In silico and experimental analyses predict the therapeutic value of an EZH2 inhibitor GSK343 against hepatocellular carcinoma through the induction of metallothionein genes

Identification of EZH2 as a therapeutic target for HCC treatment

To identify possible candidate genes essential for HCC pathogenesis, we analyzed gene expression profiles between normal and tumor liver tissues from three published microarray datasets [15-17]. The results found that 13 genes are upregulated in tumor tissues in these datasets (Figure 1A and Table 1). Their functional association was analyzed by the GeneMANIA (http://genemania.org/) [18]. Most of them are associated with cell cycle regulation (as marked by red color in Table 1), suggesting that HCC may be resulted from dysregulation of cell cycle. In addition, several genes have been reported to correlate with HCC pathogenesis, For example, deregulation of E2F1 has been implicated in the development of HCC [19]. TOP2A overexpression in HCC correlates with shorter patient survival and chemoresistance [20]. Pathway analysis showed the relationship between EZH2 and other genes in a direct or indirect manner (Figure 1B), implying EZH2 may have similar function with these genes. Indeed, EZH2 has been linked to cell cycle machinery through cyclin-dependent kinases 1/2 (CDK1/2)-dependent phosphorylation at Thr350. Blockage of Thr350 phosphorylation reduces EZH2-mediated cell proliferation and migration [21]. Therefore, we proposed that EZH2 may also participate in HCC pathogenesis. Consistently, recent studies have shown that EZH2 plays an important role in HCC tumorigenesis [22-24]. Overexpression of EZH2 is frequently detected in HCC tissues, which was correlated with the aggressiveness and poor prognosis [36-38]. Knockdown of EZH2 expression in HCC cells can reverse tumorigenicity in a nude mouse model [25], demonstrating the potential therapeutic value of EZH2 inhibition in HCC.

Figure 1. Identification of EZH2 as a potential therapeutic target for HCC treatment A. The Venn diagram for gene expression profiles between normal and tumor liver tissues from three published microarray datasets. The overlapped genes were shown in Table 1. B. Functional association of the genes in Table 1 was analyzed using the GeneMANIA (http://genemania.org/). The genes in black circles stood for the query genes. The genes in grey circles stood for the genes related to query genes.

Overexpression of EZH2 in HCC tissues

To confirm the relationship of EZH2 and HCC pathogenesis, we analyzed EZH2 expression profiles using existing cDNA microarray datasets deposited in the Oncomine database (http://www.oncomine.org/) [26]. In three microarray datasets having both HCC and normal liver tissues [27-29], significantly increased EZH2 gene copy number and mRNA expression in HCC compared with normal liver tissues were found (Figures 2A-2C). In addition, Wurmbach's dataset also shows the upregulation of EZH2 mRNA in samples from tissues of normal, cirrhosis, dysplasia, to HCC in a stepwise manner (Figure 2D). To investigate whether EZH2 was also overexpressed at protein level, the immunohistochemical staining of EZH2 in normal and cancer tissues was obtained from the Human Protein Atlas (http://www.proteinatlas.org/) that is a public database with millions of immunohistochemical images in 44 different normal human tissues and 20 different cancer types, as well as 46 different human cell lines [30-34]. As shown in Figure 2E, the protein expression of EZH2 was undetectable in normal liver tissues. In liver cancer tissues, however, 6 out of 11 cases are high/medium EZH2 staining and 5 out of 11 cases are low or undetectable. These results suggest that EZH2 is overexpressed in HCC and may be an attractive molecular target for treating HCC.

Figure 2. The gene and protein expressions of EZH2 in HCC patients A.-C. The copy number and mRNA expression of EZH2 in normal liver and tumor tissues. D. EZH2 mRNA expression in tissues of normal, cirrhosis, dysplasia, and HCC patients. Microarray datasets were obtained from the Oncomine database (http://www.oncomine.org/). E. EZH2 protein expression in normal liver and tumor tissues were obtained from the Human Protein Atlas (http://www.proteinatlas.org/).

