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Gene expression profiling regulated by Tamoxifen in human hepatocellular carcinoma cells

Dr.You-Fu Pan12, Dr. DHong-Yang Wang122, Dr. Jin-Tao Pan3,Dr. Xue-Ying Li1

1Department of Medical Genetics, Zunyi Medical University, Zunyi Guizhou 563003, China.
2Key Laboratory of Gene Detection and Treatment in Guizhou Province, Zunyi Guizhou 563000, China.
3Temasek Junior College, 22 Bedok South Road, Singapore 469278, Singapore.

*Corresponding author

Dr.You-Fu Pan, Department of Medical Genetics, Zunyi Medical University, Zunyi Guizhou 563003, China.

Abstract

Liver cancer is one of the deadliest cancers in the world. Human hepatocellular carcinoma (HCC) is responsible for the majority of liver cancers. The HCC morbidity rate shows clear gender differences with an average incidence ratio of 2 to 7:1 in males compared to females. It is believed that the liver is a hormone-sensitive organ and the imbalance of liver hormones can be one of the causes of liver cancer. Tamoxifen (Tam) has been used to treat HCC for a number of years, but there are conflicting results and therefore whether it can be used as a protective agent is debated. To examine the effect of Tam on HCC, we performed bioinformatic analysis using online databases and gene expression profile analysis in SMMC-7721 cells treated with tamoxifen (Tam) for 6h and 24h. Our analysis showed that the expression level of ESR1 is associated with HCC, suggesting that ER may play a tumor suppressive role in HCC patients in general. RNA-seq data indicate that significantly differentially expressed genes (DEGs) were enriched in steroid biosynthesis, terpenoid backbone biosynthesis, among others. In addition, C-Jun/c-Fos were enriched as important protein-protein interaction network nodes. Our data uncovered novel Tam actions in the genomic landscape and provided insights into the side effects of Tam drugs.

Keywords hepatocellular carcinoma; estrogen receptor alpha; tamoxifen；RNA-seq; c-Fos.

Introduction

Liver cancer is the third deadliest cancer in the world and it was estimated that 830,180 people died from the disease worldwide in 2020 [1]. n addition, it is the most common type of cancer in men under the age of 60 and has the highest mortality rate in this sex group[2]. The highest incidence of liver cancer is in Asia and Africa, where more than half of all liver cancer cases worldwide occur[3, 4].

There are four types of liver cancer, namely cholangiocarcinoma, mixed liver cancer, fibrous lamellar hepatocellular carcinoma and hepatocellular carcinoma (HCC). About 75-90% of liver cancer cases are HCC [5, 6], with the morbidity and mortality rates being much higher in males than in females, with an average ratio of 2-7:1[6].

It has been known since 1952 that estrogen plays an important role in liver morphogenesis and function[7]. The liver is also the main site of sex hormone metabolism, and an imbalance in liver hormones can be one of the causes of liver cancer[8, 9]. Studies have shown that some genes in the mouse liver are regulated by estrogen and may be involved in controlling liver cell proliferation [10]. It may also play a role in liver energy homeostasis and hematopoietic stem cell self-renewal [11] [12]. Therefore, the liver is believed to be a hormone-sensitive organ [13, 14]. However, it has long been debated whether estrogen is a risk factor for HCC and whether ERs play a protective role for the liver and whether thereafter the use of estrogen antagonists is beneficial for HCC patients [15].

Estrogens are pleiotropic hormones. They can function in many physiological processes including normal growth and development, regulation of transcription in tissues of the reproductive duct, central nervous system, cardiovascular system and bone. Estrogens also play a crucial role in the development of several cancers including breast, endometrial, ovarian, colon, etc. [6, 16, 17].

There are two different forms of ERs, commonly known as ER alpha (ESR1/ER) and ER beta (ESR2/ER), encoded by the ESR1 and ESR2 genes, respectively. Both ER isoforms show a similar structural organization and both consist of five domains that show high homology especially in the DNA and ligand binding domains.

Selective estrogen receptor modulators (SERMS) such as tamoxifen (Tam) are a class of nonsteroidal compounds. SERMs can act as agonists or antagonists depending on the target tissue. Both estrogens (mainly 17 beta-estradiol, E2) and SERMS (such as Tam) exert genomic and non-genomic effects via estrogen receptors. Tamoxifen (Tam) has long been used as a classic estrogen receptor antagonist (selective ER modulator). It is the first-line hormone treatment for hormone-dependent breast cancer [7-9]. However, their use in other types of cancer such as liver cancer has been less explored [10]. Some studies showed that Tam treatment could effectively reduce the development of liver cancer or significantly improve patient survival. Some others could not observe the same results.

