Background
Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide and is commonly seen in patients with chronic liver inflammation associated with viral infection or metabolic syndrome [
1]. It accounts for about 90% of non-metastatic liver tumors, which may result in systemic manifestations [
2,
3]. Over the past decades, patients have benefited from advancements in new treatment strategies. However, the overall survival rate remains low due to delayed diagnosis of the diseases.
In recent years, the use of statistical models and bioinformatics in identifying novel biomarkers has been increasing [
4]. Therefore, such research provided great potential for clinical application. Various studies showed that lncRNAs play a crucial role in various diseases including cancer [
5]. For instance, Ma et al. found that EGFR1-mediated lnc01503 can promote gastric cancer progression [
6]. While Fan et al. reported that MKL1-induced lncRNA SNHG18 drives the growth and metastasis of non-small cell lung cancer [
7].
Enhancer of zeste homolog 2 (EZH2) is a member of the polycomb group genes (PcGs) family, which is an important epigenetic regulator [
8]. It has been identified as a common oncogene in various types of tumors and is involved in many biological processes including cell proliferation [
9], invasion [
10,
11], metabolism [
12,
13], and apoptosis [
14]. Besides, EZH2 has also been identified as an oncogene in HCC. Chen et al. verified that EZH2 can promote the development of HCC through modulating the miR-22/galectin-9 axis. Therefore, we speculate that the EZH2-related gene can affect the progression of HCC.
In the present study, the differentially expressed lncRNAs/miRNAs/mRNAs are screened based on the EHZ2 expression. Then, the prognostic genes were selected according to the overall survival curve. Multiple databases were used to analyze the possible interaction between lncRNA, miRNA, and mRNA. The corresponding statistical models (cox regression analysis and nomogram) were established to evaluate the survival prediction function of key genes. Methylation and immune infiltration analyses were used to determine the potential function of VIPR1. Finally, PCR and a series of cellular function experiments were performed to confirm the comprehensive analysis. The prediction of hub gene-related drugs can help identify potential treatment targets for HCC.
Materials and methods
Differential expression analysis
The RNA-seq were divided into high and low groups according to the EZH2 expression levels. The difference analysis was performed using the “DESeq2” [
15]. For better and effective screening of interaction networks, lncRNAs with |logFC|> 2, adjusted p-value < 0.05, miRNAs with |logFC|> 0.5, adjusted p-value < 0.05, and mRNAs with |logFC|> 1, adjusted p-value < 0.05 were considered as differentially expressed genes. All the expression data were log2 transformed.
Correlation analysis
Since the expression data of RNA-seq and miRNA-seq did not conform to a normal distribution, we utilized the Spearman correlation to analyze the association between lncRNAs, miRNAs, and mRNAs. The results of the Spearman correlation were visualized using the “ggplot2” R package.
Functional enrichment analysis
To assess the potential mechanisms of the lncRNA-miRNA-mRNA axis, the KEGG and GO enrichment analyses were performed by using the “clusterProfiler” R package [
16]. Biological process terms, cellular component terms, molecular function terms, and pathways with adjusted p-value < 0.05 were considered as potential mechanisms for the onset of HCC.
Immune infiltration analysis
The markers of 24 immune cells were derived from an immunity article, and the classification and description of specific cells can be found in this article [
17]. The immune infiltration analysis was performed through the ssGSEA algorithm in the “GSVA” R package [
18]. The Pearson correlation analysis was used to evaluate the correlation between immune cells and differentially expressed genes.
Prognostic analysis
According to the clinical data of HCC, the effects of hub genes on patients’ prognoses were assessed through the Kaplan–Meier curve. The univariate Cox regression analysis was performed to screen for factors to construct the nomogram model. Nomograms were used to evaluate the role of hub genes in HCC through the “rms” and “survival” R packages. P-value < 0.05 was considered significantly.
Cell lines and cell culture
Human liver cells L0-2, HCC cells (PLC/PRF/5, HepG2, Hep3B) (Cell Bank of the Chinese Academy of Sciences, Shanghai, China), MHCC97H and HCCLM3 (Liver Cancer Institute, Fudan University, Shanghai, China), and Huh7 (Japanese Cancer Research Resources Bank) were cultured in Dulbecco's modified Eagle medium (Gibco) with 10% fetal bovine serum (Gibco) and 1% penicillin–streptomycin (Invitrogen). Cell cultures were done in a thermostatic incubator at 37 °C with a humidified atmosphere of 95% air and 5% CO2.
