Microarray and pattern miner analysis of AXL and VIM gene networks in MDA‑MB‑231 cells
- Authors:
- Published online on: August 20, 2018 https://doi.org/10.3892/mmr.2018.9404
- Pages: 4147-4155
Abstract
Introduction
Triple negative breast cancers (TNBC) lack expression of three important receptors (ER, PR, and HER2). These cancers account for 10–15% of breast cancers, and are characterized by overexpression of epidermal growth factor receptor (EGFR), high proliferative rate, and mutations in the p53 and BRCA1 tumour suppressor genes (1). TNBC are aggressive, metastatic cancers associated with early age of onset, high relapse rates, and poor clinical outcome (1). The MDA-MB-231 cell line is a model for TNBC, and is classified as a mesenchymal, stem-like, subtype of TNBC (2). TNBC is managed with standard chemotherapy agents since there are no suitable therapeutic targets (1,3). Therefore, it is important to identify new genetic markers and therapeutic targets for TNBC. Accordingly, we re-analysed 6 microarray GEO datasets and identified a common list of 71 genes upregulated in MDA-MB-231 vs. MCF7 cells. Notably, this list contains known and novel genes that regulate the invasive phenotype of MDA-MB-231 cells. It also contain known and novel therapeutic target genes for TNBC.
The AXL and VIM genes are linked with very poor prognosis in TNBC (4). Furthermore, mechanisms of action of the AXL and VIM proteins are unclear. Therefore, elucidation of functional gene networks for AXL and VIM, could provide insights on therapeutic targets for TNBC. As reported earlier (5), our data showed significant upregulation of the AXL and VIM genes in MDA-MB-231 vs. MCF7 cells. Interestingly, pattern miner analysis showed that different subsets of the 71 genes upregulated in MDA-MB-231 vs. MCF7 cells, were significantly and specifically co-expressed with either AXL or VIM. These co-expressed gene subsets were used to model distinct functional networks for AXL and VIM. Notably, these networks contain several novel genes and potential therapeutic targets in TNBC.
Materials and methods
Microarray analysis
BRB-Array Tools is an integrated software package for visualization and statistical analysis of raw microarray data (6). We used the BRB-Array (v.4.3.2) class comparison tool to identify differentially expressed genes (DEGs) between MDA-MB-231 vs. MCF7 cell lines from 6 published Gene Expression Omnibus (GEO) datasets. These 6 datasets were deposited into the GEO database. Four of the 6 datasets have not been published yet, and include GSE 26370 (Kung et al, unpublished data), GSE 34987 (Lin et al, unpublished data), GSE 41445 (Groth et al, unpublished data), and GSE 54326 (Braunstein et al, unpublished data). The remaining 2 datasets have published data, and include GSE 29682 (7) and GSE 32474 (8). Class comparison data from each dataset was analysed with the multivariate permutation test computed with 1,000 random permutations and a false discovery rate of 1%. For each dataset, we obtained a list of genes with >2-fold change in expression between the 2 cell lines with high statistical significance (P=10−4 to 10−8). The VENNY program is a web-server that compares gene lists. Accordingly, VENNY analysis identified 71 genes as significantly upregulated in MDA-MB-231 vs. MCF7 cells in at least 3 of the 6 datasets.
Gene ontology analysis
Gene ontology (GO) analysis of the 71 genes upregulated in MDA-MB-231 vs. MCF7 cells was done. Accordingly, Table I shows functional categories of genes regulating the 4 most statistically significant biological processes. The Gene Card database (http://www.genecards.org/) also provided additional information on these 71 genes.
Protein-protein interactions
Protein-protein interactions (PPIs) were analysed by STRING [https://string-db.org/, v.10.5, with a setting of high confidence (0.70)]. Combined STRING scores for each PPI are based on three types of evidence (experimental data, databases, and published texts). PPIs with combined STRING scores ranging from 0.700–0.990 were considered for the present study.
Pattern miner systems biology tool
The Pattern miner tool provides Pearson's correlation coefficients (R-values) between input and output parameters such as gene expression and a cell line (9). All significant correlations are based on data from a minimum of 35 of 60 cell lines in the NCI-60 cancer cell line database. R values > +0.340, indicate that input and output values increase in a statistically significant manner (P<0.05). For example, a ligand and its significantly co-expressed receptor, would share an R-value > +0.340. For this study, we used higher Pearson's correlation coefficients (R>0.450, P<0.001).
