Advanced Search

Publications > Journals > Journal of Clinical and Translational Hepatology > Article Full Text


Expression and Regulatory Network Analysis of Function of Small Nucleolar RNA Host Gene 4 in Hepatocellular Carcinoma

  • Jing Cao1,# ,
  • Cuicui Xiao2,# ,
  • Christ-Jonathan Tsia Hin Fong3 ,
  • Jiao Gong4 ,
  • Danyang Li1,
  • Xiangyong Li1,
  • Yusheng Jie1,*  and
  • Yutian Chong1,* 
 Author information
Journal of Clinical and Translational Hepatology   2022;10(2):297-307

doi: 10.14218/JCTH.2020.00175


Background and Aims

Long non-coding RNA small nucleolar RNA host genes (SNHGs) play a critical role in the occurrence and development of tumors. In this study, we aimed to investigate the role of SNHG4 in hepatocellular carcinoma (HCC) and its underlining mechanism.


Datasets were acquired from The Cancer Genome Atlas (TCGA) database. lncLocator 2.0 was used to identify the distribution of SNHG4 in HCC cells. Gene expression, Kaplan-Meier survival, microRNA and transcription factor target analyses were performed with the University of Alabama Cancer (UALCAN) Database, Kaplan-Meier Plotter, LinkedOmics, WebGestalt and gene set enrichment analysis, respectively. Gene Ontology and pathway enrichment analyses and assessment of RNA binding proteins were performed by R software, circlncRNAnet and Encyclopedia of RNA Interactomes (ENCORI). In addition, CirclncRNAnet and ENCORI were used to find the correlation between SNHG4 and important proteins, while the prognostic value was assessed with the Human Protein Atlas database and Kaplan-Meier Plotter.


Expression of SNHG4 in HCC is higher in HCC tissue than in normal healthy liver tissues and is mainly distributed in the nucleus. SNHG4 positively correlated with poor prognosis (p<0.01 for overall survival and recurrence-free survival). Functional enrichment analysis revealed SNHG4 involvement with regulation of ribosomal RNA synthesis and the RNA processing and surveillance pathway. SNHG4 is closely associated with miR-154 and miR-206, transcription factor target E2F family and the signaling pathway for MAPK/ERK and mTOR. U2 auxiliary factor 2 (U2AF2) showed strong correlation with SNHG4, while low-expression of U2AF2 showed good prognosis.


Based on our findings, we infer SNHG4 may play a role in the formation of HCC via regulation of tumor-related pathways.


Small nucleolar RNA host gene 4, Hepatocellular carcinoma, Prognosis, Bioinformatic analysis


Hepatocellular carcinoma (HCC), which represents about 85–90% of cases of primary liver cancer, is the fourth most common malignancy and the third leading cause of tumor-related deaths in China.1 Worldwide, HCC is the second and sixth leading cause of cancer-related deaths in males and females, respectively.2 Risk factors, including viral infection, alcoholic cirrhosis and fatty liver, along with chronic inflammatory disorders of the biliary tract, genetic diseases and carcinogens, contribute to the risk of HCC. These are responsible for abnormal gene expression, resulting in increased cancer cell proliferation and escape from immune surveillance, which can speed up the process of development into HCC.3 Patients with HCC are usually hospitalized at an advanced stage and are prone to recurrence and metastasis. Therefore, it is of primary concern to explore the potential mechanisms of HCC and discover sensitive biomarkers to screen out high-risk patients.

With the advent of molecular biology, bioinformatics and sequencing technology have become increasingly used in research. The Cancer Genome Atlas (TCGA) database has accumulated abundant genomic and gene expression profiles for different diseases in the past decade. Through analysis of these data, various key genes and signaling pathways related to the disease may be identified, which may aid in better understanding of the disease mechanism and provide a theoretical basis for various clinical applications, including prognostic markers and potential therapeutic targets. Recent studies have shown that non-coding RNA (ncRNA) characterized by microRNA (miR/miRNA) and long non-coding RNA (lncRNA) are important regulatory molecules involved in various physiological and pathological cellular processes.4 Small nucleolar RNAs (snoRNA) are a class of nuclear-enriched intron-derived lncRNAs, between 60–300 nucleotides in length, predominantly found in the nucleolus and playing an important role in the process of chemical RNA modification, pre-RNA processing and alternative splicing control. Recently, snoRNAs and their host genes (small nucleolar RNA host genes, SNHGs) have been reported in a wide spectrum of cancers.57SNHG4, which is localized at the 5q31.2 region and has five exons, can promote tumor growth in patients with osteosarcoma8 and has been associated with a shorter overall survival (OS) in patients with HCC.9 miRNAs are small endogenous RNAs involved in regulating gene-expression post-transcriptionally. SNHG4 was found to promote tumor growth by sponging miR-224-3p in osteosarcoma and miRNA-204-5p in gastric cancer.8,10