GSK343, an EZH2 inhibitor, induces metallothionein (MT) expression that is correlated with its anticancer activity

Small-molecule-based inhibition of EZH2 has recently been developed as an effective mechanism of therapeutic intervention in hematologic and solid tumors [12, 35]. Our previous study shows that an activity-based EZH2 inhibitor, GSK343 (Figure 3A), induces autophagy and enhances drug sensitivity in cancer cells including HCC. In this study, we also found that GSK343 inhibited cell viability of a human HCC cell lines, HepG2, in a dose-dependent manner (Figure 3B). To investigate the molecular mechanism involved in the anti-HCC activity of GSK343, microarray analysis was performed and found that 46 gene probes were upregulated and 123 gene probes were downregulated (Supplementary File S1). Top 10 up- and down-regulated gene probes were listed in Table 2. Interestingly, several genes (highlighted in bold) encode metallothionein 1 (MT1) subtypes and MT2 (MT2A) were induced by GSK343. MTs belong a group of cysteine-rich and low-molecular weight intracellular proteins. Due to their rich thiol content, MTs bind many trace metals such as zinc, cadmium, mercury, platinum and silver, thus protecting cells and tissues against heavy metal toxicity [36]. MT1 and MT2 (MT2A), two major isoforms of MT, are found in all types of tissues. Two other members, MT3 and MT4, are expressed in specific tissues such as brain [36]. Human MT genes are located on chromosome 16 q13 in a cluster, in which the MT2A, MT3 and MT4 proteins are encoded by a single gene, and the MT1 protein comprises many subtypes encoded by a set of MT1 genes. The known functional MT1 subtypes are MT1A, MT1B, MT1E, MT1F, MT1G, MT1H, MT1M, and MT1X, which are believed to play distinct roles, depending on their tissue-specific expression pattern [37]. The mRNA expression of all MT isoforms was examined by real-time PCR (Figure 3C). MT1 subtypes, except for MT1A and MT1F, were induced by GSK343. In addition, MT2A, but not MT3 and MT4, was also induced (Figure 3C). In addition, the induction of MT1/MT2A protein expression was also validated by western blot analysis (Figure 3D).

Figure 3. Effect of GSK343 on the cell viability and MT gene expression A. The chemical structure of GSK343. B. HepG2 cells were treated with 0-20 μM GSK343 for 72 h. Cell viability was examined by MTT assay. C. HepG2 cells were treated with 10 μM GSK343 for 6 h. The mRNA expression of MT1 subtypes, MT2A, MT3 and MT4 was analyzed by real-time PCR. D. HepG2 cells were treated with 10 μM GSK343 for 24 h. The protein expression of MT1/MT2A was analyzed by western blot analysis. E. HepG2 cells were treated with 10 μM GSK343 with or without 5μM TPEN for 24 h. The protein expression of MT1/MT2A was analyzed by western blot analysis. F. HepG2 cells were treated with 0-20 μM GSK343 with or without 5μM TPEN for 72 h. Cell viability was examined by MTT assay.

MT expression can be induced by various types of factors, such as heavy metals (e.g. zinc, cadmium, nickel, copper, silver, and cobalt), glucocorticoids, alkylating agents, oxidizing agents, and inflammatory signals [38]. Our results showed for the first time that EZH2 inhibitors can induce MT expression. The induction of MT genes can be regulated by the metal-regulatory transcription factor MTF-1 through binding to the metal regulatory elements (MREs) on MT gene promoters [38]. Because the elevated zinc ion is required for the MRE-binding activity of MTF-1 [39], whether zinc ion is responsible for the GSK343-induced MT expression was investigated by using an zinc chelator, TPEN. As shown in Figure 3E, TPEN abolished GSK343-induced MT1/MT2A protein expression. In addition, TPEN rescued HepG2 cells from GSK343-induced cytotoxicity (Figure 3F). These results suggested that GSK343 may induced MT expression in a zinc-dependent manner, and the induction of MTs was associated with the anticancer activity of GSK343.

Recently, MT is thought to play a role in the pathogenesis of HCC [40]. Several MT1 subtypes, including MT1F, MT1G, MT1H, and MT1M, have been identified as tumor suppressor genes in a variety of cancers including HCC [41-46]. The expression of MT1G and MT1M is frequently silenced in HCC due to promoter hypermethylation [42, 43]. In contrast, 5-azacytidine, a DNA demethylating agent, can activate MTF-1 and induce MT expression [47, 48]. Because EZH2 can directly control DNA methylation through the recruitment of DNA methyltransferase (DNMT) to certain PRC2 target gene promoters [49], inhibition of EZH2 may activate MTF-1- dependent MT induction.

