Analysis of Global Gene Expression Profile of Human Adipose Tissue Derived Mesenchymal Stem Cell Cultured with Cancer Cells

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Analysis of Global Gene Expression Profile of Human Adipose Tissue Derived Mesenchymal Stem Cell Cultured with Cancer Cells

Jong Myung Kim

^1,2,3

, Ji Min Yu

^2,3

, Yong Chan Bae

⁴

and Jin Sup Jung

^2,3,5

*

1

MD-PhD program, School of Medicine, Pusan National University, Yangsan 626-870, Korea

2

Department of Physiology, School of Medicine, Pusan National University, Yangsan 626-870, Korea

3

Medical Research Center for Ischemic Tissue Regeneration, School of Medicine, Pusan National University, Yangsan 626-870, Korea

4

Department of Plastic Surgery, School of Medicine, Pusan National University, Yangsan 626-870, Korea

5

Medical Research Institute, Pusan National University, Yangsan 626-870, Korea

Received February 17, 2011 /Accepted March 15, 2011

Mesenchymal stem cells (MSC) are multipotent and can be isolated from diverse human tissues in- cluding bone marrow, fat, placenta, dental pulp, synovium, tonsil, and the thymus. They function as regulators of tissue homeostasis. Because of their various advantages such as plasticity, easy isolation and manipulation, chemotaxis to cancer, and immune regulatory function, MSCs have been consid- ered to be a potent cell source for regenerative medicine, cancer treatment and other cell based ther- apy such as GVHD. However, relating to its supportive feature for surrounding cell and tissue, it has been frequently reported that MSCs accelerate tumor growth by modulating cancer microenvironment through promoting angiogenesis, secreting growth factors, and suppressing anti-tumorigenic immune reaction. Thus, clinical application of MSCs has been limited. To understand the underlying mecha- nism which modulates MSCs to function as tumor supportive cells, we co-cultured human adipose tissue derived mesenchymal stem cells (ASC) with cancer cell lines H460 and U87MG. Then, ex- pression data of ASCs co-cultured with cancer cells and cultured alone were obtained via microarray.

Comparative expression analysis was carried out using DAVID (Database for Annotation, Visualization and Integrated Discovery) and PANTHER (Protein ANalysis THrough Evolutionary Relationships) in divers aspects including biological process, molecular function, cellular component, protein class, disease, tissue expression, and signal pathway. We found that cancer cells alter the ex- pression profile of MSCs to cancer associated fibroblast like cells by modulating its energy metabo- lism, stemness, cell structure components, and paracrine effect in a variety of levels. These findings will improve the clinical efficacy and safety of MSCs based cell therapy.

Key words : Mesenchymal stem cell (MSC), cancer, MSC-cancer interaction, comparative expression analysis, cancer associated fibroblast

*Corresponding author

*Tel：+82-51-510-8071, Fax：+82-51-510-8076

*E-mail : jsjung@pusan.ac.kr

Introduction

Mesenchymal stem cells (MSC) are a population of pheno- typically heterogeneous cells that can be isolated from many easily accessible tissues such as adipose tissue, umbilical cord, placenta, and bone marrow and have ability to differ- entiate into a variety of mesodermal lineage, including adi- pocyte, osteoblast, chondrocyte, muscle cells and pericyte and its plasticity has been expanded to non-mesodemal line- age such as neural cells [38,42]. MSCs do not express HLA class II antigens, or the accessory molecules CD40, CD80 and CD86, required for immune cell activation and, thus, histo- compatibility matching is not necessary for therapeutic ap-

plication [21].

Because of its differentiation potential and relatively easy method for isolation, MSCs have been considered as ideal source of cell therapy for various human diseases [1,37].

However, especially in the field of cancer treatment, MSCs play a role as a flip side of the coin. It not only can deliver anticancer drug or be easily manipulated to introduce anti- tumor effect to cancer but also promotes malignancy by functioning as a tumor supportive stromal cells. The mi- gratory ability of MSCs to tumor sites has inspired inves- tigation into the possibility of using these cells as genes and anticancer agent delivery vesicle [45,46]. Furthermore, in contrast to other stem cells or normal stromal cells, naive MSCs have been thought to possess intrinsic antineoplastic properties [22].

On the other hand, in normal tissues, MSCs provide mi-

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croenvironment to regulate and support the tissue resident cells like hematopoietic stem cells in bone marrow [10,48].

Theses supportive features of MSCs also can be recapitulated in cancer microenvironment. MSCs regulate angiogenesis, inflammation, and homeostasis of tumor niche and favor tu- mor progression [3,52]. In spite of its protumorigenic effects, MSCs are still therapeutically useful cell source. To utilize and maximize the therapeutic efficacy of MSCs, the dark side of the coin should be revealed and deeply understood.