Statistical analysis
P-values adjusted for false positive rates for the 71 individual DEG were obtained from microarray analysis. The P-values for 19 of these 71 DEGs are in Table II. In all Tables, P-values are expressed as negative exponents. For example, a P-value of 1E-06 indicates P=0.000001.
Results
Functional analysis of genes upregulated in MDA-MB-231 vs. MCF7 cells
Our microarray analysis identified 71 genes significantly upregulated in MDA-MB-231 vs. MCF7 cells from 6 GEO datasets (P=5E-08 to 1E-04). GO analysis of these 71 genes put 59 genes into four statistically significant GO terms (Table I). Since 16 genes were in more than 1 GO term, a total of 43 (59–16) of the 71 genes regulate cell movement, response to wounding, adhesion, and survival (Table I). Interestingly, another 19 of the 71 genes upregulated in MDA-MB-231 vs. MCF7 cells, are not reported for TNBC (Table II). The gene card database showed that 5 of these 19 novel genes promote migration (ADORA2B, BCAT1, COL13A1, EXT1, and LIMCH1), whereas another 5 novel genes promote metastasis (AKR1B1, CNN3, CYBRD1, FXYD5, and SRGN).
We also identified genes in two pathways which are poorly understood in TNBC. The first pathway is senescence, which is promoted by 6 genes with a 28–82 fold upregulation in MDA-MB-231 vs. MCF7 cells. These 6 genes include IFI16, IGFBP7, TMEM158, GNG11, PLAU and SERPINE1 (Tables I and II). The second pathway is autophagy, which is regulated by 4 novel genes (PLAC8, SRPX, CTGF, and OPTN) that were 12–85 fold upregulated in MDA-MB-231 vs. MCF7 cells. These data strongly suggest that senescence and autophagy play important roles in promoting survival and chemo-resistance in MDA-MB-231 cells (3). The literature also shows that senescence plays a key role in breast cancer progression, and that autophagy regulates maintenance of ‘stem cell-like’ properties of MDA-MB-231 cells (3,10).
In summary, GO analysis of the 71 genes upregulated in MDA-MB-231 vs. MCF7 cells shows 43 known genes regulating cell adhesion, migration, and survival (Table I). As Table II shows 10 novel upregulated genes promoting known processes (migration and metastases), and 9 novel genes regulating senescence, autophagy, and drug resistance. Therefore, 62 (43+10+9) of the 71 genes upregulated in MDA-MB-231 vs. MCF7 cells, account for the invasive and malignant phenotype of MDA-MB-231 cells.
Interactions of 71 proteins overexpressed in MDA-MB-231 cells
STRING analysis showed that the 71 gene products overexpressed in MDA-MB-231 vs. MCF7 cells showed 42 protein-protein interactions (PPIs) with significant enrichment (P=4.14×10−11). STRING data also provided robust evidence for interactions between EGFR and 5 proteins which maintain cell survival (STRING scores of 0.730–0.950). These 5 proteins are ANXA1, BIRC3, CD44, GSTP1, and SPRY2. Notably, genes encoding these 5 survival proteins are in the GO term for ‘negative regulation of apoptosis’, and were 15–40 fold upregulated in MDA-MB-231 vs. MCF7 cells (Table I). EGFR also binds proteins that induce angiogenesis (CTGF and HBEGF), drug resistance (PTRF), matrix remodelling (CTGF), and epithelial-mesenchymal transition (ANXA1, CTGF). Combined STRING scores for these EGFR interactions ranged from 0.715–0.998. Interestingly, Fig. 1 also shows that interactions between EGFR, HBEGF, and FOSL1, modulate expression of the pro-inflammatory cytokine, IL8. This is important since IL8 is linked with poor prognosis in node-negative breast cancers, and resistance to EGFR inhibitors (11).
In summary, STRING data in Fig. 1 show that specific EGFR interactions promote cell survival, epithelial-mesenchymal transition (EMT), angiogenesis, drug resistance, and inflammation. This is consistent with reports showing that EGFR signalling drives migration and metastasis of MDA-MB-231 breast cancer cells (12).
Importance of the AXL and VIM genes in TNBC
Although EGFR is a promising therapeutic target in TNBC, anti-EGFR monoclonal antibodies have poor cytotoxicity in TNBC derived cells. This can occur because the AXL-receptor tyrosine kinase dimerizes with EGFR, and initiates signalling that blocks activity of anti-EGFR therapies (5). AXL is upregulated by VIM, which encodes a key marker of EMT (13). Furthermore, overexpression of VIM correlated with increased migration and invasion of breast cancer cells (13). Clinical data show that TNBC patients overexpressing AXL and VIM, have poorer prognosis and survival than TNBC patients expressing high levels of either gene (4,5).
