Introduction
Breast cancer is one of the most common malignant tumors around the world. One in 8 women will develop breast cancer in her lifetime in the United States (1). In China, breast cancer is also one of the most common cancers in females. The incidence of breast cancer has increased annually (2). With the help of early diagnosis and multimodality therapy, death rate continues to decline for breast cancer (1–3).
The treatment strategy of breast cancer includes surgery, chemotherapy, radiotherapy, endocrine therapy, target biotherapy and others. However, a considerable number of breast cancer patients inevitably suffer from relapse or metastasis and die from this disease. Long-term maintenance treatment is vital for breast cancer therapy. Endocrine therapy is the important maintenance treatment for estrogen-receptor (ER) positive breast cancer. It can significantly improve the prognosis of premenopausal and postmenopausal patients (4–8). However, for triple-negative breast cancer (TNBC), there is scarce effective maintenance treatment and the prognosis is still poor. Thus new strategies are necessary for the maintenance treatment of TNBC. Traditional Chinese medicine (TCM) has shown its potential anticancer roles. Many agents extracted from TCM have been discovered (9), such as alkaloids (10), flavonoids, polyphenols (11,12) and terpenoids (13), although some of the mechanisms of action have not been elucidated.
Trametes robiniophila murr (Huaier) is a kind of officinal fungi in China. It has been applied in TCM for approximately 1600 years (14) and used as a complementary maintenance treatment of breast cancer in recent decades. Huaier extract consists of many kinds of ingredients. It was confirmed that the most effective ingredient of Huaier extract was proteoglycan, containing 41.53% polysaccharides, 12.93% amino acids and 8.72% water. However, the inhibitory effect of proteoglycan was still less effective than the Huaier extract (15,16). Thus, it is reasonable to speculate that the effects of the Huaier extract may be due to the additive or synergistic effect of different fractions. The antitumor effects of Huaier extract displayed various biological activities, however, the detailed mechanisms of Huaier extract are not clear.
In order to identify the multi-target effects of Huaier as an effective maintenance treatment for TNBC, we detected the differentially expressed genes (DEGs) and altered biological functions of MDA-MB-231 cells after the treatment of Huaier aqueous extract by using microarray profiling in this study. The results of microarray assays indicated that Huaier indeed had various activities in anti-breast cancer treatment and has huge potential for maintenance treatment of TNBC. Under the guidance of this study, we have investigated Huaier from many different aspects and have revealed its multiple functions. Therefore, this study provided very important guiding roles for researchers.
Materials and methods
Cell culture
MDA-MB-231 cells was obtained from American Type Culture Collection (ATCC) (Rockefeller, MD, USA), and routinely cultured in Dulbecco’s modified Eagle’s medium (DMEM) (Gibco, Rockville, MD, USA) supplemented with 10% fetal bovine serum (FBS) (Clark Bioscience, Seabrook, MD, USA), 100 U/ml penicillin and 100 μg/ml streptomycin under the conditions of 5% CO2 at 37°C.
Preparation of Huaier aqueous extract
Electuary ointment of Huaier was a kind gift from Gaitianli Medicine Co. Ltd (Jiangsu, China). One gram of the electuary ointment was dissolved in 10 ml of complete DMEM medium and was sterilized with 0.22 μm filter to obtain the 100 mg/ml stock solution for long storage at −20°C.
MTT assay
The MDA-MB-231 cells (1.5×103 cells/well) were cultured in 96-well plates in 5% CO2 at 37°C in complete medium. After incubation overnight, the medium was replaced with different concentrated solutions and incubated for 24, 48 or 72 h individually. Afterwards, 20 μl of 3-(4, 5-dimethylthi-azol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT, 5 mg/ml in PBS) (Sigma-Aldrich, St. Louis, MO, USA) was added to each well and the cells were incubated for another 4 h at 37°C. Then the supernatants were carefully aspirated and 100 μl of dimethyl sulfoxide (DMSO) (Sigma-Aldrich) was added. The absorbance values were read by Microplate Reader (Bio-Rad, Hercules, CA, USA) at 570 nm.
Cell treatment for microarray profiling
MDA-MB-231 cells suspended at 1×105/ml density in DMEM with 10% FBS were incubated in 25 cm2 cell culture flask. The next day the medium was replaced with new medium with and without Huaier aqueous extract (8 mg/ml) at 37°C for 72 h.