In this study, to get insight how cancer cells manipulate MSCs to utilize its tumor promoting activity, we obtained gene expression data from adipose tissue derived MSC (ASC) co-cultured with cancer cells through whole genome expression analysis using gene chip assay. Differentially ex- pressed transcripts compared to those of ASC cultured alone were analyzed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) version 6.7 and Protein ANalysis THrough Evolutionary Relationships (PANTHER) version 7 [18,31]. Expression profiles of ASCs were categorized to functionally related groups in diverse level of cell and molecular biology and its significances were interpreted.

Materials and Methods Culture of ASCs and cancer cell lines

The ASCs were isolated in accordance with the methods described in previous studies [26]. All protocols involving human subjects were approved by the Institutional Review Boards (IRB) of Pusan National University. After giving in- formed consent, adipose tissues were acquired from in- dividuals undergoing abdominoplasty. In order to isolate ASCs, the adipose tissues were washed thoroughly with equal volumes of phosphate-buffered saline (PBS), and the tissues were digested for 30 minutes at 37°C using 0.075%

collagenase. Enzyme activity was neutralized using α -modified Eagles medium (α-MEM, Invitrogen, Carlsbad, CA), containing 10% fetal bovine serum (FBS), then centri- fuged for 10 minutes at 1,200× g in order to obtain the pellets. The pellets were filtered through 100-µm nylon mesh in order to remove cellular debris, then incubated overnight at 37°C/5% CO

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in control medium (α-MEM, 10% FBS, 100 units/ml penicillin and 100 µg/ml streptomycin). After in- cubation, the plates were extensively washed in PBS in order to remove residual non-adherent red blood cells. The media was changed every 3 days, and the cells were split upon

achieving 70% confluence. ASCs were used at passage 3-10 for the experiments. U87MG and H460 cells were commer- cially obtained (ATCC) and were cultured in DMEM con- taining 10% FBS.

Production and transduction with GFP-lentiviral vectors

Replication-defective lentiviruses were generated via the transient transfection of 293T cells using Lipofectamine Plus (Invitrogen, Carlsbad, CA), lentiviral GFP vector (pWPT- GFP), and a packaging mix (Invitrogen). After transfection, the virus was harvested at 48 and 72 hr and filtered through Millex-HV 0.45 µm PVDF filters (Millipore, Billerica, MA), then maintained at -80°C until use. U87MG and H460 cells were transduced via 6 hr of exposure to dilutions of viral supernatant in the presence of polybrene (5 µg/ml). The transduction efficiency was estimated by determining the percentage of GFP-positive cells 2 to 3 days later via fluo- rescent microscopy and flow cytometry.

Xenotransplantation of tumor cells

Balb/C nude mice at 7 weeks of age were purchased from the Laboratory Animal Center of Seoul, Korea. The animals were housed in micro-isolator cages under sterile conditions, and observed for at least 1 week in order to ensure proper health prior to the initiation of the study. Lighting, temper- ature, and humidity were centrally controlled and recorded daily. One million H460 cells were implanted subcuta- neously into the backs of the mice. After 3 days, 1×10

⁶

ASCs were injected into the tail vein or heart of the same mouse.

Animals were sacrificed 3 weeks after transplantation and tumor growth was evaluated.

Histology

Tumor samples were dissected from mice, and fixed over- night in 10% phosphate-buffered formalin, transferred to 70% ethanol, then embedded in paraffin. To analyze tumor morphology, the samples were sectioned for 10 successive layers at 5 mm intervals and stained with hematoxylin and eosin (H&E).

Co-culture experiments

For labeling of ASCs with CM-Dil (Molecular Probes,

Eugene, OR, USA), ASCs were incubated with 2 uM

CM-Dil for 5 min at 37°C and for an additional 15 min at

4°C. The cancer cells were transduced with GFP-lentivirus

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A B C

D E

Fig. 1. ASCs promote tumor growth

in vivo

. (A) Effect of intracardiac injection of ASCs on growth of H460 in nude mice. 3 days after subcutaneous transplantation of H460, ASCs were injected into heart of the same mice. After 3 weeks, tumors size were compared between H460 injected alone and H460 co-injected with ASCs. (B) Quantification of tumor weight of H460 injected alone and H460 co-injected with ASCs (intracardiac). Data represent means±SEM of three different experiments.

*

p

<0.05 compared with H460 injected alone. (C) Effect of intravenous injection of ASCs on growth of H460 in nude mice (mouse 1 and 2). 3 days after subcutaneous transplantation of H460, ASCs were injected into tail vein of the same mice.

After 3 weeks, tumors size were compared between H460 injected alone and H460 co-injected with ASCs. (D) Quantification of tumor weight of H460 injected alone and H460 co-injected with ASCs (intravenous). Data represent means±SEM of three different experiments. *

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<0.05 compared with H460 injected alone. (E) H&E staining of tumor derived from H460 or H460 co-injected with ASCs.