Our data showed that AXL and VIM were 80 and 100 fold upregulated in MDA-MB-231 vs. MCF7 cells, respectively. Although AXL and VIM regulate cell migration and invasion, these proteins are not involved in PPIs (Fig. 1), and their signaling mechanisms are unclear. Therefore, identifying functional networks for AXL and VIM requires new approaches. Accordingly, we used the pattern miner tool to identify members of the 71 genes which show statistically significant co-expression with AXL or VIM. This approach is supported by other studies on molecular networks in breast cancer. For example, a co-expression approach analysed interactions between mRNAs and long noncoding RNAs in TNBC (14). Wang et al (15), used gene co-expression networks built by Pearson's correlation coefficients, to compare metastatic and non-metastatic breast cancers. The pattern miner tool also uses Pearson's correlation coefficients to identify significantly co-expressed genes in the NCI-60 cancer cell database. Therefore, we tested and used this tool to develop functional networks for the AXL and VIM genes in MDA-MB-231 cells.
Testing pattern miner tool on genes upregulated in MDA-MB-231 vs. MCF7 cells
Genes which are significantly co-expressed with each other have a high probability of sharing similar functions. Interestingly, Kohn et al (9), reported 5 co-expressed genes which regulate cancer cell migration by different mechanisms. Notably, all 5 genes from Kohn's study (EGFR, AXL, VIM, CAPN2, and COL4A1), were significantly upregulated in MDA-MB-231 vs. MCF7 cells. The functions of EGFR, AXL, and VIM, are explained above. CAPN2 encodes a calpain 2 protease which triggers invasion of breast cancer cells (16), whereas COL4A1 encodes an isoform of collagen which controls cell invasion (17).
Since the pattern miner tool identifies co-expressed genes, we tested this tool with our data. Accordingly, we determined whether the 5 ‘migration-regulating genes’ identified by Kohn et al (9), are significantly co-expressed with any of the other 71 genes upregulated in MDA-MB-231 vs. MCF7 cells. Our results showed that all 5 ‘migration-regulating genes’ (EGFR, AXL, VIM, COL4A1, CAPN2) are significantly correlated with 7 of the 71 genes upregulated in MDA-MB-231 cells (R= +0.450 to +0.699, P<0.001). These seven genes are EXT1, MAP1b, VEGFC, SERPINE1, CNN3, PTRF, and MXRA7. Five of these 7 genes regulate cell migration (EXT1, MAP1b, VEGFC, SERPINE1, and CNN3) (Table I and Fig. 1). The sixth gene (PTRF), regulates multidrug resistance (18), and the function of the seventh gene (MXRA7), is unknown. Since PTRF and MXRA7 were significantly co-expressed with the 5 genes controlling migration (EGFR, AXL, VIM, CAPN2, and COL4A1), pattern miner predicts that both PTRF and MXRA7 can also regulate cancer cell migration. This pilot test showed that pattern miner is a valid and reliable tool to identify novel genes which regulate cell migration, and show co-expression with AXL or VIM.
Strategy to identify AXL and VIM gene networks with pattern miner
The previous section showed that only 7 of the 71 genes upregulated in MDA-MB-231 vs. MCF7 cells, were co-expressed with all 5 ‘migration-regulating genes’ (EGFR, AXL, VIM, CAPN2, and COL4A1) (9). However, Tables I and II clearly showed that additional members of these 71 genes regulate cell migration. In order to find these additional members, we used different combinations of these 5 ‘migration-regulating genes’ as input ‘baits’ for pattern miner analysis. Thus, 7 gene pairs (AXL-EGFR, AXL-VIM, AXL-COL4A1, AXL-CAPN2, VIM-EGFR, VIM-COL4A1, and VIM-CAPN2), 2 gene triplets (AXL-COL4A1-VIM and AXL-EGFR-CAPN2), and 2 gene quadruplets (AXL-COL4A1-EGFR-CAPN2 and AXL-VIM-EGFR-CAPN2) were used as input ‘baits’. Next, we used the pattern miner tool to determine whether each of these ‘baits’ were specifically and significantly co-expressed with any of the 71 genes upregulated in MDA-MB-231 vs. MCF7 cells. This approach should identify subsets of the 71 upregulated genes which co-express with AXL or VIM, and form functional networks that regulate migration of MDA-MB-231 cells.
