska, A. Maczurek et al., Novel aspects of the liver microenvironment in hepatocellular carcinoma patho- data, analysis, and interpretation of data were done by Yiwei genesis and development,” International Journal of Molecular Dong, Haojie Liu, and Mingzhe Ma. Statistical analysis was Sciences, vol. 15, no. 6, pp. 9422–9458, 2014. done by Yiwei Dong and Qianqian Cai. Critical revision of [13] G. L. Wong, H. L. Y. Chan, Y. K. Tse et al., “On-treatment the manuscript was done by Yiwei Dong and Xingzhong fi fi alpha-fetoprotein is a speci c tumor marker for hepatocellular Wu. 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