Background
Hepatocellular carcinoma (HCC) is the most common primary hepatic malignancy, accounting for ~ 90% of all primary liver cancers, which is the third most common cause of cancer-related deaths worldwide [
1]. Most HCC patients tend to have an underlying liver disease, such as hepatitis B or C, alcohol abuse disorder or non-alcoholic steatohepatitis. Therefore, efficacy of the current therapeutic approaches is limited [
2]. In addition, the prognosis of HCC remains dismal due to the lack of effective non-surgical treatment options, especially for patients at the advanced stage. Programmed cell death protein 1 (PD-1) immune checkpoint inhibitors have been approved for treatment of HCC. However, this was accompanied by a high rate of adverse events and low treatment response in patients with advanced disease [
3]. Sorafenib, the first FDA-approved, molecular-targeted drug for advanced-stage HCC, unfortunately has limited survival benefits. However, tyrosine kinase inhibitor ramucirumab has showed markedly survival benefits in HCC patients with elevated serum α-fetoprotein (AFP) after previous treatment with sorafenib [
4].
TP53, CTNNB1 and ALB were identified as the most frequently mutated loci in HCC [
5‐
7]. Furthermore, a recent study, investigating the mutation landscapes in HCC, identified PYCR2 and ADH1 as significant prognostic biomarkers that are involved in the metabolic reprogramming of HCC cells [
8]. However, these mutated gene signatures are yet to be translated so far into clinically relevant biomarkers. This is mainly due to the lack of druggable targets, considerable genetic and epigenetic heterogeneities in HCC. Aberrant DNA methylation has been associated with oncogenesis in human cancers including HCC [
9‐
11]. Recent evidence showed that promoter hypomethylation increases ITPR3 expression while promoter hypermethylation reduce OGDHL expression in HCC [
10,
11], which indicates that epigenetic regulation plays an important role in HCC development and there are promising epigenetic therapy targets for HCC patients.
Identification of novel causative molecular biomarkers would contribute to understand the molecular events in HCC development, which can potentially optimize the clinical therapy of HCC patients. Determination of epigenetic mechanism regulating potential oncogene expression in HCC would aid in identifying potential drug targets for development of novel epigenetic therapy agents. Therefore, we performed an integrated analysis of the transcriptomic, proteomic and methylomic datasets of HCC. We further validated the expression profiles of minichromosome maintenance complex component 2 (MCM2) and nucleoporin 37 (NUP37) in both clinical HCC samples and human HCC cells. Furthermore, we determined the prognostic role of MCM2 and NUP37 by analysis of three large independent HCC cohorts (n = 820). In addition, we investigated and validated the epigenetic mechanisms upregulating MCM2 and NUP37 expression in HCC.
Methods
Public data sources and bioinformatic analysis
Public bulk-RNA seq datasets TCGA-LIHC (
https://portal.gdc.cancer.gov/projects/TCGA-LIHC), GSE57957 (
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57957), and E-MTAB-4171 (
https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-4171/), including 421 HCC samples and 104 paired nontumor liver samples, were downloaded for identification of differentially expressed genes (DEGs) at mRNA levels. Public proteomic dataset OEP000321 (
https://www.biosino.org/node/project/detail/OEP000321) including 159 pairs HCC and adjacent nontumor liver samples were downloaded from the National Omics Data Encyclopedia (NODE) for identification of differentially expressed proteins (DEPs). Moreover, public methylomic datasets E-MTAB-4169 (
https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-4169/), TCGA-LIHC (
https://portal.gdc.cancer.gov/projects/TCGA-LIHC), and GSE56588 (
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56588), including 638 HCC samples, 93 paired nontumor liver samples, 10 normal liver samples and 9 cirrhotic liver samples, were analyzed for determining the methylation patterns of potentially identified biomarkers for HCC patients.
The R package “TCGAbiolinks” [
12] was used to download and process the raw data of TCGA-LIHC datasets. Differentially expressed genes (DEGs) as well as differentially expressed proteins (DEPs) between the HCC and nontumor tissues were identified using the ‘limma’ R package [
13] based on
P value < 0.05 and |logFC|
\(\ge\) 2 and 1, respectively. In addition, differential methylation regions (DMRs) were identified among CpG or non-CpG islands throughout the genome scale. The “minfi” and “limma” R packages [
13,
14] were used for identification of DMRs with
P value < 0.05 and |logFC|
\(\ge\) 0.2.