Identifying and modelling the AXL gene network
We used pattern miner to determine whether any of the 71 genes upregulated in MDA-MB-231 vs. MCF7 cells, were co-expressed with the different input ‘baits’ listed above. Table III shows that 4 input baits containing AXL, were specifically and significantly co-expressed with 4 different subsets of these 71 genes. This data was used to develop a model for the AXL gene network (Fig. 2). Thus, Fig. 2 shows that the first ‘bait’ (AXL-COL4A1) was significantly co-expressed with CTGF, which regulates EMT (19). The second ‘bait’ (AXL-COL4A1-VIM) was co-expressed with the HEG1 and LOX genes which control vasculogenesis and metastasis (20). The third ‘bait’ (AXL-COL4A1-EGFR-CAPN2), was significantly co-expressed with 3 genes regulating wound healing, angiogenesis, and cell survival, (PDGFC, CYR61, and OPTN; Table I). This bait was also co-expressed with a gene that regulates tumorigenicity (NNMT) (21). The fourth ‘bait’ (AXL-EGFR-CAPN2) was co-expressed with 2 genes regulating adhesion and drug resistance (TGM2, PLAU), and 3 genes controlling stress response (MT1E, NT5E, and AOX1) (Tables I and III) (22).
In summary, the AXL network has 4 distinct sets of co-expressed genes with different functions. These 4 sets contain 12 genes which were 12–85 fold upregulated in MDA-MB-231 vs. MCF7 cells, and account for key functions of AXL (Tables I, III, and Fig. 2). Notably, 6 of these 12 genes (HEG1, OPTN, MT1E, NNMT, AOX1, and TGM2) have not been reported for TNBC.
Identifying and modelling the VIM gene network
Since pattern miner analysis provided insights into the AXL gene network, we undertook a similar study for the VIM gene. Table IV shows that VIM, and 3 input baits containing VIM, were specifically co-expressed with 4 different subsets of the 71 genes upregulated in MDA-MB-231 vs. MCF7 cells. This data was used to develop a model for the VIM gene network (Fig. 3). Thus, VIM alone was co-expressed with 5 genes (MSN, PRNP, ACSL4, IFI16, and SRPX) with varying functions. MSN regulates invasiveness, PRNP protects against endoplasmic reticular stress (Table I), and ACSL4 promotes lipogenesis (23). The IFI16 and SRPX genes regulate senescence and autophagy, respectively (24,25) (Tables II and IV). The first ‘bait’ (VIM-COL4A1), was co-expressed with three genes (CALD1, AKR1B1, and IGFBP7) that regulate motility, detoxification, and senescence (26,27) (Tables II and IV). The second ‘bait’ (VIM-AXL), was exclusively co-expressed with an oncogene correlated with lymph node metastases (EMP3) (28). The third bait (VIM-AXL-EGFR-CAPN2), was co-expressed with the FOSL1 and CD44 genes. Notably, the FOSL1/Fra-1 transcription factor controls the invasive phenotype of MDA-MB-231 cells (29), and can regulate expression of 3 other genes upregulated in these cells (CD44, VEGFC, and ADORA2B). CD44 regulates extracellular interactions, and is a marker of tumour progression (30), whereas VEGFC and ADORA2B regulate migration (31).
In summary, the 4 components of the VIM network contain 11 genes which were 12–105 fold upregulated in MDA-MB-231 vs. MCF7 cells, and account for key functions of VIM (Tables I, II and IV and Fig. 3). Notably, 6 of these 11 genes (PRNP, AKR1B1, CALD1, EMP3, SRPX, and IFI16) have not been reported for TNBC.
Comparison of the AXL and VIM gene networks
We carefully compared the AXL and VIM gene networks shown in Figs. 2 and 3. Notably, these networks contain very different subsets of the 71 genes upregulated in MDA-MB-231 vs. MCF7 cells. The AXL network has 12 genes regulating EMT, invasion, adhesion, and chemo-resistance, whereas the VIM network has 11 genes regulating lipogenesis, senescence, autophagy, and metastases. These results strongly support our hypothesis that AXL and VIM participate in 2 separate networks of co-expressed genes that are highly upregulated in MDA-MB-231 vs. MCF7 cells.