Transfection experiments
The lncRNA-AC079061.1 small interfering RNA (siRNA) and negative control were purchased from RiboBio Company (Guangzhou, China). The transfection of the lncRNA-AC079061.1 siRNA and negative control was performed with Lipofectamine™ 3000 (#L3000008, ThermoFisher, China) according to the manufacturer’s instructions.
RNA extraction and quantitative reverse-transcription polymerase chain reaction (qRT-PCR)
The total RNA including miRNA was extracted from cells or tissues using the TRIzol Reagent (Invitrogen), while cDNA was synthesized from RNA using the Reverse Transcription Kit (Takara). Subsequently, cDNA was amplified using the Maxinma SYBR Green qPCR Master Mix (Thermo Scientific). The quantification of target genes was done with the 2−ΔΔCt method using glyceraldehyde-3-phosphate dehydrogenase (GAPDH) for normalization. The primers for hsa-miR-765 and U6 small nuclear RNA were obtained from RiboBio Company (Guangzhou, China). The sequences were covered by a patent. Analyses of miRNA expression were normalized to the expression of internal control U6 using the 2 − ΔΔCT method. Melting curve analysis was carried out to assess the specificity of PCR products. The primers have been used for real time PCR, lncRNA-AC079061.1 Sequence 5′-3′: Forward, CCCTCAGGCATCCACTCTACCC; Reverse, TCCAACGCCACCCACTTCAAAC; VIPR1 Sequence 5′-3′: Forward, ACAAGGCAGCGAGTTTGGATGAG; Reverse, GTGCAGTGGAGCTTCCTGAACAG.
Luciferase Reporter Assay
MHCC97H cells and HCCLM3 cells were seeded into 96-well plates and co-transfected with a mixture of 60 ng luciferase, 6 ng pRL-CMV Renilla luciferase reporter, and miR-765 mimic or the negative control using the Lipofectamine 2000 transfection reagent (RiboBio, Guangzhou, China), according to the manufacturer’s instructions.). After 48 h of incubation, the firefly and Renilla luciferase activities were measured using a dual-luciferase reporter assay (Promega, Madison, WI, USA).
Western blot
As described, the protein was extracted from cells or tissues using RIPA cell lysis with Protease Inhibitor Cocktail. Mitochondrial protein extraction was performed with the use of a mitochondrial isolation kit (Beyotime Biotechnology), in accordance with the manufacturer’s directions. The proteins were quantified by using the BCA kit, subjected to 12% SDS-PAGE for separation, then transferred to 0.45 μM PVDF membranes (Millipore, USA). The membranes were blocked with skimmed milk and incubated with primary antibodies at 4 ℃ overnight, followed by incubation with the corresponding HRP-conjugated secondary antibody (PeproTech). The bands were visualized by enhanced chemiluminescence. The intensity of protein expression was measured using ImageJ software. The antibodies used for western blot were listed as follows: VIPR1, Bioss, # bs-2982R, 1:1000; GAPDH, Cell Signaling Technology, #5174, 1:1000.
The cell proliferation assays were performed using a CCK-8 Kit (Yeasen, Shanghai, China) and colony formation. Three thousand cells were seeded into each well in a 96-well plate. The CCK-8 solution (10 µl) was added to 100 µl of culture media, and the optical density was measured at 450 nm. For colony formation assay, one thousand cells were seeded into each well in a 6-well plate for one week, then washed twice with PBS, fixed with 4% paraformaldehyde, stained with crystal violet, and the numbers of foci were counted for each well. Three independent experiments were performed.
Transwell migration and invasion assays
For migration assay, 5 × 104 cells were suspended in 200 µl of DMEM without serum and placed in the cell culture insert (8 µm pore size; BD Falcon, San Jose, CA) of a companion plate (BD Falcon) with a prewarmed culture medium containing 10% fetal bovine serum. For invasion assay, 1 × 105 cells suspended in serum-free medium were seeded into the upper chamber coated with 1 µg/µl Matrigel (BD Biosciences, USA) of a 24-well transwell plate (8-μm pore size, Corning, NY, USA), then 600 μl DMEM with 10% FBS was added into the lower chamber. After incubation for an indicated period of time at 37 °C in 5% CO2, the migrating and invading cells on the outside of the upper chamber membrane were then fixed with 4% paraformaldehyde, stained with crystal violet, and counted under a light microscope (100× magnification) in eight randomly selected areas. Three independent experiments were performed.