When estrogen or Tam binds to ERs, ERs dimerize and then bind to ER binding sites in the genome, either directly (to estrogen responsive elements, ERE) or tethered to some DNA-binding co-transcription factors. It then activates or inhibits the expression of target genes and triggers a range of biological effects [18].

To understand the action of tamoxifen and the functional role of ESR1 in HCC cells, we focused on gene expression profiling of HCC cells treated with Tam. Our genomic profiling will provide clinical implications of Tam for HCC patients.

Materials and methods

2.1.  Cell culture Human hepatoma cells (SMMC-7721) (Chinese Academy of Sciences Cell Bank) were cultured in complete RPMI-1640 (Hyclone, Logan, UT, USA) supplemented with 10% fetal bovine serum (FBS) (Biological Industries, Israel) and 1× Penicillin-streptomycin (Solarbio, Beijing, China). SMMC-7721 cells were maintained at 37°C in a 5% CO 2 humidified incubator. Two days prior to drug treatment, cells were maintained in phenol red-free RPMI 1640 medium containing 5% charcoal-stripped fetal bovine serum, 1% penicillin-streptomycin.

2.2.  RNA extraction and quantitative real-time PCR The SMMC-7721 cells were grown to almost 80% confluency, the cells were treated with Tam and continuously cultured for 6h or 24h. Total RNA was extracted with RNAiso™ Plus Trizol (Takara Holdings Inc., Japan), followed by treatment with chloroform and ethanol precipitation. cDNA was synthesized using the PrimeScriptRT (Perfect Real Time) reagent kit according to the manufacturer's instructions (Takara Holdings Inc., Japan). Quantitative real-time PCR (qRT-PCR) was performed using SYBRPremix Ex TaqII (Tli RNaseH Plus) (Takara Holdings Inc., Japan) on a CFX 96 instrument (Bio-Rad, USA) with the following parameters: 98° C for 30s, 39 cycles of 98°C for 5s and 57°C for 50s. Finally, dissociation curves were run (95°C for 10s, then 65°C for 5s and 95°C for 5s) to identify specific products. Each experiment was repeated at least three times.The transcription levels of 8 DEGs after Tam treatment were detected by qRT-PCR to check if they matched the pattern in the gene expression profile. Finally, eight genes (ABCG1, ERBB3, FOS, GATA4, HDAC9, IDI, JUN, and MTIF) were selected for qRT-PCR validation. The primer sequences used in this study are listed in Table 1.

2.3.   RNA-Seq and analysis The cDNA library was generated and sequenced using the BGISEQ-500 platform. In brief, mRNA molecules were purified using poly-T-oligo-attached magnetic beads, and fragmented into small pieces using divalent cations at elevated temperature. The cleaved RNA fragments were used to synthesize the first strand cDNA using reverse transcriptase and random primers, followed by second-strand cDNA synthesis using DNA Polymerase I and RNase H. These cDNA fragments were subjected to 3′ adenylation and ligation to the adapter. The products were then purified and amplified by PCR. The PCR yield was quantified with Qubit and the PCR products were heat denatured and circularized (ssDNA circle). DNA nanospheres (DNBs) were generated with the ssDNA circle by rolling circle replication (RCR) to magnify the fluorescence signals in the sequencing process. The DNBs were loaded into the patterned nanoarrays and 50 bp single-ended reads were transcribed on the BGISEQ-500 platform. RNA-Seq analysis was performed by BGI's in-house pipeline (https://www.genomics.cn/) or Lianchuan platform (https://www.omicstudio.cn/). Basically, the differentially expressed genes were screened with the DEGseq software [19], nd the genes with a fold change >=2 or =<0.5 and P=<0.05 were defined as differentially expressed genes (DEGs) and were used for pathway analysis and gene set enrichment (GSE) analysis. Relative data were submitted to the NCBI (BioProject accession number PRJNA816051, BioSample accession number SAMN26856652). 

