Oncotarget

Research Papers:

MAPK, NFκB, and VEGF signaling pathways regulate breast cancer liver metastasis

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Oncotarget. 2017; 8:101452-101460. https://doi.org/10.18632/oncotarget.20843

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Xinhua Chen _, Zhihong Zheng, Limin Chen and Hongyu Zheng

Abstract

Xinhua Chen1, Zhihong Zheng2, Limin Chen1 and Hongyu Zheng1

1Department of Medical Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China

2Department of Hematology, Fujian Medical University Union Hospital, Fuzhou, Fujian, China

Correspondence to:

Xinhua Chen, email: cxhfzfj@163.com

Keywords: breast cancer; metastasis; liver; microarray; interaction network

Received: April 23, 2017    Accepted: August 07, 2017    Published: September 12, 2017

ABSTRACT

In this study, we investigated the molecular pathways regulating breast cancer liver metastasis. We identified 48 differentially expressed genes (4 upregulated and 44 downregulated) by analyzing microarray dataset GSE62598 from Gene Expression Omnibus (GEO). We constructed a genetic interaction network with 84 nodes and 237 edges using the String consortium database. The network was reliably robust with a clustering coefficient (cc) of 0.598 and protein-protein interaction (PPI) enrichment p value of zero. Using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases, we identified MAPK, NFκB and VEGF signaling pathways as the most critical pathways regulating breast cancer liver metastasis. These results indicate that the distinct breast cancer metastatic stages, including dissemination from the primary breast tumor, transit through the vasculature, and survival and proliferation in the liver, are regulated by the MAPK, NFκB, and VEGF signaling pathways.


INTRODUCTION

Breast cancer is the most frequently diagnosed cancer globally and is the leading cause of cancer-related deaths among women [1]. In the United States, more than 240,000 newly diagnosed breast cancer cases and 40,000 deaths were reported in 2016 [2]. Liver metastasis is reported in 15% of newly diagnosed breast cancer patients [3, 4]. Breast cancer liver metastasis is associated with very poor prognosis and has a survival time of only 4-8 months, if untreated [5]. Introduction of new therapies in the last decade has resulted in 1-2% yearly decrease in mortality rates [6]. However, breast cancer patients with liver metastasis still are associated with very poor outcomes [7].

Metastatic disease is a complex, multistage process that involves detachment of breast cancer cells from the primary tumor, which then travel through the blood or lymphatic system and finally survive and proliferate in the liver. Given the complex multistep process, liver metastasis involves a sophisticated network of molecular events. However, the molecular mechanisms associated with breast cancer metastasis to the liver are unclear, and their understanding is essential for developing more effective therapies. In this study, therefore, we generated a genetic interaction network using microarray gene expression data from breast cancer liver metastases and explored the molecular mechanisms involved using bioinformatic analyses.

RESULTS

Forty-eight genes are differentially expressed in metastatic breast tumor cells

Table 1 lists the differentially expressed genes with a fold change ≥2 and false discovery rate ≤ 5%. There were 48 differentially expressed genes that were distinctly upregulated (4 genes) or downregulated (44 genes) in metastatic tumor cells than in normal parental cells. Figure 1 shows the heat map of the differentially expressed genes.

Table 1: Significant genes identified by significant analysis of microarray (SAM) in liver-aggressive explant versus primary tumor explant

