Background
Hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors globally, specifically in the Asian countries where hepatitis B virus (HBV) infection accounts for 90% cases of HCC [
1]. This renders HBV infection as a major risk factor for HCC [
2]. Although remarkable progress has been made to improve the early diagnostic and therapeutic approaches of HBV-related HCC, the detailed molecular mechanism on how HBV contributes to the HCC development is largely unclear. As a consequence, the breakthrough in clinically diagnostic and therapeutic strategies of HBV-related HCC still faces significant challenges.
The HBV genome is about 3.2 KB long and contains four open reading frames (ORFS), namely S gene, C gene, P gene, and X genome corresponding to encoding outer membrane protein (HBsAg, HBs), nucleocapsid protein (HBeAg/HBcAg, HBc), DNA polymerase (HBp), and HBxAg (HBx) [
3]. The HBV genome is unequivocally required for viral replication [
4]. HBx is essential to initiate and maintain transcription from cccDNA [
5]. HBp, which is encapsulated in Dane particles during HBV replication, can repair short and missing strands in viral genomes to form complete double-stranded DNA [
6]. In addition, these encoding proteins from HBV genome have been implicated in HBV-positive HCC development [
3]. Several reports revealed that these proteins influence carcinogenic processes such as transcriptional activation, epigenetic regulation, and cell cycle progression in promoting the initiation of HBV-positive HCC [
7]. Notably, emerging lines of evidence have shown that HBV proteins regulate non-coding RNAs, particularly microRNAs (miRNAs) that are involved in the replication of HBV [
8].
The tumor development is closely related to metabolic reprogramming. Under low oxygen concentrations, cancer cells shifts to glycolysis from oxidation in converting glucose to lactate for the production of more energy for tumor growth, a rare phenomenon known as the Warburg effect [
9]. Of note, gluconeogenesis is the fundamental characteristic of the liver. The transition from oxidation to glycolysis frequently observed in cancer cells is linked to tumorigenesis [
10]. It was found that the aberrant expressions of anti-oncogene and oncogene involved in the metabolic pathways regulate the metabolic reprogramming in carcinogenesis [
11]. Among the non-coding RNAs, it has been shown previously that miRNA regulates the metabolic reprogramming of cancer cells. Generally, miRNA regulates the expression of metabolism-related genes by inducing degradation of the target messenger RNA (mRNA) or inhibiting the gene expression at post-translational level [
12]. Although previous studies have reported the relationship of miRNA with cancer metabolic regulatory network, the underlying mechanisms on how miRNA regulates the expression of metabolic pathway-related genes or which miRNA modulates tumor glycolysis remain largely unknown.
As a key component of glycolysis/gluconeogenesis pathway, the glycolytic bypass can transform 1,3-bisphosphoglycerate (1,3-BPG) to 2,3-bisphosphoglycerate (2,3-BPG), followed by producing 3-phosphoglycerate (3-PG) and 2-phosphoglycerate (2-PG) under the action of phosphatase (Figure S
1, right) [
13]. Additionally, the glycolytic bypass is a crucial pathway that regulates the oxygen transport function of hemoglobin. In anoxic conditions, such as altitude sickness, or acute respiratory distress syndrome (ARDS), the body initiates this pathway to provide more energy [
14]. For a long time, researchers were confident that only a single enzyme, 2,3-BPG synthase/2-phosphatase (BPGM) was responsible for the glycolytic bypass reaction [
14,
15] (Figure S
1 right α). However, Jaiesoon et al recently revealed a new glycolytic reaction, which could bypass the formation of 3-PG and produce 2-PG (Figure S
1, right β). They found that this reaction was catalyzed by multiple inositol-polyphosphate phosphatase 1 (MINPP1) [
13]. Nevertheless, regardless of the conventional BPGM-mediated bypass or the new MINPP1-mediated bypass, the glycolytic bypass is a protective pathway confined in the red blood cells to correct hypoxia [
16]. There is no existing evidence that the glycolytic bypass including the MINPP1-dependent reaction participates in metabolic reprogramming of tumors.