Understanding this novel RNA crosstalk can lead to significant insight into gene regulatory networks and have implications in disease pathogenesis.11 However, the functional network and relationship between SNHG4 and HCC has not yet been elucidated. Therefore, the aim of the present study was to investigate the relationship between the expression of SNHG4 and HCC using online databases and to predict SNHG4-related pathways as well as related binding proteins and miRNAs (Fig. 1).

Workflow chart for this research study.
Fig. 1  Workflow chart for this research study.


Expression of SNHG4 in HCC

The TCGA portal is the largest and most commonly used public resource providing somatic mutation, gene expression, gene methylation and copy number variation (CNV) datasets for several thousands of tumor samples.12 Various computational tools have been developed to aid researchers in performing specific TCGA data analyses. UALCAN (http://ualcan.path.uab.edu ) is an interactive website used to perform in-depth analyses of TCGA gene expression data and analyze relative expression of a query gene(s) across tumor and normal samples, as well as in various tumor sub-groups, based on individual cancer stages, tumor grade or other clinicopathological features.13,14 Among 377 samples of liver hepatocellular carcinoma (LIHC) present in the TCGA database, SNHG4 expression was found in only 371 samples. The lncLocator 2.0 is a cell-line-specific predictor, which trains an end-to-end deep model per cell line, for predicting lncRNA subcellular localization from sequences. lncLocator 2.0 was used to identify the distribution of SNHG4 in the HepG2 cell line.15 Experimental validation was then conducted (see Supplementary Materials).

Survival analysis of SNHG4 in HCC

The Kaplan-Meier Plotter (www.kmplot.com ) is an online database which can assess the effects of 54,675 genes on survival using 10,461 cancer samples.16 In this study, it was used to analyze OS and the recurrence-free survival (RFS) of HCC patients; patient samples were split into two groups, according to whether the expression value was above or below the median. The Mantel-Cox test, also known as the log-rank test, was used to determine significance of difference between survival curves. The number-at-risk is indicated below each panel, while the hazard ratio (HR) with 95% confidence interval (CI) and log-rank p-value are shown in each plot.

Functional and signaling pathway analysis

The functional annotation of SNHG4-related genes was performed using Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. GO enrichment analysis is commonly used to annotate the degree of gene function terms in differentially expressed genes (DEGs), which include molecular function (MF), biological process (BP), and cellular component (CC). KEGG enrichment analysis was used to demonstrate enriched signaling pathways in DEGs. Both GO and KEGG pathway analyses were performed using the R package “clusterProfiler”. Enrichment maps which allow visualization of pathway enrichment results were drawn using R software (http:///www.r-project.org/ ). Gene set enrichment analysis (GSEA; http://www.broad.mit.edu/gsea ) was conducted to perform analysis of SNHG4-related canonical pathways in HCC.

Analysis of genes, miRNA and transcription factor targets related to SNHG4 in HCC

Analysis of genes which interact with SNHG4, miRNA and transcription factor targets of SNHG4 in HCC was carried out using LinkedOmics. The LinkedOmics database (http://www.linkedomics.org/login.php ) harbors multiomics data and clinical data for 32 cancer types and a total of 11,158 patients from TCGA.17 LinkedOmics consists of three analysis modules: LinkFinder, LinkCompare, and LinkInterpreter. The LinkFinder module in LinkedOmics was used to study genes differentially expressed in correlation with SNHG4. Results were graphically presented as volcano plots, heat maps or scatter plots, and were statistically analyzed using Pearson’s correlation coefficient. A p-value of <0.05 was considered significant. Subsequently, GSEA was performed using the LinkInterpreter module in LinkedOmics. The results were further analyzed by KEGG and Wikipathway, conducted in WebGestalt for analysis of genes interacting with SNHG4, miRNA targets and transcription factor-target enrichment. The rank criterion was a false discovery rate (FDR) <0.05, and 500 simulations were performed. In order to validate the transcription factors obtained by LinkedOmics, we used Transcription factor Affinity Prediction (TRAP) Web Tools (http://trap.molgen.mpg.de/cgi-bin/home.cgi ). TRAP Web Tools was developed at the Max Planck Institute for Molecular Genetics to predict transcription factor (TF) binding affinities to DNA.18

Oncomine is an online platform that provides data from cancer microarray datasets, which can also be used for data mining in multiple cancers, including HCC.19 Wurmbach Liver dataset was selected from the Oncomine to evaluate the expression characteristics of RNF44 and HEATR1 in HCC, compared to that in normal liver tissues. A p-value <0.05 was considered as statistically significant.