Gene ID

Gene Name

Fold Change

Gene regulation

A_52_P618173

Limch1

2.290749902

Up

A_52_P418791

Rbp1

2.424147188

Up

A_51_P423484

Rbp1

2.165856946

Up

A_52_P299915

Map2k6

2.176087369

Up

A_51_P102538

Otop1

0.336723951

Down

A_51_P289341

Fermt1

0.317362329

Down

A_52_P452667

Prom2

0.285970233

Down

A_51_P333923

Tspan1

0.315241505

Down

A_51_P167489

Lama3

0.41612039

Down

A_51_P177242

Unc13b

0.418318499

Down

A_52_P88091

Dsg2

0.403969687

Down

A_51_P233153

Cadps2

0.298078637

Down

A_51_P196207

Capsl

0.388252581

Down

A_52_P79821

Esrp1

0.26893644

Down

A_52_P559779

Dsg2

0.347328438

Down

A_51_P493987

Moxd1

0.417459194

Down

A_52_P87757

Il24

0.336785971

Down

A_52_P134455

Fermt1

0.367135842

Down

A_51_P356055

Grp

0.449573589

Down

A_51_P353252

Mal2

0.291415896

Down

A_51_P187602

Serpinb5

0.3120555

Down

A_52_P638605

Ap1m2

0.436913739

Down

A_51_P105879

Myo5b

0.486596961

Down

A_52_P405945

Prl3d2

0.483474132

Down

A_51_P401517

Il24

0.483144818

Down

A_52_P252931

Dsc2

0.491809463

Down

A_52_P468068

Tchh

0.490774711

Down

A_51_P322115

Htr5b

0.372641522

Down

A_52_P286350

Sh2d1b1

0.471867312

Down

A_52_P487686

BC100530

0.483518325

Down

A_51_P489488

Pde4dip

0.487698119

Down

A_51_P179293

2310002L13Rik

0.382311761

Down

A_51_P322090

Ovol2

0.489037358

Down

A_52_P661412

Adora1

0.485167002

Down

A_52_P683580

Tbc1d9

0.471654273

Down

A_51_P206475

Lce1i

0.476512201

Down

A_51_P496540

Sh2d1b1

0.488430246

Down

A_52_P601757

Dsg2

0.414988774

Down

A_51_P496253

Slc6a4

0.464974691

Down

A_51_P438283

Il1a

0.497937489

Down

A_51_P455620

Fam167a

0.45781262

Down

A_51_P332309

Eomes

0.434829918

Down

A_51_P225827

Ovol1

0.474676527

Down

A_51_P338878

P2ry12

0.424196491

Down

A_52_P373982

Grhl2

0.481346604

Down

A_52_P642488

Kcnk1

0.43461204

Down

A_51_P303079

Tmem54

0.492962995

Down

A_51_P362328

Grhl2

0.469572322

Down

Abbreviation: SAM, Significance Analysis Microarray

Heatmap visualization of the differently expressed genes identified by Significant Analysis of Microarray (SAM) in metastatic tumor cells (GSM1529777, GSM1529778, GSM1529779) versus 4T1 parental cells (GSM1529768, GSM1529769, GSM1529770).

Figure 1: Heatmap visualization of the differently expressed genes identified by Significant Analysis of Microarray (SAM) in metastatic tumor cells (GSM1529777, GSM1529778, GSM1529779) versus 4T1 parental cells (GSM1529768, GSM1529769, GSM1529770). Red represents up-regulated genes, while green represents down-regulated genes.

A genetic interaction network based on the differently expressed genes

A genetic interaction network was constructed from the 48 differentially expressed genes using the String platform future analysis (Figure 2). The interaction network consisted of 84 nodes and 237 edges. The average node degree was 5.64. The network was reliably robust with a clustering coefficient (cc) of 0.598 and protein-protein interaction (PPI) enrichment p value of zero.

Genetic interaction network associated with breast cancer liver metastases basing on String platform.

Figure 2: Genetic interaction network associated with breast cancer liver metastases basing on String platform. In this figure, each circle represents a gene (node) and each connection represents a direct or indirect connection (edge). Line color indicates the type of interaction evidence and line thickness indicates the strength of data support.

GO analysis of the differently expressed genes

Molecular function analysis by the GO con-sortium database revealed that most of the differently expressed genes regulated protein binding and kinase activity (Table 2). Besides, the major biological processes associated with the liver metastases were positive regulation of cell communication, MAPK cascade, signaling, and protein kinase activity (Table 3).

Table 2: Molecular function analysis of the genetic interaction network associated with liver-aggressive explant in terms of Gene Ontology (GO)

GO ID

Molecular Function

Observed Gene Count

FDR

GO.0004702

receptor signaling protein serine/threonine kinase activity

15

3.13E-21

GO.0005515

protein binding

7

2.03E-05

GO.0004708

MAP kinase kinase activity

41

2.41E-05

GO.0017137

Rab GTPase binding

5

2.74E-05

GO.0031489

myosin V binding

6

0.000307

GO.0017022

myosin binding

4

0.000381

GO.0004709

MAP kinase kinase kinase activity

5

0.000518

GO.0005488

binding

4

0.00169

GO.0017075

syntaxin-1 binding

59

0.00354

GO.0004707

MAP kinase activity

3

0.00402

GO.0004674

protein serine/threonine kinase activity

3

0.00636

GO.0004946

bombesin receptor activity

9

0.0113

GO.0005102

receptor binding

2

0.0128

GO.0004908

interleukin-1 receptor activity

14

0.018

GO.0019905

syntaxin binding

2

0.0215

GO.0019899

enzyme binding

4

0.0253

GO.0004871

signal transducer activity

15

0.032

GO.0005179

hormone activity

16

0.0377

GO.0060089

molecular transducer activity

4

0.0377

GO.0086083

cell adhesive protein binding involved in bundle of His
cell-Purkinje myocyte communication

17

0.0377

Abbreviations: FDR, false discovery rate; GO, Gene Ontology.