Herein, we first showed that HBV-positive HCC converts glucose to lactate geared towards providing energy for the proliferation of cancer cells through the glycolytic bypass. MINPP1, an anti-oncogene involved in the glycolytic bypass, was suppressed by an upstream miRNA (miRNA-30b-5p), thereby, facilitating the glycolytic bypass to produce more energy only in HBV-positive HCC. This is because HBp protein promotes the expression of miRNA-30b-5p through interaction with a transcription factor Forkhead Box O3 (FOXO3) to initiate the glycolytic bypass. Our study uncovered a novel mechanism, the HBp/FOXO3/miRNA-30b-5p/MINPP1 axis, contributing to the development of HBV-positive HCC through the glycolytic bypass, and suggested miRNA-30b-5p/MINPP1 as a potential target for treatment strategy against HBV-related HCC.
Materials and methods
Tissue specimens and cell lines
A total of 20 HBV-positive and 20 HBV-negative HCC liver tissue were collected from HCC patients who were firstly diagnosed and underwent surgical resection at the First Affiliated Hospital of Zhejiang University in 2018. The study focused on HCC patients without any prior treatment, and other viral infections including viral hepatitis caused by virus other than HBV (such as HAV, HCV, HEV) were excluded from the study. The human liver cell lines, including the HBV-positive Hep3B and HBV-negative Huh7, were obtained from the State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University. These cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (Gibco™, New Zealand) at the 37 °C, humidified atmosphere with 5% CO2.
Microarray and computational analysis
In total, 7 HBV-positive HCC liver tissue samples with HBsAg (+), HBeAg (+), HBcAb (+), and HBV-DNA > 10
4 IU/ml and 7 HBV-negative HCC liver tissue samples were used for microarray sequence analysis by KangChen Biotech (Shanghai, China). The clinical characteristics of participants were presented in Table S
1. Total RNA was isolated from each tissue specimen using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s instructions. In addition, sample labeling and array hybridization were performed as per the manufacturer’s standard protocols for Agilent One-Color Microarray-Based Gene Expression Analysis and miRNA Microarray System with miRNA Complete Labeling and Hyb Kit (Agilent Technologies, Palo Alto CA, USA). After the removal of rRNA (mRNA-ONLY™ Eukaryotic mRNA Isolation Kit, Epicentre), mRNA was purified from the total RNA. Each sample was then amplified and transcribed into fluorescent cRNA along the entire length of the transcripts without 3′ bias utilizing a random priming method (Arraystar Flash RNA Labeling Kit, Arraystar). The labeled cRNAs were purified by RNeasy Mini Kit (Qiagen, USA). The concentration and specific activity of the labeled cRNAs (pmol Cy3/μg cRNA) were measured by NanoDrop ND-1000 Spectrophotometer (Thermo Fisher, USA). The total miRNA from each sample was labeled with Cyanine 3-pCp under the action of T4 RNA ligase. After hybridization, washing, and fixation, 100 μl of hybridization solution was dispensed into the gasket slide to be assembled to the gene and miRNA expression microarray slide, which is scanned with Agilent DNA Microarray Scanner (part number G2505C). The array images were obtained and analyzed using Agilent Feature Extraction Software v10.7. The low intensity of mRNA and miRNA were discarded followed by normalization of signal intensities through GeneSpring GX v11.5.1 (Agilent Technologies, Palo Alto CA, USA). The differential expression patterns of mRNA and miRNA between HBV-positive and HBV-negative HCC samples were identified by volcano plot filtering. Thereafter, the significantly differential expression of mRNA and miRNA was verified by paired
t-test, and fold change ≥2.0 with
P-value ≤0.05 of mRNA and fold change ≥1.0 with
P-value ≤0.05 of miRNA were being the threshold for statistical significance. Further, the significantly differential expression of mRNAs and miRNAs was clustered using the heatmap R package, and the biological function of the Kyoto Encyclopedia of Genes and Genomes (KEGG) in significantly differential mRNAs was analyzed via clusterProfiler R package. KEGG (
http://www.kegg.jp/) is as an integrated database resource for biological interpretation of genomics, transcriptomics, proteomics, metagenomics, and other high-throughput data by pathway mapping [
17]. Subsequently, the microarray data were uploaded to the Gene Expression Omnibus (GEO) database with an access number of GSE151441 for mRNA and GEO140400 for miRNA.