Prediction and analysis of RNA-binding proteins (RBPs) related to SNHG4

CirclncRNAnet ( ) is an integrated web-based resource for mapping of functional networks of ncRNAs.20 The Encyclopedia of RNA Interactomes (ENCORI) is an open-source web tool used for studying the ncRNA interactions of CLIP-seq, degradome-seq, and RNA-RNA interactome and RBPs data.21 The RBPs relevant to SNHG4 were downloaded from the CirclncRNAnet and ENCORI. Subsequently, RBPs coexistent in both databases and which were expressed in HepG2 cells (Human hepatoma cell lines), were screened.

The Human Protein Atlas (HPA) (https://www.proteinatlas.org/about ) is a Swedish-based program initiated in 2003, aiming to map all the human proteins in cells, tissues and organs, by integrating various omics technologies. The HPA consists of six separate parts: the Tissue Atlas, the Single Cell Type Atlas, the Pathology Atlas, the Brain Atlas, the Blood Atlas and the Cell Atlas, each focusing on a particular aspect of the genome-wide analysis of the human proteins. The Cell Type Atlas shows single cell RNA sequencing (scRNAseq) data from 13 different human tissues, together with immunohistochemically-stained tissue sections, allowing for visualization of the corresponding spatial protein expression patterns.


SNHG4 expression in HCC

RNA sequencing (RNA-seq) datasets were obtained from TCGA. SNHG4 was found to be upregulated in patients with HCC compared to healthy subjects (Fig. 2A). The HCC group was further stratified based on sex (Fig. 2B), age (Fig. 2C), individual cancer stages (Fig. 2D), tumor grade (Fig. 2E) and promoter methylation levels (Fig. 2F) by UALCAN. Moreover, the OS and RFS of patients with HCC expressing high SNHG4 levels and low SNHG4 levels were significantly different (Fig. 3A and B). Based on results of lncLocator 2.0, SNHG4 was found to be mainly distributed in the nucleus of HCC cells. We then performed in vitro analysis of lncRNA SNHG4 expression in cytoplasm and nucleus. RNA was extracted from the cytoplasm and nucleus of two different kinds of hepatoma cell lines (HepG2 and HuH-7) using the PARIS™ Kit, and subjected to reverse transcription for analysis of lncRNA SNHG4 expression using real-time fluorescent quantitative PCR. These results showed that lncRNA SNHG4 was mainly located in the nucleus (For details, see Supplementary Fig. 1).

<italic>SNHG4</italic> expression in subgroups of patients with HCC, stratified based on sex, age and other criteria (University of Alabama Cancer Database).
Fig. 2  SNHG4 expression in subgroups of patients with HCC, stratified based on sex, age and other criteria (University of Alabama Cancer Database).

Relative expression of SNHG4 is shown in boxplots. (A) In normal and LIHC samples. (B) In normal individuals of both sexes compared to male and female patients with LIHC. (C) In normal individuals of any age or in patients with LIHC aged 21–40, 41–60, 61–80 or 81–100 years. (D) In normal individuals or in patients with LIHC in stages I, II, III or IV. (E) In normal individuals or patients with LIHC with grade 1, 2, 3 or 4 tumors. (F) Promoter methylation in normal individuals or patients with LIHC. Data are expressed as mean±standard error. *p<0.05, **p<0.01, ***p<0.001. SNHG4, small nucleolar RNA host gene 4; HCC, hepatocellular carcinoma; LIHC, liver hepatocellular carcinoma.

Mantel-Cox survival curves.
Fig. 3  Mantel-Cox survival curves.

(A) Disease-specific survival (HCC-related death). (B) HCC recurrence-free survival. SNHG4, small nucleolar RNA host gene 4; HCC, hepatocellular carcinoma; HR, hazard ratio; OS, overall survival; RFS, recurrence-free survival.

Functional and signaling pathway analysis

GO annotation showed that SNHG4-related biological processes included RNA splicing, RNA processing and ribonucleoprotein complex biogenesis; cellular component included the spliceosomal complex and small subunit processome, while molecular functions involved ATPase, helicase and catalytic activity (Fig. 4A, B and C). In addition, KEGG pathway annotation revealed that RNA transport, ribosome biogenesis, cell cycle and RNA surveillance pathway were significantly enriched pathways (Fig. 4D). To identify the potential function of SNHG4, GSEA was conducted to search for biological processes enriched in samples with high levels of SNHG4. A total of 96 gene sets were found to be upregulated. Three gene sets, namely mitogen-activated protein kinase (MAPK)/ERK, hepatocyte growth factor (HGF)/MET and mTOR, were enriched in samples with high expression levels of SNHG4 (Fig. 5A, B and C).