Patients and tissue specimens
We prospectively collected 10 HCC and adjacent tissue (peritumor) samples from primary HCC patients without receiving neoadjuvant treatment after curative surgical therapy from May to July 2020 at the First Affiliated Hospital of Zhejiang University, Hangzhou, China. The collected each fresh specimen was snap-frozen in liquid nitrogen, and then used for protein and RNA extraction, or stored at −80 °C for subsequent experiments. Moreover, two sets of tissue microarrays (TMAs), generated in-house, including paraffin-embedded tissues from 300 primary HCC patients who had undergone surgical resection from 2010 to 2018 in our hospital, were used for immunohistochemistry (IHC). All patients were followed-up routinely every 3 to 6 months after surgery. The clinicopathological information on these 300 cases of HCC patients was summarized in Additional file
1: Table S1. Written informed consents were obtained from each patient in this study. The experiment protocol for use of clinical samples was approved by Institutional Ethics Committee in First Affiliated Hospital of Zhejiang University.
Reagents and antibodies
Anti-MCM2 antibody (HPA031496), anti-NUP37 antibody (ab220675), anti-GAPDH antibody (ab181602), Goat Anti-Rabbit IgG H&L (ab205718) and DAB Substrate Kit (ab64238) were purchased from Sigma and Abcam, respectively. Human MCM2, NUP37 and β-actin primer sets were purchased from Gene Chem (Shanghai, CN). Decitabine (S1200) and CPI-455 (S8287) were purchased from Selleck (Shanghai, CN). Dimethyl sulfoxide (DMSO) was purchased from Sigma.
Cell culture
Human HCC cell lines Huh7, Hep3B, HepG2, Bel-7402, HCC-LM3, normal liver cell line HL7702, and mouse hepatoma cell line Hep1-6 were purchased from The Cell Bank of Chinese Academy of Sciences (Shanghai, China) with authentication between 2018 and 2020. HL7702, Huh7, and Bel-7402 were cultured in high glucose DMEM (Gibco), other cells were cultured in MEM (Gibco) supplemented with 10% FBS, 100 μg/ml streptomycin (Gibco), and 100 U/mL penicillin (Gibco) at 37 °C in 5% CO2 atmosphere. All cell lines had been tested regularly for mycoplasma contamination. In addition, 3 × 105 Huh7 cells per well were planted in 6-well cell culture plate and incubated for 48 h in high glucose DMEM supplemented with 10% FBS and the indicated concentration of decitabine or CPI-455. Decitabine was dissolved with phosphate buffered saline (PBS) and CPI-455 was dissolved with DMSO.
Cell viability assay
Cell viability was assessed using a CCK8 assay (Beyotime, Shanghai, CN). Briefly, Hep3B cells (1 × 104 cells per well) were seeded into 96-well plates and incubated in antibiotic-free growth medium supplemented with decitabine, CPI-455, PBS or DMSO. After incubated for 24, and 48 h, fresh complete medium with CCK-8 (1:10) was added to each well, and the cells were incubated at 37 °C in 5% CO2 atmosphere for 1 h. Finally, the absorbance at 450 nm was detected using a microplate reader (800 TS, BioTek, USA).
Immunoblotting
Soluble proteins from fresh HCC and paired peritumor tissue were obtained after following procedure: (1) manually grinding tissue into powder within 2 min using a clean mortar after immersed it into liquid nitrogen; (2) then collecting the tissue powder (5–10 mg) into a pre-cold Eppendorf tube containing 300 μl RIPA lysis buffer (Beyotime, Shanghai, CN) supplemented with 1 × protease inhibitor buffer (#5871, Cell Signaling Technology, CN) on ice; (3) constantly agitating the mixture on an orbital shaker at 4 °C atmosphere for 2 h; (4) After centrifugation at 16,000g for 20 min at 4 °C, the supernatants were collected. For normal liver cells and HCC cells, washing two times with ice-cold PBS, and lysis with 100 μl RIPA buffer supplemented with protease inhibitor cocktail for 30 min on ice. After centrifugation at 12,000g for 15 min at 4 °C, the supernatants were collected as the whole cellular protein extracts. Both tissue and cellular soluble protein extracts were quantified using the bicinchoninic acid protein assay kit (Beyotime, Shanghai, CN).