Identifying therapeutic targets in MDA-MB-231 cells
One of our objectives was to identify novel therapeutic targets in MDA-MB-231 cells. The gene card database showed that 15 of the 71 genes upregulated in MDA-MB-231 vs. MCF7 cells, are therapeutic targets in other cancers. These 15 therapeutic targets regulate EGFR signalling, invasion, inflammation, angiogenesis, metastases, senescence, and autophagy (CD44, CTGF, CYR61/CCN1, EMP3, HEG1, IL8, ADORA2B, AGPS, AKR1B1, EXT1, IGFBP7, PLAC8, PTRF, SRGN, and TMEM158/RIS1). Six of these 15 target genes (CD44, CTGF, CYR61/CCN1, EMP3, HEG1, and IL8) are reported for TNBC. Notably, three of these 6 known therapeutic targets are in the AXL network in Fig. 2 (CTGF, CYR61/CCN1, and HEG1), and another 2 are in the VIM network in Fig. 3 (CD44 and EMP3). However, 9 of these 15 therapeutic targets are novel for TNBC, and are in Table II (ADORA2B, AGPS, AKR1B1, EXT1, IGFBP7, PLAC8, PTRF, SRGN, and TMEM158/RIS1). Interestingly, 2 of these 9 novel genes are in the VIM network. (AKR1B1 and IGFBP7) (Fig. 3). We also note 2 other novel, potential therapeutic target genes which regulate iron uptake (CYBRD1 and FXYD5, Table II).
To summarize, our re-analysis of the 6 GEO datasets identified many therapeutic targets from the list of 71 genes upregulated in MDA-MB-231 vs. MCF7 cells. Thus, we identified 6 known therapeutic gene targets reported in other cancers, 9 novel therapeutic targets for TNBC, and 2 potential targets which regulate iron uptake (Table II).
Discussion
This study is unique for five reasons. First, we re-analysed raw data from 6 microarray GEO datasets and identified a common list of 71 genes upregulated in MDA-MB-231 vs. MCF7 cells. Second, we identified novel upregulated genes regulating migration and metastasis in MDA-MB-231 vs. MCF7 cells. This is important, since migration and metastasis are phenotypic hallmarks of TNBC. Third, we found 9 novel upregulated genes that promote senescence, autophagy, chemo-resistance, and stem-cell like properties of MDA-MB-231 cells. Fourth, we used pattern miner to determine whether any of the 71 genes upregulated in MDA-MB-231 vs. MCF7 cells, were significantly co-expressed with different combinations of the 5 ‘migration-regulating genes’ (EGFR, AXL, VIM, CAPN2, and COL4A1). Interestingly, our results identified 2 distinct, non-overlapping networks of upregulated genes significantly co-expressed with either AXL or VIM. Notably, these networks can drive malignancy by regulating very different processes in MDA-MB-231 cells. Furthermore, these networks contain known therapeutic target genes, and other novel genes that can expand the signalling network and functionality of the AXL and VIM proteins in MDA-MB-231 cells. Fifth, we identified 6 known therapeutic targets, 9 novel therapeutic targets, and 2 potential therapeutic target genes; from the list of 71 genes upregulated in MDA-MB-231 vs. MCF7 cells. All novel therapeutic targets for TNBC require experimental verification.
Acknowledgements
The authors sincerely thank the Management of Dr. M.G.R. Educational and Research Institute (Chennai, India) for providing laboratory and computational facilities. The authors thank Dr. Rama Vaidyanathan, Director R&D (Dr. M.G.R. Educational and Research Institute) for valuable support.
Funding
The present study was supported by the Science and Engineering Research Board, Government of India under the Fast track Scheme for Young Scientists (grant no. SR/FT/LS-140/2010).
Availability of data and materials
The original microarry data are freely available from the GEO datasets of NCBI (https://www.ncbi.nlm.nih.gov/gds). Analysis of these microarray data for this study will be made available from the corresponding author on reasonable request.
Authors' contributions
SM and MR contributed to the acquisition and analysis of data, and preparation of figures. VNS and SN performed the interpretation of data, manuscript writing and critical revisions for important intellectual content. All authors approved the final version to be published.
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.
Glossary
Abbreviations
Abbreviations:
|
DEGs |
differentially expressed genes |
|
EGFR |
epidermal growth factor receptor |
|
EMT |
epithelial-mesenchymal transition |
|
GEO |
Gene Expression Omnibus |
|
GO |
Gene Ontology |
|
ER |
estrogen receptor |
|
PPIs |
protein-protein interactions |
|
PR |
progesterone receptor |
|
TNBC |
triple negative breast cancers |
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