GO and KEGG pathway of genes relevant to <italic>SNHG4</italic> in HCC.
Fig. 4  GO and KEGG pathway of genes relevant to SNHG4 in HCC.

(A) Biological process. (B) Cellular component. (C) Molecular function. (D) KEGG pathway enrichment of SNHG4. SNHG4, small nucleolar RNA host gene 4; HCC, hepatocellular carcinoma; KEGG, Kyoto Encyclopedia of Genes and Genomes.

GSEA for gene sets related with <italic>SNHG4</italic> expression.
Fig. 5  GSEA for gene sets related with SNHG4 expression.

SNHG4, small nucleolar RNA host gene 4; GSEA, Gene set enrichment analysis; MAPK, mitogen-activated protein kinase; ERK, extracellular-signal-regulated kinase; MTOR, mammalian target of rapamycin.

Analysis of genes, miRNA and transcription factor targets related to SNHG4 in HCC

LinkedOmics was used to analyze RNA sequencing data of 371 HCC patients from TCGA LIHC. A total of 8,744 genes showed significant correlation with SNHG4 (Fig. 6A), with a false discovery rate of <0.01 and p-value < 0.05. We found 49 positive and 50 negative gene correlations with SNHG4, as shown in the heatmap in Figure 6B and C, respectively. Among these positively correlated genes, RING finger protein 44 (RNF44; Pearson’s correlation coefficient: 0.66, p=3.76×10−47) and HEAT repeat-containing protein 1 (HEATR1; Pearson’s correlation coefficient: 0.64, p=2.3×10−44) significantly correlated with SNHG4. KEGG and Wikipathway cancer enrichment analyses of genes interacting with SNHG4, which had been identified with LinkedOmics LinkFinder, were conducted in WebGestalt. Ribosome biogenesis in eukaryotes and the RNA processing and surveillance pathway were among the top terms (Fig. 6D and E). Oncomine analysis of the data from the Wurmbach Liver dataset validated the significant difference in expression of RNF44 and HEATR1 in HCC (Fig. 6F and G), and normal healthy liver tissue was significantly different; the levels of expression were based on the gene rank percentile, fold change and p-value. The target transcription factors and miRNAs are shown in Figure 7A and B, respectively. SNHG4-related miRNAs were (ATAACCT) mir-154 [enrichment score (ES): 0.684; normalized enrichment score (NES): 1.834], (ACATTCC) mir-1, mir-206 (ES: 0.595; NES: 1.832) and (ATAGGAA) mir-202 (ES: 0.659; NES: 1.831), while SNHG4-related transcription factors included the E2F transcription factor (E2F) family, consisting of E2F1DP1_01 (ES: 0.622; NES: 1.896), E2F1DP2_01 (ES: 0.622; NES: 1.896) and E2F4DP2_01 (ES: 0.622; NES: 1.896). Moreover, by using TRAP web tools to validate the above findings, we obtained transcription factors related to SNHG4, which were found to be related to the E2F family. (See Supplementary Table 1; in the supplementary table, we ranked them from highest to lowest, according to corrected p-value.)

Genes related to <italic>SNHG4</italic> in HCC (LinkedOmics).
Fig. 6  Genes related to SNHG4 in HCC (LinkedOmics).

(A) Volcano map showing the relationship between SNHG4 and the genes expressed in LIHC. Red indicates positive correlation; green indicates negative correlation. (B) Heat map showing genes positively correlated with SNHG4 in LIHC. (C) Heat map showing genes negatively correlated with SNHG4 in LIHC. (D, E) KEGG (D) and Wikipathway (E) analyses results of SNHG4-related genes. (F, G) Oncomine analysis showed high expression of RNF44 (F) and HEATR1 (G) in HCC. SNHG4, small nucleolar RNA host gene 4; HCC, hepatocellular carcinoma; LIHC, liver hepatocellular carcinoma; KEGG, Kyoto Encyclopedia of Genes and Genomes; RNF44, RING finger protein 44; HEATR1, HEAT repeat-containing protein 1.

MiRNA and transcription factor targets related to <italic>SNHG4</italic> in HCC (LinkedOmics).
Fig. 7  MiRNA and transcription factor targets related to SNHG4 in HCC (LinkedOmics).