Real-time quantitative polymerase chain reaction (RT-qPCR)
Total RNA from the tissue and cells was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA) according to the manufacturer’s instruction. A total of 2 μg RNA of miRNA and 5 μg RNA of mRNA was reverse transcribed to cDNA using a miRNA cDNA Synthesis Kit with Poly (A) Polymerase Tailing (abm, Canada) and PrimeScript™ RT reagent Kit with gDNA Eraser (Takara, Dalian, China), respectively. The expression of miRNAs and mRNAs was analyzed by RT-qPCR system (Roche Diagnostics, Basel, Switzerland) using SYBR Premix Ex Taq™ (TaKaRa, Dalian, China). The primers for RT-qPCR are presented in Table S
2. GAPDH/β-actin and U6 snRNA were used as the endogenous controls for mRNA and miRNA RT-qPCR, respectively. The 2
-△△Ct method was used to calculate the relative fold changes in the RNAs.
Cell treatment and transfection
The cells were transfected using Lipofectamine 2000 (Invitrogen Corp., Carlsbad, CA, USA) following the manufacturer’s instruction. Small interfering RNA (siRNA) duplexes against MINPP1 gene was synthesized from GenePharma (Shanghai, China). The mimics, inhibitors, and corresponding negative control of miRNA-30b-5p were obtained from GENECHEM (Shanghai, China). Cells were transfected with 50 nM miRNA-30b-5p mimics in a six-well plate with 2 ml culture medium. The MINPP1 cDNA was cloned into PGLV3/H1/GFP lentiviral vectors using a lentivirus package (GenePharma, Shanghai, China). For lentivirus transfection, 5 × 10
6 transducing units of lentivirus were transfected into cells. The sequence of miRNA-30b-5p mimics was sense, 5′-UGUAAACAUCCUACACUCAGCU-3′ and antisense, 5′-AGCUGAGUGUAGGAUGUUUACA-3′. Other sequences of siRNA and inhibitors used are shown in Table S
2. Transfection of HBV DNA transient was performed by introducing 1.3mer HBV DNA (pHBV1.3) into Huh7 cells. The transfection efficiency of targets into the cells were tested by green fluorescence intensity from Green Fluorescent Protein (GFP) using fluorescence microscopy.
Cell growth and function assays
The cellular proliferation capacity was assessed using the Cell Counting Kit-8 (CCK-8) (Beyotime Inst Biotech, China) when the cells were cultured in a hypoxic incubator (Heal Force, Shanghai, China) according to the manufacturer’s guideline. The absorbance of cells was measured with a microplate reader (Bio-Rad, Hercules, CA, USA) at a wavelength of 450 nm. The scrape motility assay was used to measure cell migration. Untreated cells were plated into culture inserts and then wound tip was created by scratched the cell monolayer with a sterile 200 μl pipette tip. Images of wound monolayers were captured using an inverted microscope (Olympus, Japan) at × 100 magnification at 0, 24, and 48 h post-wounding.
