Six-transmembrane epithelial antigen of the prostate 1 accelerates cell proliferation by targeting c-Myc in liver cancer cells
- Authors:
- Published online on: May 24, 2021 https://doi.org/10.3892/ol.2021.12807
- Article Number: 546
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Copyright: © Iijima et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Primary liver cancer is estimated to be the third leading cause of cancer-related deaths worldwide, accounting for 830,000 deaths each year (1). Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer comprising 75–85% of cases (1). Despite recent advances in multikinase inhibitors, such as sorafenib, regorafenib, and lenvatinib, as well as anti-vascular endothelial growth factor therapies and immune check point inhibitors, advanced HCC has a dismal prognosis (2–5). An exploration of the molecular characteristics of liver cancer is needed to develop more effective therapeutics.
A well characterized oncoprotein, c-Myc contributes to the pathogenesis of a broad range of human cancers, including liver cancer (6). Overexpression of c-Myc is associated with a poor prognosis (7). The amplification of c-Myc and alterations of proximal c-Myc network members have been identified in over 30% and 70% of HCC cases, respectively (8). Such findings highlight c-Myc as an attractive target for liver cancer therapeutics. However, its structure, which lacks a druggable hydrophobic pocket, and its nuclear localization have hampered the development of specific inhibitors of c-Myc (9). Certainly, ongoing clinical trials of c-Myc inhibitors are non-existent, except for a trial involving 90-amino acid peptide as a dominant negative inhibitor (10). Specifically, it is critical to seek druggable targets in the c-Myc pathway to combat c-Myc-driven liver cancer.
Six-transmembrane epithelial antigen of the prostate 1 (STEAP1), which was initially identified in prostate cancer cells and is expressed at low levels in normal cells, is a cell surface protein (11) that is over-expressed in many human cancers (12). STEAP1 has thus emerged as an ideal target in cancer therapeutics. STEAP1 is believed to play a physiological role as an ion channel and transporter (13). Additionally, structural analyses using a cryo-electron microscopy revealed that STEAP1 works as a ferric reductase when binding to the NADPH-binding domain of STEAP4 (14). In contrast, the pathological functions of STEAP1 in cancer cells have been largely unexplored. We recently discovered that high expression of STEAP1 lead to the suppression of reactive oxygen species (ROS) that escaped from apoptosis via a NF-E2-related factor 2 (NRF2) pathway in colorectal cancer cells (15). However, the roles of STEAP1 in liver cancer pathogenesis remain completely unknown.
Here, we sought to characterize the biological roles of STEAP1 in liver cancer. We identified that STEAP1 transcript levels were significantly increased in liver cancer compared to normal liver cells, and that such high levels were associated with a poor prognosis. The knockdown of STEAP1 led to cell-growth inhibition accompanied by G1 arrest by targeting the suppression of c-Myc, which was discovered by mining publicly available databases. Our findings yield a new treatment strategy targeting the STEAP1-c-Myc axis in liver cancer.
Materials and methods
Databases and gene expression data analysis
Gene expression levels of STEAP1 in non-tumor and liver cancer tissues were evaluated using gene expression profiles of GSE14520 and GSE36376 from the Gene Expression Omnibus, a public and freely available database. The GSE14520 dataset includes 488 samples of 241 non-cancerous and 247 cancerous hepatic tissues. These datasets have been widely used and well accepted in bioinformatics analysis of liver cancer. The GSE36376 dataset includes 433 samples consisting of 193 non-cancerous hepatic tissues and 240 cancerous tissues. The correlation between STEAP1 levels and clinical outcomes of patients with liver cancer was investigated using GSE14520 and the Cancer Genome Atlas Program (TCGA) (16). We used a receiver operating characteristic curve to determine the cutoff value. In total, 247 patients from the GSE14520 dataset and 360 patients from TCGA, all with liver cancer, were divided into two groups having high or low levels of STEAP1, respectively. Kaplan-Meier analyses of survival were performed based on these groups. Statistical analyses were performed using EZR software version 1.33 (17).
Gene set enrichment analysis (GSEA) was performed using the open source software, GSEA 4.0.3. Initially, we set two groups (STEAP1_high and STEAP1_low) in GSE14520-GPL3921, which includes 225 liver cancer samples in total. We conducted GSEA of the two groups using Hallmark gene sets. Gene sets showing a NOM P-val. (P-value) <0.05 and false discovery rate (FDR) Q-val. (FDR) <0.25 were considered significant. Differentially expressed genes (DEGs) between these two groups were identified using an online tool, GEO2R, with |logFC| >1.5 and an adjusted P-value <0.05.
Cell lines and culture conditions
HepG2 and Hep3B cell lines were purchased from the American Type Culture Collection; these were authenticated by short tandem repeat DNA profiling prior to all experiments. Both cell lines were cultured in DMEM containing 10% fetal bovine serum (FBS), 2 µM L-glutamine and 1% penicillin-streptomycin (the medium and all supplements from Sigma-Aldrich; Merck KGaA).