(A, B) SNHG4-related transcription factor targets and miRNAs, respectively. SNHG4, small nucleolar RNA host gene 4; HCC, hepatocellular carcinoma.

RBPs related to SNHG4

A total of 65 SNHG4 binding proteins were downloaded from CirclncRNAnet and 111 SNHG4 binding proteins were downloaded from ENCORI. U2AF2 and BUD13 were coexistent in both databases and found to be expressed in HepG2 cells. Moreover, both U2AF2 and BUD13 had prognostic significance as shown in Kaplan–Meier plotter (Fig. 8A and C) and the HPA (Fig. 8B and D). Low expression of U2AF2 and BUD13 was found to be related to a better prognosis, while U2AF2 showed better correlation with SNHG4 compared to BUD13 (Fig. 8E and F).

Prognostic value of <italic>U2AF2</italic> and <italic>BUD13</italic> in LIHC patients.
Fig. 8  Prognostic value of U2AF2 and BUD13 in LIHC patients.

(A–D) Low expression of U2AF2 and BUD13 was associated with better prognosis. (E, F) Correlation of BUD13 (E) and U2AF2 (F) with SNHG4. SNHG4, small nucleolar RNA host gene 4; LIHC, liver hepatocellular carcinoma; U2AF2, U2 auxiliary factor 2.


HCC is considered to be one of the main causes of cancer mortality worldwide with high morbidity and mortality rates. HCC accounts for about 80–90% of liver cancer cases and globally results in an annual death toll of 600,000.22 Therefore, identifying novel prognostic biomarkers is essential to improving current HCC treatment. LncRNAs, which have recently attracted much attention, are dysregulated in a wide spectrum of cancers and have potential roles in tumorigenesis and tumor progression. Increasing evidence has demonstrated that snoRNA, a type of lncRNA, plays an important role in tumorigenesis.23 Moreover, SNHG4 which is an important type of snoRNA, has been reported to promote tumor growth and is a poor prognostic factor in patients with osteosarcoma.8

The significance of the role of SNHGs has been documented in the development of various cancers. For instance, SNHG6 has been reported to promote both cell proliferation in gastric cancer and tumor progression in HCC.24 Additionally, knockdown of SNHG1 inhibited cell growth and metastasis of osteosarcoma in vitro and in vivo,6 while several genes including SNHG1, SNHG3, SNHG4, SNHG5, SNHG6, SNHG7, SNHG10, SNHG11 and SNHG12, were found to be upregulated in HCC. Increased SNHG4 expression was also found to be associated with shorter OS (HR: 1.319, 95% CI: 1.131–1.537, p<0.001).9

The significant correlation between SNHG4 expression in liver cancer and histological type, histologic grade, stage, T classification and survival status, was reported by Jiao et al.;25 however, only GSEA analysis was performed during that study. The present study performed a comprehensive bioinformatics analysis of SNHG4 using TCGA datasets and found that SNHG4 was significantly upregulated in patients with HCC and showed poor prognosis in terms of OS and RFS. Based on functional and pathway enrichment analyses, SNHG4 was identified to be mainly involved in cancer-related signaling pathways, potentially facilitating tumorigenesis and progression in HCC. KEGG pathway analysis results showed SNHG4 may be involved in RNA transport, ribosome biogenesis in cell cycle, mRNA surveillance pathway and aminoacyl-transfer RNA biosynthesis. Among the genes interacting with SNHG4, RNF44 and HEATR1 showed the most significant correlation. Interestingly, both have been previously reported to be involved in DNA repair, regulation of rRNA synthesis, RNA stability, and initiation of translation.26,27 Using WebGestalt, KEGG and Wikipathway cancer enrichment analyses of SNHG4-related genes were performed; significantly enriched processes included ribosome biogenesis in eukaryotes and the RNA processing and surveillance pathway, results similar to the KEGG pathway analysis of SNHG4 with R software.

MiRNAs are short ncRNA sequences containing ∼22 nucleotides that act as important regulators of gene expression by specifically binding and cleaving mRNAs or inhibiting their translation.28SNHG4 was reported to promote tumor growth in patients with osteosarcoma, where miRNA-154 is downregulated in liver tissue and can inhibit tumorigenesis and the G1/S transition in cancer cells when overexpressed.29 miRNA-206, which is downregulated in human HCC tissues, and abnormally increased miR-206 expression (in HCC cell lines) have been reported to attenuate cell viability, migration, invasion and increased apoptosis.30 E2F1 plays a crucial role in the control of cell cycle and action. E2F1 acts as a transcription activator for E2F target genes, and can regulate transcriptional activities by binding to the promoters of a target gene via their response elements during the cell cycle progression of the late G1/S phase.31 Studies have shown that SNHG3 could be transcriptionally activated by E2F1. Choi et al.32 reported that E2F1 acts as an inhibitor on the hepatitis B virus life cycle, which mediated HCC by overcoming the control of virus-encoded HBx function on the host p53 promoter, while E2F1 activated the p53 promoter via the E2F1 binding site. Moreover, Palaiologou et al.33 reported E2F1 overexpression and its pro-apoptotic nature in human HCC.