Luciferase reporter assay
Luciferase reporters were constructed in the psiCHECK2 vector (Promega, Madison, WI, USA). The complete 3′ untranslated regions (UTR) of MINPP1 gene with the putative binding sites of miRNA-30b-5p were amplified and cloned into the psiCHECK2 vector to create psiCHECK2-MINPP1. Cells were then seeded in 24-well plates and allowed the cell density of 5000 cells per well overnight. The luciferase reporter was co-transfected with miRNA-30b-5p-wild-type mimics, miRNA-30b-5p mutant-type mimics, and control vectors into cells by Lipofectamine 2000. After 48 h of transfection, the luciferase activity was assessed by the dual-Luciferase Reporter Assay System (Promega, Madison, WI, USA).
RNA fluorescence in situ hybridization (FISH)
Fresh HBV-positive and HBV-negative HCC liver samples were collected from surgical resection and immediately stored in liquid nitrogen, then the tissue were cut into 5-μm-thick sections and adhered to slides. The tissue was washed with phosphate-buffered saline (PBS) and fixed in 3.7% formaldehyde for 10 min. After washing the slides in 2 × sodium citrate buffer with a solution of 10% formamide, 4 μl fluorescent probes were added to the hybridization solution, which contained 10% dextran sulfate, 10% formamide, and 2 × sodium citrate buffer. Hybridization using MINPP1 and miRNA-30b-5p probes were performed overnight at 37 °C. The slides were rinsed twice for 20 min in 2× sodium citrate buffer with a solution of 10% formamide, then counterstained with 4′-6’diamidino-2-phenylindole (DAPI). The miRNA-30b-5p was labeled with 6-carboxy-fluorescein fluorophore (CY3) while MINPP1 was labeled with cyanine dye 3 (FAM). The location of MINPP1 and miRNA-30b-5p was detected using confocal laser scanning microscopy (Leica, Wetzlar, Germany).
Cells were cultured in phenol red-free DMEM for 15 h, then the medium was harvested for the measurement of metabolic indicators, including lactate production, glucose consumption, and 2-PG level. The glucose, lactate, and 2-BGP levels in the medium were quantified using glucose assay kit, lactate Assay kit, and 2-PG kit according to the manufacturer’s instructions (BioVision, Mountain View, USA).
Xenograft in animal and histological immunohistochemistry
Six-week-old female BALB/c nude mice were purchased from the Beijing Experimental Animal Center (CSA, Beijing, China). For the establishment of xenograft tumors in animals, Hep3B cells stably expressing miRNA-30b-5p, inhibitors miRNA-30b-5p, MINPP1, and overexpressing MINPP1 were harvested and suspended in DMEM, and 3 × 106 Hep3B cells in 200 μL of DMEM were subcutaneously injected into the proper bilateral flanks. Using the calipers, the tumor size was measured at an interval of every 2 days. After 4 weeks, the nude mice were sacrificed, their tumors dissected and the growth of subcutaneous tumors was measured. The tumors were then fixed with phosphate-buffered neutral formalin for histological examination. Notably, 44% PBS was used to buffer formaldehyde and extract the tissue, which were then embedded in paraffin and sectioned. The antibodies of MINPP1, miRNA-30b-5p, and proliferating cell nuclear antigen (PCNA) (Abcam, Cambridge, MA, USA) were subjected to immunohistochemical (IHC) analyses. The immunoreactivity was quantified by the horseradish peroxidase kit (BioGenex, Fremont, CA, USA). Moreover, the tissue were counterstained with hematoxylin-eosin (HE). All animal experiment procedures were performed according to the Animal Care Commission of Zhejiang University.