A total of 30 μg protein was resolved on SDS-PAGE (10% polyacrylamide), transferred to polyvinylidene difluoride (PVDF) membranes. Membranes were blocked with 5% skim milk for 1 h at room temperature, then incubated with anti-MCM2 antibody (0.1 μg/ml), anti-NUP37 (1:2000) or anti-GAPDH antibody (1:5000) overnight at 4 °C. After 3 times washes of membranes in 1× tris-buffered saline with tween 20 (TBST), the membranes were incubated with anti-rabbit immunoglobulin G-horseradish peroxidase-linked secondary antibody (1:3000) for 2–3 h at 4 °C. The protein bands were visualized by a Chemiluminescence Dectection Kit for HRP according to the manufacture’s instruction and detected using ChemiScope 6100 Touch system (CliNX, CN).
RNA isolation and real-time PCR
Total RNA from human cells or tissues was extracted using Trizol reagent (Life Technologies) according to the instruction of the user manual. The concentration of RNA was determined by spectrophotometry at 260 nm using Nanodrop One (Thermo SCIENTIFIC). The purity of sample was determined by the 260/280 nm ratio. The detected ratio, ranging from 1.8 to 2.1, indicating that the extracted RNA was fine and suitable for subsequent experiments. cDNAs were generated from 1 μg RNA of each samples by using PrimeScript™ RT reagent Kit with gDNA Eraser (Takara, Japan). Real-time PCR (RT-PCR) analysis was performed using TB Green ™ Premix Ex Taq™ (Tli RNaseH Plus, Japan). A final volume of 20 μl mixture was amplified by RT-PCR using ABI QuantStudio-5 Real-Time PCR System (Applied Biosystems by Thermo Fisher Scientific). Reaction conditions were as follows: 95 °C for 30 s, followed by 40 cycles of denaturation at 95 °C for 5 s and annealing at 60 °C for 30 s. The relative mRNA expression was determined using the ΔΔCT method. The primer sequences used in real-time PCR are as follows: MCM2, forward 5’-CTACCAGCGTATCCGAATCCA-3’ and reverse 5’-CCTACAGCAACCTTGTTGTCCT-3’; NUP37, forward 5’-CTGCGTTTCGTGACCTTGTC-3’ and reverse 5’-TACACGTGCCAATGACCACA-3’; β-actin, forward 5’-CGACAGGATGCAGAACGAGA-3’ and reverse 5’-GACCCTGGATGTGACAGCTC-3’.
Immunohistochemistry (IHC)
Immunohistochemical staining was performed as described previously [
15]. After anti-NUP37 antibody (1:200) and anti-MCM2 antibody (1:200) were incubated with tissue sections and TMAs overnight at 4 °C, the biotinylated secondary antibody (1:300) was applied before visualization using DAB substrate. The IHC reaction result was calculated by multiplying a percentage score and staining intensity score. The percentage score was used to describe the estimated percentage of positively stained neoplastic cells (0: 0–5%; 1: 6–25%; 2: 26–50%; 3: 51–75%; 4: > 75%). The intensity score described the estimated staining degree (0: negative; 1: weak; 2: moderate; 3: strong). Both the percentage score and staining score were evaluated independently by two pathologists who were blinded to the research design. Any discrepancy wound be resolved by a consensus among these two pathologists after a re-assessment. The NUP37 score and MCM2 score were used to stratify patients into low and high groups according to the optimal cut-off value.
Pyrosequencing
For validation of the methylation pattern of MCM2 enhancer region and NUP37 promoter region, genomic DNA from frozen HCC tissues and paired nontumor tissues samples (n = 6) were extracted using Blood and Tissue Kit (#69504, Qiagen). The concentration of genomic DNA from each sample was determined using NANO Quant infinite M200PRO (TECAN) and a total of 500 ng genomic DNA (A260/A280 ratios ranging from 1.8 to 2.0) from each sample was used for bisulfite DNA conversion using the EpiTect Bisulfite Kit (#59104, Qiagen) following the manufacturer’s handbook for user. After cleanup of bisulfite converted genomic DNA, we amplified the target sequence using ABI 9700 PCR System and performed quantitative pyrosequencing using the PyroMark Q96 ID (QIAGEN). The genomic DNA methylation percentage was calculated with using Pyro Q—CpG software (Biotage). The primer sequences used in PCR amplification and pyrosequencing, as well as the chromosomal location of each target sequence in this study were summarized in Additional file
1: Table S2. Pyrosequencing was performed using the platforms from Geneland Biotech Co., Ltd (Shanghai, CN).