Negative association of EZH2 and MT genes in cell lines and cancer tissues

To investigate the correlation of MTs and EZH2, the related expression levels of EZH2 and MT genes from 26 selected NCI-60 cell lines (average transcript intensity Z scores of EZH2 > 0.1) were retrieved from the CellMiner database (http://discover.nci.nih.gov/cellminer/) [50, 51]. Heat map and Pearson's correlation between EZH2 and MT genes showed the negative correlation between EZH2 and MT1 subtypes except for MT1M (Figure 4A). In addition, MT2A, but not MT3 and MT4, is also negatively correlated with EZH2 expression (Figure 4A), which was consistent with the results that GSK343 only induced mRNA expression of MT1 subtypes and MT2A (Figure 3C). To investigate the clinical relevance of EZH2 and MTs in cancers, we perform a pan-cancer analysis for the expressions of EZH2 and MT genes in normal and cancerous tissues by using a complete collection of human cancer microarray data (Oncomine Database, http://www.oncomine.org/) [26]. As shown in Figure 4B, EZH2 was frequently overexpressed in various cancer datasets (68 out of 449 analyses). Oppositely, MT1/MT2A, but not MT3/MT4, genes were frequently downregulated in cancer datasets, especially for MT1E (48 out of 419 analyses), MT1F (51 out of 441 analyses), MT1G (65 out of 449 analyses), MT1H (54 out of 448 analyses), MT1M (67 out of 354 analyses), MT1X (62 out of 434 analyses), and MT2A (39 out of 416 analyses). Therefore, these results suggest the negative association of EZH2 and MT1/MT2A in cancers including HCC. To further confirm the relationship between EZH2 and MTs in HCC, the expressions of MT genes were correlated with the level of EZH2 in three microarray datasets having both HCC and normal liver tissues [27-29]. Consistently, most MT1/MT2A genes were downregulated in two of these datasets, which was associated with higher expression of EZH2 (Table 3).

Figure 4. The correlation of EZH2 and MT genes in cell lines and cancer tissues A. Left part: Heat map shows the relative expression level of EZH2, MT1 genes, MT2A, MT3 and MT4 from 26 selected NCI-60 cell lines. Red square: increased expression; green square: decreased expression. Right part: Pearson's correlation between EZH2 and MT genes. Coefficient > 0: positive correlation. Coefficient < 0: negative correlation. Coefficient near 0 indicates no correlation between two genes. B. Summary view of EZH2 and MT gene expression profiles in human tumors using published human oncology microarray data (Oncomine, http://www.oncomine.org/). The number in each cell under “Cancer vs. Normal” corresponds to the amount of cancer types that contains a significantly different level of genes compared to normal corresponding tissue. Thresholds for significance are: fold expression > 2; p-value < 0.05 and ranking of gene in the analyses > top 10%. Red signifies the gene overexpression in the analyses; blue represents the gene underexpression. Intensity of color signifies the best rank of gene in those analyses.

Materials

MT1/MT2A and GAPDH antibodies were purchased form GeneTex (Hsinchu, Taiwan). Horseradish peroxidase-labeled goat anti-rabbit and anti-mouse secondary antibodies were purchased from Jackson ImmunoResearch (West Grove, PA, USA). DMEM medium, L-glutamine, sodium pyruvate, and Antibiotic- Antimycotic (penicillin G, streptomycin and amphotericin B) were purchased from Life Technologies (Gaithersburg, MD, USA). Fetal bovine serum (FBS) was purchased from GIBCO (Grand Island, NY, USA). GSK343 was purchased from Biovision (Mountain View, CA, USA). N,N,N',N'-tetrakis(2-pyridinylmethyl)-1,2-ethanediamine (TPEN) was purchased from Cayman Chemical (Ann Arbor, MI, USA). 3-(4,5-dimethylthiazol-2-yl)-2,5- diphenyl tetrazolium bromide (MTT) and dimethyl sulfoxide (DMSO) were purchased from Sigma (St. Louis, MO, USA). Protease and phosphatase inhibitor cocktails were purchased from Roche (Indianapolis, IN, USA). Other chemicals or reagents not specified were purchased from OneStar Biotechnology (New Taipei City, Taiwan).

Cell culture

HepG2 cells were purchased from the Bioresources Collection and Research Center (BCRC), Food Industry Research and Development Institute (Hsinchu, Taiwan). Cells were cultured in DMEM medium supplemented with 10% FBS, 1 mM sodium pyruvate, 1% L-glutamine, 1% Antibiotic-Antimycotic Solution, and incubated at 37°C in a humidified incubator containing 5% CO2.