Inhibition of STEAP1 expression by small-interfering RNA
Control small-interfering RNA (siRNA; Control; #4390843; Thermo Fisher Scientific, Inc.) and two independent siRNAs targeting human STEAP1 (siSTEAP1; D-003713-01: 5′-GGAGAGAAUUUCACUAUAU-3′ and D-003713-02: 5′-UAAAGAAGAUGCCUGGAUU-3′; Dharmacon) were transfected using Lipofectamine RNAiMAX (Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol. Cells were seeded at a density of 3×105 cells/well into 6-well plates and cultured for 24 h at 37°C. Subsequently, cells were transfected with control siRNA or siRNA targeting human STEAP1, and incubated for 72 h at 37°C. Final siRNA used per well was 25 pmol. After incubation, floating cells in media were collected, adhesive cells were washed and collected, and both were immediately used for experiments.
Reverse transcription-quantitative PCR (RT-qPCR)
Total RNA was extracted using TRIzol Reagent (Thermo Fisher Scientific) according to the manufacturer's protocol. Subsequently, complementary (c)DNA was synthesized from the RNA using a SuperScript VILO cDNA synthesis kit (Thermo Fisher Scientific). qPCR was performed with an Applied Biosystems 7300 Real-time PCR system (Applied Biosystems; Thermo Fisher Scientific, Inc.). The analysis of target genes (STEAP1 and c-Myc) was conducted in quadruplicate using a POWER SYBR-Green Master Mix (Thermo Fisher Scientific, Inc.) as previously described (18). The thermal profile of the qPCR program consisted of 2 min at 50°C, 10 min at 95°C, 40 cycles of 15 sec at 95°C and 1 min at 60°C, and a dissociation stage at the end of the run from 60°C to 95°C. Transcript levels were normalized to β-actin expression and analyzed using the 2−ΔΔCq method. The following PCR primers were designed: 5′-CCCTTCTACTGGGCACAATACA-3′ and 5′-GCATGGCAGGAATAGTATGCTTT-3′ for STEAP1; 5′-TTTTTCGGGTAGTGGAAAACC-3′ and 5′-GCAGTAGAAATACGGCTGCAC-3′ for c-Myc; and 5′-GGCATCCTCACCCTGAAGTA-3′ and 5′-GAAGGTGTGGTGCCAGATTT-3′ for β-actin.
Western blotting
As previously described (19), cells were solubilized in radioimmunoprecipitation assay lysis buffer (50 mM Tris-HCl, pH 7.5, 1% NP-40, 0.5% Na-deoxycholate, 1 mM EDTA, 150 mM NaCl, 1 mM EGTA, and protease inhibitor cocktail; Sigma-Aldrich; Merck KGaA), and centrifuged at 12,000 × g for 10 min. The supernatants were collected, and protein concentrations were determined using a bicinchoninic acid Protein Assay Kit (Thermo Fisher Scientific, Inc.). Equal amounts of protein were separated on MULTIGEL II mini gels (Cosmo Bio Co., Ltd.) and transferred to polyvinylidene fluoride membranes using a QBlot Kit (ATTO, Tokyo, Japan). The blots were probed using the following primary antibodies: anti-STEAP1 (sc25514; Santa Cruz Biotechnology), anti-STEAP1 (#88677; Cell Signaling Technology), anti-cyclin D1 (#2987; Cell Signaling Technology,), anti-p27 Kip1 (#3686; Cell Signaling Technology), anti-c-Myc (OP10L; EMD Biosciences), and anti-actin-horse radish peroxidase (HRP; sc-47778; Santa Cruz Biotechnology).
Evaluation of cell proliferation
Hepatocellular carcinoma cells were seeded at a density of 2×103 cells/well into 96-well plates. Control siRNA or two independent siRNAs targeting human STEAP1 were transfected 24 h after seeding. Cell viability was assessed at 0, 24, 48 and 72 h using a WST-1 assay (Premix WST-a Cell Proliferation Assay; Takara Bio) and Infinite M1000 Pro microplate reader (Tecan Japan). A growth curve was constructed by plotting absorbance against time.
Cell cycle analysis
Liver cancer cells were seeded at a density of 3×105 cells/well into 6-well plates and cultured for 24 h. Subsequently, cells were transfected with control siRNA or an siRNA targeting human STEAP1, and incubated for 72 h. After incubation, floating cells in media were collected and adhesive cells were washed, fixed in ethanol, and stained with propidium iodide using a cell-cycle analysis kit (FxCycle PI/RNase Staining Solution; Thermo Fisher Scientific), followed by analysis on a BD FACS II (BD Biosciences) instrument using FACSDiva (BD Biosciences) as previously described (20).