Statistical analysis
The expression of hub genes was assessed using the Wilcoxon Signed rank test and Mann–Whitney U test. Experimental data were expressed as mean ± standard deviations from three independent experiments and were analyzed using SPSS software (21.0; SPSS, Inc, Chicago, IL). Continuous variables between two groups or among three groups were compared using the unpaired Student’s t-test or one-way analysis of variance (Bonferroni post hoc test) as appropriate. Categorical variables were compared using the Chi-square test or Fisher’s exact test as appropriate. All statistical tests were two-sided and a P-value < 0.05 was considered statistically significant.
Discussion
Cancer-related mortality remains the leading cause of death and a major health burden in Asia. Approximately, 49.3% of new cancer cases are located in Asia. Interestingly, more than half of these cases were reported in China, indicating that tumors have always been a major health concern. According to the global cancer statistics, the number of new cases and deaths of liver cancer ranks 7th among all cancers in the United States [
32]. Furthermore, regardless of gender, liver cancer has always been ranked top 10 leading causes of death [
33]. The understanding of underlying molecular mechanisms in HCC is of great importance, especially in the early diagnosis and treatment of the disease. In the present study, bioinformatics analyses combined with experimental validation identified novel biomarkers for the diagnosis and treatment of HCC.
In recent years, the continuous improvements in bioinformatics have been well applied to the discovery of new research directions, new research targets, and therapeutic drugs [
34‐
36]. Xu et al. identified several immune-related lncRNA signatures in HCC through bioinformatics technology [
37]. Liang et al. found that methylation-related genes (CTF1, FZD8, PDK4, and ZNF334) affected the progression of HCC [
38]. The present study identified the lncRNA- AC079061.1/hsa-miR-765/VIPR1 axis, providing great research value in HCC by employing bioinformatics technology.
In the present study, an EZH2-related ceRNA network that is associated with the prognosis of HCC was constructed. Previous studies confirmed EZH2 as an oncogene that accelerates the development of HCC and negatively regulates the expression of immune checkpoint inhibitor PD-L1 in HCC [
19,
20,
39]. Based on the function of EZH2, we screened for relevant differentially expressed lncRNAs, miRNAs, and mRNAs in HCC. Among all the DEGs, lncRNA- AC079061.1, hsa-miR-765, and VIPR1 potentially regulate the progression of HCC. hsa-miR-765 may bind to the 3’-UTR pf lncRNA- AC079061.1 and VIPR1, resulting in mutual regulation.
Interestingly, lncRNA- AC079061.1 has not been reported or studied before in HCC. Therefore, our study is the first to identify that this lncRNA has a function that suppresses HCC progression and the malignant phenotype of HCC. We also found that lncRNA- AC079061.1 is closely related to the prognosis of HCC.
In addition, it was reported that hsa-miR-765 is involved in various types of malignant tumors. Xie et al. verified that miR-765 promoted cell proliferation in HCC [
40]. While Zhu et al. validated that LINC00994 repressed the malignant behaviors of pancreatic cancer cells through regulating the miR-765-3p/RUNX2 axis (which means that miR-765 accelerated the development of HCC) [
41]. The oncogenic effect of miR-765 is not only limited to these two tumors, but also plays a similar role in esophageal squamous cell carcinoma [
42], gastric cancer [
43], and osteosarcoma [
44].
For VIPR1, there are few relevant studies in HCC. However, low expression of VIPR1 has been shown to have an adverse prognostic impact on HCC [
45]. However, it is not enough to study the role of VIPR1 in HCC alone, Zhao et al. also pointed out that VIPR1 has been confirmed to play the same role in lung adenocarcinoma [
46]. These studies revealed the good theoretical feasibility of VIPR1 in HCC.Then, DNA methylation is known to regulate gene transcription and silence tumor suppressors [
47]. A previous study reported that the H3K27 deacetylation and promoter methylation affect the expression of VIPR1 [
45]. Altogether, these findings suggested the antitumor effect of VIPR1 in HCC.
Combining the results of previous studies and corresponding prediction databases, lncRNA- AC079061.1, hsa-miR-765, and VIPR1 were collectively analyzed to investigate the influence on the outcome of HCC either as an independent prognostic factors or as a lncRNA- AC079061.1/hsa-miR-765/VIPR1 axis.
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (
http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.