It has long been established that alternative splicing contributes to tumorigenesis by producing splice isoforms which can stimulate cell proliferation and cell migration or induce resistance to apoptosis and anticancer agents.34 Among RBPs interacting with SNHG4, low expression of BUD13 and U2AF2 showed good prognosis in HCC patients, while U2AF2 showed good correlation with SNHG4 (r=0.6745, p<0.001). U2AF, comprised of a large and a small subunit, is a non small nuclear ribonucleoprotein particles (snRNP) protein required for the binding of U2 snRNP to the pre-mRNA branch site. Pre-mRNA splicing is a crucial step in eukaryotic gene expression. The recognition of the splice sites is initiated by U2AF2, which binds to the poly-pyrimidine tract (referred to also as Py-tract) RNA upstream of exons to assemble the spliceosome. Li et al. found that U2AF2 was related to activation of the AKT/mTOR pathway in non-small cell lung cancer.35 Similarly, from our GSEA analysis results, we can infer that the SNHG4 related-biological pathways were MAPK/ERK and mTOR, the former presenting with a significantly higher number of gene hits before the peak (see Fig. 5A) and hence contributing more to the enrichment score of the gene set. This suggests that SNHG4 might promote tumor progression by regulating the MAPK/ERK pathway. Both MAPK/ERK and mTOR have been reported as majorly important cellular signaling pathways during the genesis and development of HCC.36 Taking into account that numerous studies have found that SNHG4 plays a crucial role in carcinogenesis,8,9 it can be inferred that SNHG4 contributes to the progression of HCC and influences prognosis by regulating the MAPK/ERK or mTOR pathway.

SNHG4 has the potential to be the basis of future drug treatment or gene therapy in HCC. The present findings suggest that SNHG4 may play a critical role in hepatocarcinogenesis by influencing different steps of gene expression, from RNA splicing and ribosome biogenesis to the mRNA processing and surveillance pathway. A limitation of our study is the lack of validation at experimental level, which we shall perform in the future. Secondly, HCC has many different causes, which may lead to different pathogeneses. We did not explore the role of SNHG4 in HCC of different etiology. However, based on patient medical history and risk factors of HCC, patients in our study could not be simply subclassified into distinct groups, such as virus-induced HCC and alcohol-related HCC, since for most patients HCC was found to be of mixed etiology, as is often the case at clinical level. To our knowledge, this is a limitation of any study on HCC involving data from the TCGA database. In addition, the findings of the present study are preliminary and purely descriptive.

Supporting information

Supplementary File 1

Supplementary materials.


Supplementary Fig. 1

Relative expression of lncRNA SNHG4 in the nucleus and cytoplasm of hepatoma cells HuH-7 and HepG2.

*U3 and β-actin were used as the internal references for the nucleus and cytoplasm, respectively. The results show that lncRNA SNHG4 was mainly located in the nucleus.


Supplementary Table 1

Transcription factors of SNHG4 predicted by the TRAP web tool.




biological process


cellular component


confidence interval


copy number variation


differentially expressed genes


Encyclopedia of RNA Interactomes


extracellular-signal-regulated kinase


enrichment score


false discovery rate


Gene Ontology


gene set enrichment analysis


hepatocellular carcinoma


HEAT repeat-containing protein 1


hepatocyte growth factor


Human Protein Atlas


hazard ratio


Kyoto Encyclopedia of Genes and Genomes


liver hepatocellular carcinoma


long non-coding RNA


mitogen-activated protein kinase


molecular function




mammalian target of rapamycin


non-coding RNA


overall survival


normalized enrichment score


RNA-binding protein


recurrence-free survival


RNA sequencing


small nucleolar RNA host genes


small nucleolar RNAs


small nuclear ribonucleoprotein particles


RING finger protein 44


The Cancer Genome Atlas


Transcription factor Affinity Prediction


U2 auxiliary factor 2


University of Alabama Cancer


Data sharing statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.