Table 3: Biological process analysis of the genetic interaction network associated with liver-aggressive explant in terms of Gene Ontology (GO)

GO ID

Biological Process

Observed Gene Count

FDR

GO.0051046

regulation of secretion

21

5.45E-10

GO.0080134

regulation of response to stress

28

6.97E-10

GO.1903530

regulation of secretion by cell

19

4.53E-09

GO.0051047

positive regulation of secretion

15

8.72E-09

GO.0032101

regulation of response to external stimulus

20

1.24E-07

GO.0032879

regulation of localization

31

1.24E-07

GO.0051049

regulation of transport

27

1.24E-07

GO.0051050

positive regulation of transport

20

1.24E-07

GO.0031347

regulation of defense response

18

3.95E-07

GO.0010647

positive regulation of cell communication

25

4.18E-07

GO.0060341

regulation of cellular localization

22

4.18E-07

GO.0043410

positive regulation of MAPK cascade

14

8.81E-07

GO.0014047

glutamate secretion

6

1.17E-06

GO.0050690

regulation of defense response to virus by virus

6

1.38E-06

GO.0023056

positive regulation of signaling

23

1.79E-06

GO.0051650

establishment of vesicle localization

10

2.00E-06

GO.0046717

acid secretion

7

3.36E-06

GO.0001934

positive regulation of protein phosphorylation

17

5.02E-06

GO.0016079

synaptic vesicle exocytosis

37

3.10E-13

GO.0045860

positive regulation of protein kinase activity

11

3.55E-13

Abbreviations: FDR, false discovery rate; GO, Gene Ontology; MAPK: mitogen-actived protein kinase.

Signaling pathways involved in breast cancer liver metastasis

Table 4 shows the signaling pathways involved in breast cancer liver metastases by the KEGG database. The major signaling pathways included the MAPK, NF-kappa B and VEGF signaling pathways that maybe critical for the distinct pathological stages of liver metastasis.

Table 4: Signaling pathway analysis of the genetic interaction network associated with liver-aggressive explant in terms of Gene Ontology (GO)

Pathway ID

Signaling pathway

Observed Gene Count

FDR

4010

MAPK signaling pathway

16

1.42E-12

4668

TNF signaling pathway

9

7.29E-08

5014

Amyotrophic lateral sclerosis (ALS)

7

1.26E-07

4750

Inflammatory mediator regulation of TRP channels

8

3.45E-07

4380

Osteoclast differentiation

8

1.45E-06

5140

Leishmaniasis

6

1.24E-05

4721

Synaptic vesicle cycle

5

0.000104

4664

Fc epsilon RI signaling pathway

5

0.000156

4660

T cell receptor signaling pathway

5

0.000787

5146

Amoebiasis

5

0.000993

4060

Cytokine-cytokine receptor interaction

7

0.00133

4722

Neurotrophin signaling pathway

5

0.00145

5160

Hepatitis C

5

0.00206

4015

Rap1 signaling pathway

6

0.00207

4911

Insulin secretion

4

0.00355

4728

Dopaminergic synapse

4

0.0148

5131

Shigellosis

3

0.0148

4370

VEGF signaling pathway

3

0.0155

5162

Measles

4

0.0162

5120

Epithelial cell signaling in Helicobacter pylori infection

3

0.0194

5222

Small cell lung cancer

3

0.0351

4064

NF-kappa B signaling pathway

3

0.0384

5168

Herpes simplex infection

4

0.0384

4723

Retrograde endocannabinoid signaling

3

0.0473

Abbreviations: FDR, false discovery rate; GO, Gene Ontology.

DISCUSSION

Breast cancer liver metastasis is a complex process that includes tumor cell dissemination from the primary tumor, transit through the blood or lymphatic system, and proliferation in liver. Underlying this complex multistep process is a sophisticated network of molecular events. In this study, we generated, for the first time, a comprehensive genetic interaction network from the microarray gene expression profile to identify the molecular mechanisms involved in breast cancer liver metastases. The results suggested that MAPK, NF-kappa B and VEGF signaling pathways are significantly associated with distinct stages of breast cancer liver metastasis.