2.4.  Western blot assay Cells were collected and lysed with RIPA buffer (Solarbio, Beijing, China) in the presence of protease inhibitor (Roche #11836170001) and phenylmethylsulfonyl fluoride (PMSF) (Solarbio, #P0100). After protein concentration was quantified using the BCAProtein Assay Kit (Solarbio, Beijing, China) according to the manufacturer's instructions, protein samples were denatured and resolved on 10% SDS-Page gels. The PVDF membrane (EpizymeBiotech, China) was activated in 100% methanol and then blocked with 5% non-fat milk in Tris-buffered saline with Tween 20 (TBST) for 2 h. Diluted primary antibody was incubated with the membrane at 4 overnight, followed by washing the membrane three times with TBST. Then the membrane was incubated with diluted second antibody for 2 h at room temperature, followed by washing with TBST and ECL treatment. Protein bands were imaged with a gel imaging system (Gel Doc XR, Bio-Rad, USA) and analyzed with ImageJ software. The following primary antibodies were used: ER antibody (Santa Cruz, sc-543), ER antibody (HUABIO, EM1708-51), beta-actin (Huanan, #EM21002), and horseradish peroxidase-conjugated goat anti-rabbit IgG The experiment was performed in triplicate and the representative blots are shown.

2.5.  Data analysis We used Photoshop 6.0 to crop the images and used GraphPad Prism 6 and SPSS V 16.0 to plot the data. For statistical analysis, the results were all expressed as ±SD. The two-tailed Student’s test was used to check the level of significance. P<0.05 and P<0.01.

2.6.  Online databases The cBioPortal server (Cbioportal: http://www.cbioportal.org) was used for sex disparity and ESR1 mutational analysis. Lianchuan's online platform was used for RNA-seq analysis. The DEGs obtained from the analysis were used to analyze the protein-protein interaction networks (PPI) by String software (v11.5). [20].

Table 1: Primers used in this study.

Table.1

Table 2: TOP 10 nodes of the PPI network.

Table.2
Fig.1

Figure 1: The ESR1 genetic alterations and ESR status correlation with patients’ survival.

A) The gender disparity is observed in HCC patients. The ratio of male to female is 378 : 150 =2.52:1 in this study. Red: male; Green: female. B) ESR1 is altered in 4 of 5 data sets. Green: mutation; red, amplification; Blue: deep deletion; Grey: multiple alterations (cBioPortal). C) The status of estrogen receptor genes (including ESR1 and ESR2) was correlated with disease free survival of HCC patients (P=0.0734. (cBioPortal).

Fig.2

Figure 2: The expression of ERα in SMMC-7721 HCC cells and determination of IC50 of Tam.

ERα is expressed in SMMC-7721 HCC cells. The IC50 of Tam is 38.26μM at 24h, and 26.93 μM at 48h, respectively.

Fig.3

Figure 3: RNA-seq analysis of  SMMC-7721 HCC cells after Tam treatment.

Heatmap of SMMC-7721 HCC cells with Tam treatment. (Column, samples; Row: DEGS; Red: up-regulated; Green: down-regulated).

Fig.4

Figure 4:Validation of selected DEGs.

ABCG1, ERBB3, FOS, GATA4, HDAC9, IDI, JUN and MTIF genes were selected for qRT PCR validation. The expression patterns were similar to those of RNA-seq data.

Fig.5

Figure 5:Pathway analysis of  DEGs after Tam treatment.

A)Significantly enriched pathways with Tam treatment for 6h. B) Significantly enriched pathways with Tam treatment for 24h.

Fig.6

Figure 6: GSE analysis of DEGs after Tam treatment.

A) Acevedo_liver_tumor_vs_normal_adjacent_tissue_dn.  B) Rutella_response_ to _csf2rb_ and _il4_dn.  C) Kan_response_to_arsenic_trioxide. D) Acevedo_ liver_cancer_dn. E)  Meissner_brain_hcp_ with _H3K4me3_and _H3K27me3.   F) Naba_matrisome. G) Senese_HDAC3 _targets_dn.  H) Sweet_lung_cancer_ Kras_dn.

Fig.7

Figure 7: Protein-Protein Interaction Network analysis of DEGs after Tam treatment.

The PPI network, the number of nodes is 472 and the number of edges is 282, while the expected number of edges is 118. PPI enrichment p-value: < 1.0e-16.

Fig.8

Figure 8: The expression heatmap of top node genes after Tam treatment.

Left: gene symble; Up::sample name. For the color key, red:up; green ,down.

Result

3.1 ESRs status was correlated with disease free survival in HCC  The number of HCC cases clearly showed the gender differences. The ratio of men to women in the current study is 378:150 = 2.52:1 (Fig. 1A). Our analysis also showed that ESR1 genetic alterations occurred in most datasets (4 of 5) (Fig. 1B). In addition, the status of estrogen receptor genes (ESR1 & ESR2) significantly correlated with disease free survival of HCC patients (Fig. 1C).