Experimental animal model
To establish subcutaneous implantation model that was used to assess the impact of decitabine on HCC progression in vivo, 3 × 105 Hep1-6 cells (per mouse) in the logarithmic phase, resuspended in 100 μl PBS, were injected into the flanks of 5-week-old male C57BL/6 J mice (Model Animal Research Centre of Nanjing University, Nanjing, CN). Tumor growth was monitored daily using a digital caliper. Upon tumor reached 150 to 300 mm3, mice were randomized into control (PBS) or decitabine (3 mg/kg, 3 times weekly) groups with each group involving 5 mice. The mice were administrated with decitabine or PBS by intraperitoneal injection. After the treatment, each mouse body weight was monitored daily using an electronic balance. Mice were sacrificed according to the Institutional Animal Care & Use Committee (IACUC) protocol and subcutaneous tumor lesions were completely excised with using the dissection instruments in a mouse surgical kit. We calculated the tumor volume as follows: Volume = length × (width)2 × π/6.
All animal experiments were performed in accordance with the National Institutes of Health’s Guide for the Care and Use of Laboratory Animals. The animals were housed in a specific-pathogen-free room with a 12-h light/dark schedule in the facility of the First Affiliated Hospital, Zhejiang University School of Medicine. All animals were fed an autoclaved water and standard food ad libitum, and routine husbandry procedures and daily care were provided by animal care staffs of the facility. All experimental animals were routinely monitored for their general health status, and the experimental protocol was reviewed and approved by IACUC.
Statistical analysis
All statistical analyses were performed with using R version 4.0.3 for Windows 64-bit. Data are presented as arithmetic mean ± Standard Deviation (SD). ANOVA, chi-square test, fisher's exact test, Student’s t test, or Wilcoxon test was used to determine the statistical significance according to variable type and distribution. The “surv_cutpoint ()” function, encoded in the “survminer” R package (
https://cran.r-project.org/web/packages/survminer/index.html) was used to determine the optimal cut-off point for continuous variables that correspond to the survival of HCC patients. Overall survival (OS) analysis was performed using the Kaplan–Meier method, and survival differences were determined by log-rank test encoded in “Survival” R package (
https://cran.r-project.org/web/packages/survival/index.html). Multivariate survival analysis was performed using the Cox proportional hazards regression model. Spearman or Pearson correlation analyses were performed to determine the relationship between NUP37 and MCM2 expression, MCM2 or NUP37 mRNA expression and DNA methylation levels, respectively. Differences or correlations with
p values < 0.05 were considered statistically significant.
Discussion
To identify novel prognostic markers and potential epigenetic therapy targets for primary HCC patients, we performed an integrative analysis of primary HCC-associated multi-omics datasets. Transcriptomic analysis revealed a total of 1915 DEGs between HCC and nontumor. Meanwhile, proteomic analysis only identified 368 DEPs occurred in HCC development when compared with nontumor samples. These findings indicated that there are a lot of molecular events occurred in HCC development and there are many potential drug targets for precision treatment of HCC. Moreover, the huge gap between the DEPs and DEGs for HCC implied that there are complex post-transcriptional modification mechanisms regulating DEGs mRNA translation. Methylation frequently occurred in DNA sequences with a high density of cytosine-guanine dinucleotides, which plays a significant role in maintaining chromosomal stability [
16]. In our work, methylomic analysis revealed that demethylation frequently occurred in HCC development, which possibly upregulates HCC-associated gene expression at transcriptional level.