Dissemination of carcinoma cells is the initial step of the metastasis, which is initiated by epithelial-mesenchymal transition (EMT) program during which tumor cells acquire mesenchymal features and lose epithelial properties [8, 9]. The complex molecular events during EMT are initiated and controlled by signaling pathways that respond to extracellular cues. The transforming growth factor-β (TGF-β) signaling family plays a predominant role in EMT [10]. Moreover, the MAPK signaling pathway is required for the initiation of TGF-β induced EMT [11, 12]. In addition to TGF-β family proteins, tyrosine kinase receptors (RTKs) play a key role in the trans-differentiation process, further highlighting the importance of MAPK signaling [13]. MAPK pathway inhibitors have been used clinically for many cancers, including breast cancer [14]. In addition, NFκB is an important regulator of the expression of various proteins involved in the immune response [15].

After successfully disassociating from the primary tumor, metastatic carcinoma cells traverse the blood or lymphatic system, during which they interact with several cell types including platelets, neutrophils, monocytes, macrophages, and endothelial cells [16]. The circulating tumor cells also interact with platelets [17] and high platelet counts are associated with poor prognosis in carcinomas [18]. Recent studies have revealed that platelets alter the fate of circulating cancer cells [19]. Platelet-tumor cell contacts and platelet-derived TGF-β synergistically activate the TGF-β/Smad and NFκB pathways in cancer cells enabling their transition to an invasive mesenchymal-like phenotype, thereby enhancing metastasis [20]. Inhibition of NFκB signaling in cancer cells or ablation of TGF-β1 expression in platelets protects against lung metastasis in vivo [20].

In the liver, a pre-metastatic niche is established by VEGFR+ bone marrow progenitors before the arrival of tumor cells [21]. In fact, the initial events during the development of metastasis are VEGF-dependent [22]. Once the metastatic cancer cells survive in the new environment, they undergo colonization before the onset of the final process of malignancy. In general, a tumor requires angiogenesis to grow beyond 1-2 mm in size. In the initial pre-vascular phase, the size of the tumor does not exceed a few millimeters, but, neo-vascularization results in rapid growth of the tumor. Vascular endothelial growth factor (VEGF) is a key regulator of angiogenesis, which stimulates endothelial proliferation and migration, inhibits endothelial apoptosis, and increases vascular permeability and vasodilatation [23]. VEGF-targeting therapy has shown significant benefits in the treatment of metastatic breast cancer [24, 25]. In conclusion, based on the genetic interaction network, we identified MAPK, NF-kappa B and VEGF signaling pathways as key regulators of breast cancer liver metastasis.

MATERIALS AND METHODS

Microarray dataset resources

Microarray dataset with the accession number GSE62598 was downloaded from Gene Expression Omnibus (GEO). In this study, the authors examined if the propensity of breast cancer cells to metastasize to liver was associated with distinct patterns of immune cell infiltration [26]. Total RNA was extracted from 4T1 parental and individual metastatic sub-populations. The mRNA array was performed on Agilent-014868 Whole Mouse Genome Microarray 4×44k G4122F platform.

Analysis of differentially expressed genes

The gene expression profiles of metastatic tumor cells versus disseminated tumor cells were normalized by log10 transformation after normalization. Then, Significance Analysis of Microarrays software (SAM, http://statweb.stanford.edu/~tibs/SAM/) was used to produce a cluster of up- or down-regulated genes [27].

Genetic interaction network construction

Genetic interaction network was constructed using the String consortium database (http://string-db.org/). In addition, to identify the pathways involved Gene Ontology consortium (GO, http://www.geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/) functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov/).

Statistical analysis

According to a previous publication [28], gene expression was considered significant if the threshold of false discovery rate (FDR) ≤ 5% and fold change ≥ 2. For GO and KEGG enrichment analysis, biological process, molecular function and signaling pathways, p ≤ 5% was considered significant.

Author contributions

All authors contributed towards data analysis, drafting and revising the paper and agree to be accountable for all aspects of the work.

ACKNOWLEDGMENTS

We thank Gene Expression Omnibus (GEO), Significance Analysis of Microarrays (SAM), and String databases for making their data readily available to the scientific community.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

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