HTA 2.0 transcriptome microarray assay
Total RNA was isolated with TRIzol (Invitrogen, Carlsbad, CA, USA) from cells after 72 h treatment of Huaier aqueous extract (8 mg/ml). RNAs from three donors with equal amount were pooled together for each sample. Biotinylated cDNA were prepared from 250 ng total RNA according to the Affymetrix standard protocol by using Ambion® WT Expression kit. cDNA were hybridized in Hybridization Oven 645 for 16 h at 45°C on GeneChip Human Transcriptome Array 2.0. GeneChips were scanned by using Affymetrix® GeneChip Command Console (AGCC). The data were analyzed with Robust Multichip Analysis (RMA) algorithm using Affymetrix default analysis settings and global scaling as normalization method. Fold change was used to identify differentially expressed genes (DEGs).
GO analysis
GO analysis was applied to analyze the main function of the differential expression genes according to the Gene Ontology which is the key functional classification of NCBI, which can organize genes into hierarchical categories and uncover the gene regulatory network on the basis of biological process and molecular function (17,18).
Specifically, two-sided Fisher’s exact test and χ2 test were used to classify the GO category, and the false discovery rate (FDR) (19) was calculated to correct the p-value, the smaller the FDR, the small the error in judging the p-value. The FDR was defined as,
F
D
R
=
1
-
N
k
T
where Nk refers to the number of Fisher’s test p-values less than test p-values. We computed p-values for the GOs of all the differential genes. Enrichment provides a measure of the significance of the function: as the enrichment increases, the corresponding function is more specific, which helps us to find those GOs with more concrete function description in the experiment. Within the significant category, the enrichment Re was given by:
Re
=
(
n
f
/
n
)
/
(
N
f
/
N
)
where ‘nf ‘ is the number of flagged genes within the particular category, ‘n’ is the total number of genes within the same category, ‘Nf ‘ is the number of flagged genes in the entire microarray, and ‘N’ is the total number of genes in the microarray (20).
Pathway analysis
Pathway analysis was used to find the significant pathway of the differential genes according to KEGG, Biocarta and Reatome. The Fisher’s exact test and χ2 test were employed to select the significant pathway, and the threshold of significance was defined by P-value and FDR. The enrichment Re was calculated like the equation above (21–23).
Gene-gene Interaction Network
Gene-gene interaction network was constructed based on the data of differentially expressed genes. Using java that allows users to build and analyze molecular networks, network maps were constructed. For instance, if there is confirmative evidence that two genes interact with each other, an interaction edge is assigned between the two genes. The considered evidence is the source of the interaction database from KEGG. Networks are stored and presented as graphs, where nodes are mainly genes (protein and compound) and edges represent relation types between the nodes, e.g. activation or phosphorylation. The graph nature of Networks raised our interest to investigate them with powerful tools implemented in R.
To investigate the global network, we computationally identified the most important nodes. To this end we turned to the connectivity (also known as degree) defined as the sum of connection strengths with the other network genes:
K
i
=
∑
u
≠
i
a
u
i
In gene networks, the connectivity measures how correlated a gene is with all other network genes. For a gene in the network, the number of source genes of a gene is called the indegree of the gene, and the number of target genes of a gene is its outdegree. The character of genes is described by betweenness centrality measures reflecting the importance of a node in a graph relative to other nodes. For a graph G:(V,E) with n vertices, the relative betweenness centrality C′B (v) is defined by:
C
B
′
(
v
)
=
2
n
2
-
3
n
+
2
∑
s
≠
v
≠
t
∈
V
s
≠
t
σ
s
t
(
v
)
σ
s
t
where σst is the number of shortest paths from s to t, and σst (v) is the number of shortest paths from s to t that pass through a vertex v (24–28).
Discussion
Breast cancer is one of the most common malignant tumors in the world. Although the incidence of breast cancer has increased annually, the death rate continues to decline because of early diagnosis and multimodality therapy (1–3). However, still many breast cancer patients inevitably suffer from relapse or metastasis and die from this disease.
Maintenance treatment is very important for breast cancer patients. For ER positive breast cancer, long-term endocrine therapy is the effective maintenance treatment. Large randomized trials have shown that 10 years of adjuvant endocrine therapy is superior to 5 years (6), showing the value of the long-term maintenance treatment. In comparison, chemotherapy is the only systemic therapy for TNBC. Management of TNBC is a challenge due to lack of targeted therapy, aggressive tumor behavior and relatively poor prognosis (29). Although it has been reported that TNBC was chemosensitive, the results remained unsatisfactory (30–33). Maximal effort should be made to select the best possible drugs for effective maintenance treatment of TNBC.