3.2 Determination of IC50 of Tam in SMMC-7721 HCC cells We then treat SMMC-7721 HCC cells with Tam of serial concentrations for 24 h or 48 h. The IC50 of Tam in SMMC-7721 cells is 38.26 M at 24 h and 26.93 M at 48 h, respectively (Fig. 2A-B). We also examined the expression of ER and when it was affected by E2 (10 nM) or Tam (25 M) treatment, the western blot results showed no significant difference (Fig. 2C-D).

3.3 RNA-Seq analysis of SMMC-7721 cells after Tam treatment To obtain a global overview of the Tam-triggered transcriptional program in HCC cells, we generated RNA-seq data from SMMC-7721 cells treated with Tam for 6 and 24h. With our RNA-seq data, as shown in the MA diagram, we identified 158 dysregulated genes after 6h of Tam treatment. Among these genes, 124 were upregulated and 34 were downregulated. The number of dysregulated genes increased to 464 after 24h of Tam treatment, there were 313 upregulated genes and 151 downregulated genes, respectively (Fig. 3A). We combined these two datasets to get a union dataset and used it for further analysis.The heat map generated with the Lianchuan platform clearly showed that multiple clusters could be observed. While some genes were upregulated after 6 h, more genes were gradually activated after 24 h. At the same time, some genes were downregulated (Fig. 3B).Next, we randomly selected 8 genes for validation purposes. To see if Tam is a full antagonist of E2, we also included a group treated with E2 at 10 nM, a conventional concentration used to treat breast cancer cells. For the Tam group, the qPCR results were consistent with the RNA-seq data at both the 6-hour and 24-hour time points. For the E2 group, the expression patterns of some genes were similar to those of the Tam group, such as ABCG1 (6 h and 24 h), GATA4 (24 h only), and Fos (c-Fos) (6 h only). Some genes showed different patterns, such as HDAC9 and MTIF. However, some genes showed dynamic patterns at certain time points, including GATA4, ERBB3, Jun, Fos (Fig. 4).

3.4 Pathway analysis of  DEGs after Tam treatment Significantly enriched pathways in 24-hour Tam treatment include circadian rhythm, rheumatoid arthritis, hepatitis C, cocaine addiction, mineral absorption, IL-17 pathway (Fig. 5B) among others. Next, we performed a GSE analysis. Significantly enriched gene sets include: acevedo_liver_tumor_vs_normal_adjacent_tissue_dn (P=0.0003), rutella_response_to_csf2rb_and_il4_dn (P=0.0003), kan_response_to_arsenic_trioxide (P=0.0004), acevedo_liver_cancer_dn (p=0.0004), meissner_brain_hcp_with_H3K4me3_and_H3K27me3 (P=0. 0001), naba_matrisome (P=0.0002), senese_HDAC3_targets_dn (P=0.0009), sweet_lung_cancer_Kras_dn (P=0.0047) (Fig. 6).

3.5 Protein-Protein Interaction network analysis The network type is full STRING network with the highest confidence (0.90). The number of nodes is 472 (expected number of edges is 118), the p-value of PPI enrichment is < 1.0e-16. Individual nodes were removed when using kmeans=6. The PPI network prediction of the identified DEGs was performed using string software. Of these, 508 proteins were used for this analysis. The obtained PPI network includes 472 nodes and 282 edges with P = 1.0e10-16. See Fig. 7 for details. To simplify the appearance, the graph was constructed with high confidence (0.90) and Kmeans=10. Several color-coded clusters were observed at this setting.Top nodes include EGR1 (red), JUN/FOS (red), ERBB3 /MAP2K6 (yellow), CYP51A1/IDI1 (green), etc. The top 10 nodes are listed in Table 2, including JUN, FOS, HSP90AA, 1CYP51A1, HMGCS1, MSMO1, SQLE, UBC, FDFT1. The heat map showed that most of these genes were upregulated at both the 6-hour and 24-hour time points compared to the control sample (Fig. 8).

DISCUSSION

Clinical use of Tam has shown some positive results, but it is not clear how Tam works in HCC. To date, Tam signaling in liver or liver cancer is unknown, hence there is a desire to understand it at the genomic level in HCC.Our analysis showed that ESR1 genetic alterations were frequently observed in HCC samples and the status of estrogen receptor genes (ESR1 & ESR2) was associated with disease-free survival of HCC patients. There is interesting evidence supporting a protective role for ER in HCC.