The research data, constituted of RNA sequencing (RNA-Seq), miRNASeq, and clinical follow-up information, were downloaded from The Cancer Genome Atlas (TCGA) database. Sixty HBV-positive HCC and 211 HBV-negative HCC samples were selected for subsequent analysis. In addition, the GSE55092 dataset was collected, including 49 HBV-positive HCC samples from the GEO database. Student’s
t-test and Wilcoxon test were performed to assess the differences in variance between indicators for normally distributed and non-normally distributed data respectively. On the other hand, Kruskal–Wallis tests were used to perform statistical analysis for nonparametric testing of three or more datasets. The NetworkD3 R packages were used to depict the alluvial diagram of mRNA and corresponding KEGG pathways. A alluvial diagram is a kind of plane diagram that can visually show the changes between groups, time series, complex multi-attribute, and multi-correlation [
18]. The mRNA-miRNA regulatory network was analyzed by Weighted Correlation Network Analysis (WGCNA) R package and was depicted using Cytoscape software. Additionally, miRanda (
http://www.microrna.org/microrna/home.do) and TargetScan (
http://www.targetscan.org/vert_72/) tools were used to predict the direct target of 3′ UTR in MINPP1. The association between MINPP1 and miRNA in the TCGA was calculated by the Pearson correlation coefficient, with a correlation diagram being used to depict the relationship by Corrplot R package. HBV-positive HCC samples of TCGA were divided into high and low expression groups by the median MINPP1 expression. The Kaplan–Meier (KM) method was used to depict the survival curve and estimate the survival probability of patients which was further examined by the log-rank test. The Forest plot was performed using the Forest plot R package to present the univariate Cox regression analysis for statistical summary of clinical indicators and MINPP1. The relationship between MINPP1 and genes from TCGA and GSE55092 datasets was analyzed using coexpression models i.e., WGCNA R package. Moreover, the association between miRNA from KEGG and predicted genes were revealed through the Hmisc R package. Of note, WGCNA is a systems biology method applied in constructing scale-free networks using gene expression data. The overlapped genes associated with MINPP1 between TCGA and GSE55092 were used for KEGG and gene ontology (GO) analyses by clusterProfiler R package and GOplot R package respectively. For statistical analyses, SPSS software and R package were used, and
P < 0.05 was considered statistically significant.
Discussion
HBV is the most common causative factor for HCC. HBV infections can lead to chronic hepatitis and cirrhosis [
26]. Therefore, understanding of the molecular mechanisms through which HBV triggers HCC is crucial for effective HCC diagnosis and management. In this study, microarray, sequencing, cell biology experiment, and multi-dimensional bioinformatics methods were employed to analyze interactions among genes, miRNA, signaling pathways that regulate the progression of HBV-related HCC. Unlike other studies that compared different molecules between tumor cells and normal cells, we compared HBV-positive and HBV-negative HCC tissue or cells. In this way, we precisely reveal the molecular mechanisms through which HBV infection causes HCC.
In this study, 110 genes were found to be significantly differentially expressed between HBV-positive and HBV-negative HCC liver tissues, which was far less than the number often reporeted between HCC and normal tissues [
27‐
29]. Moreover, 6 biological pathways identified to be associated with these differentially expressed genes. Therefore, our results reveal shared and uniques pathways between HBV-positive and -negative HCC allowed to focus shared pathogenesis pathways and evidenced those involved uniquely in the HBV pro-cancer activity. Among the 6 biological pathways, glycolysis/gluconeogenesis was found to be closely associated with metabolism that drives tumorigenesis [
30]. In addition, we identify the MINPP1 gene which participate in the glycolytic bypass, a component of anaerobic glycolysis. These findings are in agreeement with previous studies, which reported that tumor metabolic reprogramming mechanisms provide energy for its own growth through glycolysis with relatively low oxidative productivity [
10]. Several genes involved in metabolism, such as
TUG1 [
31],
HIF [
32], and
PKM2 [
33] regulate the development of HCC. However, there is no evidence that metabolic pathways involved in tumorigenesis contribute to dysregulation of MINPP1. Thus, exploratory experiments were performed to determine the function of MINPP1 in HCC. Results showed that MINPP1 inhibits HCC development, but this was limited to HBV-positive HCC and not other liver cancers. These findings reveal that MINPP1 expression is decreased in HBV-positive HCC, suggesting MINPP1 may be a specific biomarker of HBV-positive HCC. MINPP1 enzyme hydrolzyes inositol pentakisphosphate (IP5) and inositol hexakisphosphate (IP6) [
13]. Previous studies found that IP6 is a co-factor required for efficient production of infectious human immunodeficiency virus (HIV) particles [
34]. Clifton et al. found that MINPP1 can ablate IP6, therefore decrease the HIV infectious particles, whereas a decrease in MINPP1 expression would increase IP6 expression favoring the formation of HIV particles [
35]. We hypothsized that MINPP1 may suppressthe development of HBV-positive HCC through other metabolic pathways, such as ablation of IP6 which inhibits HBV formation and replication. This may be a novel hypothesis to the mechanism of this reseach, which worthy of further research.