MCM2, one member of the highly conserved minichromosome maintenance complex (MCM) protein family, initiates eukaryotic genome replication and promotes symmetric inheritance of modified histones during DNA replication [
17]. MCM2 has been identified as a new biomarker for predicting tumor cell proliferation and prognosis of various cancers [
18,
19]. Although several recent studies with small sample size reported that MCM2 upregulated in HCC and promotes HCC cell proliferation [
20,
21], -lack of evidence elucidating the epigenetic mechanism regulating MCM2 expression in human HCC. In our present work, firstly, we comprehensively revealed and validated the expression profile of MCM2 in both clinical HCC samples and human hepatoma cell lines. Then we confirmed that MCM2 overexpression in primary HCC tissues was significantly associated with worse OS in 3 large independent HCC cohorts (Fig.
4C). Furthermore, multivariate survival analysis further showed that MCM2 upregulation was an independent risk factor for OS of HCC patients (Fig.
4F). Interestingly, MCM2 protein was lowly detected in HepG2 and Hep3B cell lines (Fig.
1H) whereas the relative expression level of MCM2 transcript was higher in these two cell lines relative to the remaining cells (F
ig.
1I), which may be caused by some potential post-transcriptional modifications occurred more frequently that inhibit specific translation processes of MCM2 mRNA in HepG2 and Hep3B cells.
Aberrant DNA methylation has been correlated with oncogenesis of multiple cancers [
9]. DNA hypomethylation of promoter regions played a key role in regulating ITPR3 gene expression in human HCC [
11]. In the present work, we firstly elaborated the demethylation profiles of MCM2 promoter region in HCC compared with nontumor samples. Moreover, we found that DNA methylation degree of MCM2 enhancer (cg08889930) was responsible for regulating MCM2 expression in HCC. Our work also determined that demethylation at MCM2 enhancer is a common event in human HCC development. DNA methyltransferase (DNMT) inhibitors azacitidine and decitabine, antimetabolites that can inhibit DNMT activity and induce hypomethylation when incorporated into DNA, have been approved to treat myelodysplastic syndrome or leukemia [
22,
23]. In our study, we showed that decitabine can upregulate MCM2 expression by inducing hypomethylation in MCM2 genome-scale. Inversely, we also found that MCM2 expression was attenuated in HCC cells after treatment with KDM5 inhibitor CPI-455. Together, these finding implied that MCM2 is a potential epigenetic therapy target for HCC patients.
NUP37, also known as p37 or MCPH24, a part of nuclear pore complexes (NPC), is essential for kinetochore-microtubule interaction and mitosis. NUP37 has been reported as an oncogene in lung and liver cancers [
24‐
26]. Although NUP37 was demonstrated to be capable of promoting HCC progression via regulation of YAP/TEAD pathway [
24], the clinical significance of NUP37 and epigenetic mechanism of NUP37 expression in HCC cases remain largely unknown. Our work comprehensively revealed that NUP37 overexpressed in both clinical HCC samples and human hepatoma cell lines. We also determined that NUP37 overexpression significantly correlated with worse OS in patients with primary HCC from 3 large independent cohorts (Fig.
4D). In addition, multivariate cox survival analysis indicated that NUP37 overexpression was a novel independent risk factor for OS of patients with primary HCC.
Unlike the demethylation profiles of MCM2 in HCC, we found that DNA methylation pattern of NUP37 promoter region in clinical HCC samples was similar with that in adjacent nontumor samples. Interestingly, in comparison with the DNA methylation levels of NUP37 promoter-associated sites, promoter-unassociated sites (cg24826236, cg08316365 and cg08085165) had markedly higher methylation levels (Fig.
8A–C). Furthermore, correlation analysis indicated that DNA methylation level of MCM2 enhancer (cg08889930) significantly negatively correlated with NUP37 mRNA expression in HCC, which indicated that demethylation at cg08889930 indirectly regulating NUP37 expression at transcriptional level. In addition, we found that decitabine can upregulate NUP37 expression by inducing hypomethylation at genome-scale. Inversely, NUP37 expression can be suppressed in HCC cells after treated with KDM5 inhibitor CPI-455. All in all, these finding implied that NUP37 is a potential epigenetic therapy target for HCC patients.
In summary, our work demonstrated that the MCM2 or NUP37 overexpression correlates with worse clinical outcome for patients with primary HCC from 3 large independent cohorts. Demethylation at MCM2 enhancer was a common event in HCC patients. Demethylation at enhancer significantly upregulates MCM2 or NUP37 expression. NUP37 and MCM2 are potential epigenetic therapy targets for HCC patients.
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