In fact, studies have already shown that estrogen can inhibit the occurrence, development and metastasis of HCC in several ways. ER has been shown to inhibit STAT3 signaling and thus reduce the incidence of tumors in Huh-7 and SMCC-7721 cell lines[21].

It has also been reported that estrogen enhanced caspase-3 expression, decreased expression of MMP-2, MMP-9, PCNA, cyclin A, cyclin D1 and Bcl-2, thereby promoting apoptosis and inhibiting proliferation of malignant HCC [22].

After determining the IC50 of Tam in SMMC-7721 cells at 26.93 M after 48 h, we then treated the cells with IC50 concentration for the following RNA-seq experiments. The RNA-seq data showed very dynamic expression profiles comparing the Tam-6h group with the Tam-24h group. There were some early response genes that were dysregulated 6h after Tam treatment.This implies that some of them might be directly regulated by Tam/ESR1. There were more genes that were dysregulated in the 24-hour Tam group. While most genes were upregulated during this time course, some genes show the opposite pattern, being downregulated compared to the control group. We randomly selected some genes and performed qPCR validation. The qPCR results were consistent with the RNA-seq data. When we included the E2 treatment and hope to compare the effects between the E2 treatment and the Tam treatment. Interestingly, while some genes were oppositely regulated by E2 and Tam, some genes showed a similar trend for both drugs, suggesting that Tam might act as a partial antagonist of E2 in HCC cells.

Although Tam has an antiestrogenic effect and is used as a breast cancer treatment [23, 24], it is a partial agonist in the endometrium and increases the risk of uterine cancer [25].

Our data suggest that Tam may be a partial agonist of E2 in liver cancer. To understand the effect of Tam in HCC cells, we performed a pathway analysis. For the Tam-6h group, the most notable enriched pathways include steroid biosynthesis, terpenoid backbone biosynthesis, mineral absorption, longevity regulatory pathway - worm, MAPK signaling pathway, ErbB signaling pathway, etc. ER is known to play a crucial role in the steroid metabolism, and that DEGs have been enriched in steroid biosynthesis and terpenoid backbone biosynthetic pathways is consistent with previous reports [26]. The MAPK signaling pathway and the ErbB signaling pathway were also enriched. This implies that Tam's action is closely linked to MAPK and ERBB signaling.

For the Tam-24h group, the most notable enriched pathways include circadian rhythm, rheumatoid arthritis, hepatitis C, cocaine addiction, mineral absorption, among others. This indicates that the long-term effect of Tam in HCC differs from short-term (6 h) Tam treatment.

GSE analysis showed that DEGs in some gene sets (acevedo_liver_tumor_ vs _normal_adjacent_tissue_dn and rutella_response_to_csf2rb_ and_il4_dn, etc.) were upregulated with 6 h Tam treatment. In contrast, DEGs were downregulated in some other gensets (meissner_brain_hcp_with_H3K4me3_and_H3K27me3 and senese_HDAC3 _targets_dn, etc.). These two signaling pathways were associated with histone modifications, suggesting that 24-hour Tam treatment can induce profound epigenetic change throughout the genome.

For the PPI analysis, we generated a complicated network containing 472 nodes and 282 edges, p<1.0e-16. The top key nodes were also shown in Table 2. Interestingly, genes such as FOS (c-Fos) and CYP51A1 were enriched as key nodes. c-Fos,  a component of the AP-1 transcription factor, is usually highly expressed in HCC and is associated with necrotic foci, increased proliferation, aggressive gene signatures and metastasis [27, 28]. Jun(c-Jun) is similar to c-Fos. It has been reported that CYP51A1 was associated with tumor size [29]. These data imply that Tam treatment increased expression levels of some genes, which may favor tumor growth and progression.

Because Tam can act as a partial estrogen agonist in HCC, the effectiveness of Tam in HCC medication is questionable. If it is true that ER generally plays a protective role in HCC, the agonistic aspect of Tam may benefit patients, while the antagonistic aspect may favor cancer growth and progression. Thus, the development of agents capable of blocking properties of Tam antagonists, such as e.g. B. c-Jun/c-Fos activity, to be explored for ER-based targeted therapies.

In conclusion, our data show that Tam treatment can trigger both an early and a late response on the gene expression profile. Tam can act as a partial antagonist of E2. Some known oncogenes such as c-Jun and c-Fos can be upregulated with Tam treatment, providing new insights into Tam's action in HCC.

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