Apoptosis assay
Apoptosis was evaluated using an Annexin V/7-amino-actinomycin (AAD) staining kit (BD Biosciences). Liver cancer cells were seeded at a density of 3×105 cells/well into 6-well plates and cultured for 24 h. Subsequently, cells were transfected with control siRNA or an siRNA targeting human STEAP1, and incubated for 72 h. After incubation, floating cells in media were collected and adhesive cells were washed, stained with Annexin V and 7-AAD, and analyzed on a BD FACSCanto II (BD Biosciences) instrument using FACSDiva (BD Biosciences) as previously described (21).
PCR array
Total RNA was reverse-transcribed using an RT2 First Strand Kit (Qiagen). PCR array was performed using RT2 Profiler™ PCR Array Human MYC Targets (PAHS-177Z; Qiagen) according to the manufacturer's protocol.
Statistical analysis
The significance of differences was determined by Student's t-test, Mann-Whitney U test, log-rank test or one-way ANOVA followed by Bonferroni's post-hoc test, as appropriate. Pearsons correlation was used to perform the correlation analysis. All statistical analyses were performed using EZR software version 1.33 (17). Statistical significance was defined as P<0.05.
Results
STEAP1 is up-regulated and significantly associated with poor overall survival and recurrence-free survival in liver cancer
We first investigated the expression of STEAP1 in patients with liver cancer using publicly accessible datasets (GSE14250 and GSE36376) from the Gene Expression Omnibus. In both datasets, STEAP1 is over-expressed in liver cancer tissues compared to non-cancerous hepatic tissues (Fig. 1A and B). Next, we evaluated the correlation between STEAP1 expression and survival in patients with liver cancer using GSE14520 and TCGA datasets. Patients with high STEAP1 expression presented with significantly shorter overall survival (OS) and recurrence-free survival (RFS) in GSE14520 and significantly shorter OS in TCGA (Fig. 1C-E). These data imply that STEAP1 may have oncogenic functions in liver cancer.
Knockdown of STEAP1 inhibits proliferation of liver cancer cell lines
To evaluate the effect of STEAP1 on liver cancer, we performed STEAP1 silencing using an RNA interference method in two different liver cancer cell lines, HepG2 and Hep3B. Knockdown efficiency was examined by RT-qPCR and western blot. STEAP1 expression in these cell lines was significantly down-regulated 72 h after transfection of two independent siRNAs (Fig. 2A, B, D and E). We next evaluated the impact of STEAP1 silencing on liver cancer cell lines using WST-1 assays. STEAP1 silencing significantly reduced proliferation in both cell lines (Fig. 2C and F). Based on these data, we concluded that STEAP1 activated proliferation in liver cancer cell lines.
STEAP1 silencing promotes G1 arrest in liver cancer cell lines
To evaluate the mechanism of decreasing proliferation in response to the knockdown of STEAP1, we examined the effects of STEAP1 silencing on the cell cycle in the liver cancer cell lines, HepG2 and Hep3B. STEAP1 silencing significantly induced G1 arrest in both liver cancer cell lines (Fig. 3A and B). We also performed a flow cytometry analysis using Annexin V/7AAD staining to evaluate the rate of apoptosis. However, an increased percentage of apoptosis was not observed in STEAP1-silenced liver cancer cell lines (Fig. S1A and B). To analyze the mechanism of G1 arrest in HCC cell lines induced by the knockdown of STEAP1, we evaluated protein levels of several cell-cycle-related proteins in liver cancer cell lines using western blot. The expression of the G1 arrest-associated protein, cyclin D1, was decreased, whereas the expression of p27, which promotes cell-cycle arrest, was apparently increased (Fig. 3C).
c-Myc target genes were significantly enriched in patients with liver cancer showing high STEAP1 expression
To clarify the pathways related to STEAP1, we first extracted DEGs between low and high STEAP1 liver cancer samples in a publicly accessible dataset, GSE14520-GPL3921, using GEO2R. The significant DEGs with |logFC| >1.5 and adjusted P-value < 0.05 are highlighted in red and blue colors. Each gene was represented as a volcano plot (Fig. 4A) and listed in a table (Table I). Next, we conducted GSEA to explore the gene sets regulated by STEAP1 in liver cancer and found five pathways which were significantly enriched (NOM P-val <0.05 and FDR Q-val <0.25; Fig. 4B, Fig. S2, and Table SI). The genes belonging to MYC_TARGET_V2 were the most significantly enriched among these five pathways (Fig. 4C and D). Based on these findings, we hypothesized the existence of a relationship between STEAP1 and c-Myc in liver cancer. To confirm this, we evaluated their expression using the publicly accessible datasets, GSE14250, GSE36376, and TCGA. Pearson's correlation coefficient analysis revealed a significant positive relationship between STEAP1 and c-Myc in all datasets (Fig. 4E-G).