This work was supported by grants from the National key research and development program (2018YFC1315400) and the National Natural Science Foundation of China (Nos. 81773176, 81870449).

Conflict of interest

The authors have no conflict of interests related to this publication.

Authors’ contributions

Designed the study and take responsibility for the integrity of the data and the accuracy of the data analysis (JG, YJ, YC), contributed to collection and analysis of the data (JC, CX, CJTHF, DL, XL), and wrote the manuscript (JC, CX, CJTHF). All authors contributed to data interpretation, and reviewed and approved the final version.


  1. Zhou J, Sun HC, Wang Z, Cong WM, Wang JH, Zeng MS, et al. Guidelines for diagnosis and treatment of primary liver cancer in China (2017 Edition). Liver Cancer 2018;7(3):235-260 View Article
  2. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68(6):394-424 View Article
  3. Roderburg C, Luedde T. The role of the gut microbiome in the development and progression of liver cirrhosis and hepatocellular carcinoma. Gut Microbes 2014;5(4):441-445 View Article
  4. Siprashvili Z, Webster DE, Johnston D, Shenoy RM, Ungewickell AJ, Bhaduri A, et al. The noncoding RNAs SNORD50A and SNORD50B bind K-Ras and are recurrently deleted in human cancer. Nat Genet 2016;48(1):53-58 View Article
  5. Li Z, Qiu R, Qiu X, Tian T. SNHG6 promotes tumor growth via repression of P21 in colorectal cancer. Cell Physiol Biochem 2018;49(2):463-478 View Article
  6. Wang J, Cao L, Wu J, Wang Q. Long non-coding RNA SNHG1 regulates NOB1 expression by sponging miR-326 and promotes tumorigenesis in osteosarcoma. Int J Oncol 2018;52(1):77-88 View Article
  7. You J, Fang N, Gu J, Zhang Y, Li X, Zu L, et al. Noncoding RNA small nucleolar RNA host gene 1 promote cell proliferation in nonsmall cell lung cancer. Indian J Cancer 2014;51(Suppl 3):e99-e102 View Article
  8. Xu R, Feng F, Yu X, Liu Z, Lao L. LncRNA SNHG4 promotes tumour growth by sponging miR-224-3p and predicts poor survival and recurrence in human osteosarcoma. Cell Prolif 2018;51(6):e12515 View Article
  9. Zhu Q, Yang H, Cheng P, Han Q. Bioinformatic analysis of the prognostic value of the lncRNAs encoding snoRNAs in hepatocellular carcinoma. Biofactors 2019;45(2):244-252 View Article
  10. Cheng XB, Zhang T, Zhu HJ, Ma N, Sun XD, Wang SH, et al. Knockdown of lncRNA SNHG4 suppresses gastric cancer cell proliferation and metastasis by targeting miR-204-5p. Neoplasma 2021;68(3):546-556 View Article
  11. Tay Y, Rinn J, Pandolfi PP. The multilayered complexity of ceRNA crosstalk and competition. Nature 2014;505(7483):344-352 View Article
  12. Deng M, Bragelmann J, Schultze JL, Perner S. Web-TCGA: an online platform for integrated analysis of molecular cancer data sets. BMC Bioinformatics 2016;17:72 View Article
  13. Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi B, et al. UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia 2017;19(8):649-658 View Article
  14. Lin Y, Liang R, Qiu Y, Lv Y, Zhang J, Qin G, et al. Expression and gene regulation network of RBM8A in hepatocellular carcinoma based on data mining. Aging (Albany NY) 2019;11(2):423-447 View Article
  15. Lin Y, Pan X, Shen HB. lncLocator 2.0: a cell-line-specific subcellular localization predictor for long non-coding RNAs with interpretable deep learning. Bioinformatics 2021:btab127 View Article
  16. Mongre RK, Jung S, Mishra CB, Lee BS, Kumari S, Lee MS. Prognostic and clinicopathological significance of SERTAD1 in various types of cancer risk: a systematic review and retrospective analysis. Cancers (Basel) 2019;11(3):337 View Article
  17. Vasaikar SV, Straub P, Wang J, Zhang B. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res 2018;46(D1):D956-D963 View Article
  18. Thomas-Chollier M, Hufton A, Heinig M, O’Keeffe S, Masri NE, Roider HG, et al. Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs. Nat Protoc 2011;6(12):1860-1869 View Article