This study demonstrates a novel pathomechanism of HCC tumorigenesis involving the glycolytic bypass (Figure S
1, right α), which is a separate dephosphorylation of 2,3-BPG to 2-PG, a process catalyzed by MINPP1 [
13]. Most of the molecules studied in glycolytic bypass regulate systemic oxygen homeostasis [
36]. In most mammals, 2,3-BPG of the glycolytic bypass is the major allosteric effector which facilitate the release of oxygen from hemoglobin in red blood cells to the surrounding tissue [
37]. Therefore, the glycolytic bypass facilitates the supply of oxygen is thus a fundamental physiological negative feedback regulation for hypoxia [
14]. This study suggests for the first time that tumor cells may mimic the glucose metabolism of red blood cells through the glycolytic bypass. This is a novel approach for studying the metabolism of HBV-positive HCC. It is also important to investigate whether the glycolytic bypass participates in other types of cancer.
Studies have reported that gene involved in diseases interact with other molecules or are regulated by upstream molecules or modulated by epigenetic modification [
38,
39]. We therefore aimed to identify molecules that regulate the expression of MINPP1. Research on functions of non-coding RNAs has achieved significant results in recent years, especially the regulatory mechanisms of miRNA [
40]. In addition, several studies have revealed that miRNA participates in tumorigenesis, including HCC development [
41]. Here, we found thatmiRNA-30b-5p was up-regulated in HBV-positive HCC and inhibited the translation of MINPP1 by binding to its base binding site. Further analysis revealed that miRNA-30b-5p could be a potential therapeutic target in HBV-positive HCC.
We combined experimental and bioinformatics analysis methods in this research. Multi-dimensional bioinformatic analysis was conducted to identify potential biomarkers of gene and miRNA, and their association in HBV-positive HCC. Importantly, through bioinformatic analysis, we analyzed the expression level of MINPP1 and its clinical significance in a big database of HCC samples. Bioinformatic analysis based on big data transcriptome conquers the shortcomings of small sample size and low homogeneity of tissue, and confirms the plausibility of accurately identifying the pathogenesis and related biomarker of the disease [
42]. By combining bioinformatics and experimental analyses, we obtained more accurate results.
The miRNA-30b-5p/MINPP1 axis contributes to HCC development through the glycolytic bypass, and this is limited to HBV-positive HCC. The present study shows that HBV infection activates the miRNA-30b-5p/MINPP1/glycolytic bypass axis in HBV-positive HCC. Furthermore, we show that HBp promotes the expression of miRNA-30b-5p. Emerging evidence reveals that miRNAs are involved in HBV replication [
43]. Such studies have also revealed the mechanism through which HBV modulates expression of cellular miRNAs, such as intercation between transcriptional factors and promoters [
44] and epigenetic modifications of promoters by HBV proteins [
45]. In this study, we found that HBp promotes the expression of miRNA-30b-5p by interacting with FOXO3. HBp is a broad-range transactivator that stimulates the transcription of its own HBV genes as well as other host genes, including proto-oncogenes related to HCC. Several studies have investigated how HBx regulates miRNAs, but few have explored how HBp modulates miRNAs. Therefore, further investigations are essential to accurately reveal how HBp regulate miRNA-30b-5p.
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