TCM has a very long history in China. In modern medical research many kinds of TCM have been found with antitumor effects (9). Trametes robiniophila murr (Huaier) is a kind of officinal fungi in China. In recent decades it has been used as a complementary maintenance treatment mostly in liver cancer and breast cancer. However, its mechanisms are still unclear. In recent three or four years, studies on Huaier extract began to appear. The use of Huaier in liver cancer is relatively mature. Until now it has been reported that Huaier extract could inhibit liver cancer from multiple perspectives (34–37). However, there are few studies on the mechanisms of Huaier extract in breast cancer. Therefore, we conducted this study to clarify the possible effects.
Huaier extract consists of many kinds of ingredients. Proteoglycan was confirmed to be the most effective ingredient. However, it was reported that the inhibitory effect of proteoglycan was less effective than the Huaier extract (15,16). It indicated that the effects of Huaier in the clinical use are not only because of the proteoglycan. In addition Huaier extract was the one which was applied in the clinical work but not proteoglycan. Thus in our study we chose Huaier aqueous extract to treat breast cancer cells and to investigate its mechanisms.
Microarray profiling has given great guidance for modern medical research. We used HTA 2.0 Transcriptome Microarray Assay to analyze the effects of Huaier aqueous extract. MDA-MB-231 cells, which are the most often used in TNBC research, were selected to be treated by Huaier extract.
Preliminary screening of DEGs gave 387 genes (226 up- and 161 down-regulated) in MDA-MB-231 cells which were regulated significantly. Table I lists the top 5 up- and down-regulated genes in MDA-MB-231 cells. Due to the limits of DEGs screening, we further conducted the GO analysis, KEGG pathway analysis and gene-gene interaction network analysis to provide a more intuitive result of the effects of Huaier extract.
Figs. 3 and 4 provide the results of GO analysis. Due to the count of genes and degree of enrichments of each GO categories, Fig. 3 was constructed according to the enrichment ranks. In Fig. 3, the GO category with larger enrichment ranked the top and was more affected by Huaier extract when the p-values were equal. According to the p-values of each GO category, we constructed Fig. 4, providing the p-values and gene numbers of each category. From the results of GO analysis, we found that many key steps during the breast cancer development and progression were regulated significantly by Huaier extract, including DNA-dependent transcription, apoptotic process, cell cycle arrest, cellular response to hypoxia, immune response, DNA replication, and cell proliferation. It gave us preliminary analysis of the multi-target effects of Huaier extract.
Results of KEGG pathway analysis are shown in Fig. 5. There were 59 pathways containing genes which were upregulated and 45 pathways downregulated. Pathways, such as proteoglycans in cancer, MAPK signaling pathway, NF-κB signaling pathway, apoptosis, cell cycle, DNA replication and metabolic pathways, were significantly regulated. These results suggested further that Huaier extract inhibited breast cancer from multiple perspectives.
According to the methods described above, we constructed the gene-gene interaction network (Figs. 6 and 7) to fully understand the relationships between the gene groups. Based on these results, some traditional key genes play important roles in the regulation of Huaier extract treatment, including SQSTM1, ATM, CDK4 and E2F1. It provided explicit research fields for future study.
All the analyses described above indicated that many functions and pathways were regulated obviously by Huaier extract. Based on these results, we further studied the effects of Huaier extract in detail. Fig. 7B shows part of the gene-gene interaction network on proliferation, apoptosis and the cell cycle. According to this result, we found that cell invasion and migration were suppressed with exposure to Huaier extract. Huaier was able to induce G0/G1 cell cycle arrest and p53 accumulation and activation. Breast cancer cell apoptosis executed by caspase-3 were induced by Huaier extract through the mitochondrial pathway (38). In a similar way, Fig. 7A showed the regulated genes which were related with angiogenesis. Based on this, we found that treatment with Huaier extract inhibited angiogenesis in a dose-dependent manner and decreased the levels of phosphorylated extracellular signal-regulated kinase (ERK), transcription factor p65, c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription 3 (STAT3) and the expression of vascular endothelial growth factor (VEGF) (39). Results of microarray also hinted at the regulation of metabolic, immune and stem-like characteristics by Huaier extract, thus, these altered functions will be our future research directions.
In conclusion, microarray profiling showed the multi-target effects of Huaier extract and provided us various research fields. Based on the results of microarray, we further studied the functions of Huaier and confirmed its key roles in treatment of TNBC. All these studies indicated that Huaier extract had multiple effects in breast cancer therapy and could be an important potential maintenance treatment drug for TNBC.