  19. Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, et al. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 2004;6(1):1-6 View Article
  20. Wu SM, Liu H, Huang PJ, Chang IY, Lee CC, Yang CY, et al. circlncRNAnet: an integrated web-based resource for mapping functional networks of long or circular forms of noncoding RNAs. Gigascience 2018;7(1):1-10 View Article
  21. Fan K, Zebisch A, Horny K, Schrama D, Becker JC. Highly expressed miR-375 is not an intracellular oncogene in Merkel cell polyomavirus-associated Merkel cell carcinoma. Cancers (Basel) 2020;12(3):529 View Article
  22. Roberts LR. Sorafenib in liver cancer—just the beginning. N Engl J Med 2008;359(4):420-422 View Article
  23. Gong J, Li Y, Liu CJ, Xiang Y, Li C, Ye Y, et al. A pan-cancer analysis of the expression and clinical relevance of small nucleolar RNAs in human cancer. Cell Rep 2017;21(7):1968-1981 View Article
  24. Chang L, Yuan Y, Li C, Guo T, Qi H, Xiao Y, et al. Upregulation of SNHG6 regulates ZEB1 expression by competitively binding miR-101-3p and interacting with UPF1 in hepatocellular carcinoma. Cancer Lett 2016;383(2):183-194 View Article
  25. Jiao Y, Li Y, Jia B, Chen Q, Pan G, Hua F, et al. The prognostic value of lncRNA SNHG4 and its potential mechanism in liver cancer. Biosci Rep 2020;40(1):BSR20190729 View Article
  26. He S, Ma X, Ye Y, Zhang M, Zhuang J, Song Y, et al. HEATR1 modulates cell survival in non-small cell lung cancer via activation of the p53/PUMA signaling pathway. Onco Targets Ther 2019;12:4001-4011 View Article
  27. Li YY, Wu C, Shah SS, Chen SM, Wangpaichitr M, Kuo MT, et al. Degradation of AMPK-alpha1 sensitizes BRAF inhibitor-resistant melanoma cells to arginine deprivation. Mol Oncol 2017;11(12):1806-1825 View Article
  28. Wang W, Peng B, Wang D, Ma X, Jiang D, Zhao J, et al. Human tumor microRNA signatures derived from large-scale oligonucleotide microarray datasets. Int J Cancer 2011;129(7):1624-1634 View Article
  29. Zhou H, Zhang M, Yuan H, Zheng W, Meng C, Zhao D. MicroRNA-154 functions as a tumor suppressor in osteosarcoma by targeting Wnt5a. Oncol Rep 2016;35(3):1851-1858 View Article
  30. Wang Y, Tai Q, Zhang J, Kang J, Gao F, Zhong F, et al. MiRNA-206 inhibits hepatocellular carcinoma cell proliferation and migration but promotes apoptosis by modulating cMET expression. Acta Biochim Biophys Sin (Shanghai) 2019;51(3):243-253 View Article
  31. Cam H, Dynlacht BD. Emerging roles for E2F: beyond the G1/S transition and DNA replication. Cancer Cell 2003;3(4):311-316 View Article
  32. Choi M, Lee H, Rho HM. E2F1 activates the human p53 promoter and overcomes the repressive effect of hepatitis B viral X protein (Hbx) on the p53 promoter. IUBMB Life 2002;53(6):309-317 View Article
  33. Palaiologou M, Koskinas J, Karanikolas M, Fatourou E, Tiniakos DG. E2F-1 is overexpressed and pro-apoptotic in human hepatocellular carcinoma. Virchows Arch 2012;460(5):439-446 View Article
  34. Shkreta L, Bell B, Revil T, Venables JP, Prinos P, Elela SA, et al. Cancer-associated perturbations in alternative pre-messenger RNA splicing. Cancer Treat Res 2013;158:41-94 View Article
  35. Li J, Cheng D, Zhu M, Yu H, Pan Z, Liu L, et al. OTUB2 stabilizes U2AF2 to promote the Warburg effect and tumorigenesis via the AKT/mTOR signaling pathway in non-small cell lung cancer. Theranostics 2019;9(1):179-195 View Article
  36. Whittaker S, Marais R, Zhu AX. The role of signaling pathways in the development and treatment of hepatocellular carcinoma. Oncogene 2010;29(36):4989-5005 View Article
  • Journal of Clinical and Translational Hepatology
  • pISSN 2225-0719
  • eISSN 2310-8819
Back to Top

Expression and Regulatory Network Analysis of Function of Small Nucleolar RNA Host Gene 4 in Hepatocellular Carcinoma

Jing Cao, Cuicui Xiao, Christ-Jonathan Tsia Hin Fong, Jiao Gong, Danyang Li, Xiangyong Li, Yusheng Jie, Yutian Chong
  • Reset Zoom
  • Download TIFF