Cell viability assay

Cell viability was measured with an MTT assay. Cells were plated in 96-well plates and treated with drugs. After 72 h of incubation, 0.5 mg/mL of MTT was added to each well for an additional 4 h. The blue MTT formazan precipitate was then dissolved in 200 μL of DMSO. The absorbance at 550 nm was measured on a multiwell plate reader.

Western blot analysis

Cells were lysed in an ice-cold buffer containing 50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM MgCl₂, 2 mM EDTA, 1% NP-40, 10% glycerol, 1 mM DTT, 1x protease inhibitor cocktail and 1x phosphatase inhibitor cocktail at 4°C for 30 min. Cell lysates were separated on a sodium dodecylsulfate (SDS)-polyacrylamide gel, and then transferred electrophoretically onto the Hybond-C Extra nitrocellulose membrane (GE Healthcare, Piscataway, NJ, USA). The membrane was pre-hybridized in 20 mM Tris-HCl (pH 7.5), 150 mM NaCl, 0.05% Tween-20 (TBST buffer), and 5% skim milk for 1 h, and then transferred to a solution containing 1% bovine serum albumin (BSA)/TBST and a primary antibody and incubated overnight at 4°C. After washing with the TBST buffer, the membrane was submerged in 1% BSA/TBST containing a horseradish peroxidase-conjugated secondary antibody for 1 h. The membrane was washed with TBST buffer, and then developed with an enhanced chemiluminescence (ECL) system (Perkin-Elmer, Boston, MA, USA) and exposed to x-ray film (Roche, Indianapolis, IN, USA).

Microarray analysis and real-time PCR

Total RNA was extracted from HepG2 cells that were treated with 10 μM GSK343 for 6 h. The mRNA profiles were analyzed using Human OneArray Plus (Phalanx Biotech, Hsinchu, Taiwan). Target genes were validated by real-time PCR. Total RNA was extracted by using TRIZOL reagent (Invitrogen, Carlsbad, CA, USA). The quantity of RNA samples was determined using NanoDrop ND-1000 (Thermo Scientific, Wilmington, DE, USA). RNA samples were reverse-transcribed for 120 min at 37°C with High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). Quantitative PCR was performed by the condition: 10 min at 95°C, and 40 cycles of 15 sec at 95°C, 1 min at 60°C using 2X Power SYBR Green PCR Master Mix (Applied Biosystems) and 200 nM of forward and reverse primers. Each assay was run on an Applied Biosystems 7300 Real-Time PCR system in triplicates and expression fold-changes were derived using the comparative CT method, GAPDH gene as endogenous control and untreated control sample as calibrator. The sequences of the primers used in real-time PCR were 5′-CCCATGAGCTGTGCCAAGT-3′ and 5′-TTCCAAGTTTGTGCAGGTCACT-3′ for MT1A, 5′-GCCCAGGGCTGCATCTG-3′ and 5′-TTCCAAGTTTGTGCAGGTCACT-3′ for MT1B, 5′-GGCTCCATTCTGCTTTCCAA-3′ and 5′-AGTGGCGCAAGAGCAGTTG-3′ for MT1E, 5′-AGCGGCCGGCTGTTG-3′ and 5′-AGAGACTGGACTTTCCAAGAGAGAAG-3′ for MT1F, 5′-CCTGTGCCGCTGGTGTCT-3′ and 5′-TGCAGCCTTGGGCACACT-3′ for MT1G, 5′-GGGCTGCATCTGCAAAGG-3′ and 5′-TTACGTGTCATTCTGTTTTCATCTGA-3′ for MT1H, 5′-CGCTCCATTTATCGCTTGAGA-3′ and 5′-TGCAGGCGCAGGAGACA-3′ for MT1M, 5′-GCTGCGTGTTTTCCTCTTGAT-3′ and 5′-GAGCAGCAGCTCTTCTTGCA-3′ for MT1X, 5′-GCCCAGGGCTGCATCTG-3′ and 5′-TTTGTGGAAGTCGCGTTCTTT-3′ for MT2A, 5′-CTGCCCCTGCCCTTCTG-3′ and 5′-ACACAGTCCTTGGCACACTTCTC-3′ for MT3, and 5′-GCAACTGTAAAACATGTCGGAAGA-3′ and 5′-AGCCTCCTTTGCAGATGCA-3′.

Statistical analysis

Means and standard deviations of samples were calculated from the numerical data generated in this study. Data were analyzed using Student's t test. p values < 0.05 (*) were considered significant.