 | Table I.List of significant DEGs in samples with high and low STEAP1 expression in publicly accessible gene expression profiling dataset, GSE14520-GPL3921. |
STEAP1 regulates c-Myc and its related genes in liver cancer cell lines
To confirm the relationship between STEAP1 and c-Myc in liver cancer, we evaluated the expression of c-Myc after STEAP1 knockdown in HepG2 and Hep3B cell lines by RT-qPCR and western blot. As we expected, downregulation of c-Myc was observed in both cell lines when transfected with siRNA targeting STEAP1 compared to non-targeting siRNA (Fig. 5A-D). Next, we conducted a PCR array to analyze components of c-Myc-related genes; most were significantly downregulated by STEAP1 silencing (Figs. 5E and S3). Taken together, our data suggest that c-Myc lies downstream of STEAP1, and that the STEAP1-c-Myc pathway promotes cell proliferation and cell-cycle progression in liver cancer.
Discussion
Recently, treatment options for HCC have been expanding as new drugs are approved (2–5). However, unresectable HCC is an incurable disease; its median overall survival remains around a year (22). Thus, the further exploration of novel molecularly-based therapies is required to improve survival in patients with advanced HCC. c-Myc is a high priority target of liver cancer therapeutics because its pathological functions exist in a subset of liver cancer cases. The structure of c-Myc has hampered the development of c-Myc-specific inhibitors and highlights the need for further investigations of novel c-Myc signaling components as potential targets for liver cancer therapeutics. The current study elucidated STEAP1 as a member of the c-Myc signal transduction pathway using in vitro and bioinformatic analyses. Inhibition of STEAP1 led to the suppression of cell growth accompanied by G1 arrest in liver cancer, encouraging the development of STEAP1 inhibitors as therapeutics for STEAP1-c-Myc axis-driven liver cancer. Additionally, STEAP1 is an attractive target for antibody drug conjugates (ADC) in cancers because it is expressed on the plasma membrane (11). In fact, DSTP3086S, an ADC-targeting STEAP1, has been introduced for patients with metastatic castration-resistant prostate cancer; it has been evaluated as safe and shows promising therapeutics (23). Therefore, an ADC-targeting STEAP1 can be used for patients with liver cancer, who, according to our data, show the overexpression of STEAP1 in cancerous hepatic tissue compared to adjacent non-cancerous parts (Fig. 1A and B).
In our previous work, we demonstrated that STEAP1 knockdown led to apoptosis in colorectal cancer cells in an NRF2-dependent fashion, corresponding to the increased production of ROS (15). As shown in Fig. S4, intracellular ROS levels were increased by STEAP1 inhibition as found in our previous work (Fig. S4A and B). Furthermore, GSEA revealed an ROS-related pathway was significantly enriched in patients with liver cancer showing upregulated STEAP1 (Fig. S2D). However, as mentioned above, apoptotic cells were not increased by STEAP1 inhibition in liver cancer cells (Fig. S1A and B). In addition, we found no statistical correlation between STEAP1 and NRF2 in three individual datasets (GSE14520, GSE36376 and TCGA; Fig. S4C-E). Furthermore, previous studies reported that c-Myc generates ROS in liver cancer cells (24,25). However, the current study demonstrated that STEAP1 leads the increased expression of c-Myc and reduced ROS production in liver cancer cells. These results seem inconsistent, suggesting the existence of an NRF2 or c-Myc independent ROS-related pathway in the regulation of STEAP1-mediated cell growth. Additionally, others have shown STEAP1 silencing induced cell growth inhibition, which was associated with decreased levels of ROS in cases of Ewing sarcoma (26). These results suggest the existence of multiple pathways between STEAP1 and ROS in a cancer-type specific manner. Accordingly, our next steps include exploring the relationship between STEAP1 and ROS in STEAP1-driven cancer cells.
In summary, this study provides a preclinical concept for STEAP1 as a druggable target in liver cancer, an often fatal cancer. The STEAP1-c-Myc axis has potential as an attractive and promising therapeutic target in liver cancer, and its manipulation will lead to the development of a novel strategy to conquer this malignant disease.
Supplementary Material
Supporting Data
Acknowledgements
The authors would like to thank Ms. Kei Yoneguchi (Department of Medical Oncology, Sapporo Medical University School of Medicine, Sapporo, Hokkaido, Japan) for her technical assistance.
Funding
The present study was funded by a grant from Japan Society for the Promotion of Science (grant no. 19K08397)
Availability of data and materials
The datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus repository, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE173813.
Authors' contributions
KT, HN and KI were responsible for the conception and design of the study and for confirming the authenticity of the data. NH, TK, YU, SI, KM, MK and JK performed the analysis and interpretation of data. HN and KT drafted the manuscript. JK critically reviewed and revised the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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