A B

Fig. 2. Co-culture of ASCs and U87MG cells. (A) Representative image of co-culture experiments. CM-Dil labeled ASCs were co-cultured with GFP positive U87MG cells. (B) ASCs (PE positive) were sorted from cancer cells (FITC positive) by FACS.

and GFP labeled cancer cells were sorted with a flow cy- tometry cell sorter (FACSaria, BD Biosciences) prior to co-culture. The ASCs were plated on 6-well plate with H460 or U87MG cells. ASCs co-cultured with cancer cells were harvested 48 hours after co-culture. The cells were washed twice in PBS containing 0.5% BSA, and ASCs cells were sorted by a flow cytometry cell sorter (BD FACSAria, BD Biosciences).

Microarray analysis

The ASCs were harvested after co-cultured with U87MG or H460 cells or alone and RNA was isolated using the Invitrogen RNA isolation kit (Invitrogen Corporation). For Illumina microarray analysis, samples were prepared and analyzed in Illumina laboratories by Illumina personnel.

Biotinylated cRNA was prepared using the Illumina RNA

Amplification Kit (Ambion, Inc., Austin, TX) according to

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Table 1. List of differentially regulated genes Genelist highly (>4-fold) up-regulated

Symbol Definition Fold

(H460) Fold

(U87MG) Avg.

AKR1B10 aldo-keto reductase family 1, member B10 (aldose reductase) (AKR1B10), mRNA. 124.23 10.56 67.40 LOC144501 hypothetical protein LOC144501 (LOC144501), mRNA. 34.25 38.16 36.21 KYNU kynureninase (L-kynurenine hydrolase) (KYNU), transcript variant 1, mRNA. 23.68 33.88 28.78

LAMA5 laminin, alpha 5 (LAMA5), mRNA. 24.85 26.12 25.48

OLFML2A olfactomedin-like 2A (OLFML2A), mRNA. 19.79 17.68 18.73

TNFSF7 tumor necrosis factor (ligand) superfamily, member 7 (TNFSF7), mRNA. 19.02 15.00 17.01

TNNT1 troponin T type 1 (skeletal, slow) (TNNT1), mRNA. 4.45 20.21 12.33

SPP1 secreted phosphoprotein 1 (osteopontin, bone sialoprotein I, early T-lymphocyte

activation 1) (SPP1), mRNA. 20.32 4.18 12.25

COL4A5 collagen, type IV, alpha 5 (Alport syndrome) (COL4A5), transcript variant 3, mRNA. 17.55 6.42 11.98 DNER delta-notch-like EGF repeat-containing transmembrane (DNER), mRNA. 8.56 11.44 10.00 TM4SF1 transmembrane 4 L six family member 1 (TM4SF1), mRNA. 8.79 9.45 9.12

TMEM118 transmembrane protein 118 (TMEM118), mRNA. 8.71 9.48 9.10

E2F2 E2F transcription factor 2 (E2F2), mRNA. 4.22 11.50 7.86

CYP26B1 cytochrome P450, family 26, subfamily B, polypeptide 1 (CYP26B1), mRNA. 5.18 10.45 7.81

TGFB3 transforming growth factor, beta 3 (TGFB3), mRNA. 5.50 9.45 7.47

IL6 interleukin 6 (interferon, beta 2) (IL6), mRNA. 10.06 4.01 7.04

CD82 CD82 antigen (CD82), transcript variant 2, mRNA. 4.98 8.80 6.89

COL15A1 collagen, type XV, alpha 1 (COL15A1), mRNA. 7.27 5.99 6.63

SFRP2 secreted frizzled-related protein 2 (SFRP2), mRNA. 5.73 7.08 6.41

CDCA7 cell division cycle associated 7 (CDCA7), transcript variant 2, mRNA. 4.86 7.71 6.29

AIM2 absent in melanoma 2 (AIM2), mRNA. 5.96 6.27 6.12

TDO2 tryptophan 2,3-dioxygenase (TDO2), mRNA. 4.81 7.34 6.07

RASD1 RAS, dexamethasone-induced 1 (RASD1), mRNA. 4.81 6.78 5.79

PTGES prostaglandin E synthase (PTGES), mRNA. 6.95 4.28 5.61

C21orf63 chromosome 21 open reading frame 63 (C21orf63), mRNA. 5.15 5.70 5.42 ASS argininosuccinate synthetase (ASS), transcript variant 1, mRNA. 5.76 4.88 5.32

KIAA1598 KIAA1598 (KIAA1598), mRNA. 5.17 4.01 4.59

Up-regulated gene list;

EZH2, LASS6, APOE, SLC27A5, KLF9, RBP1, WISP1, GSTM3, OCIAD2, SNRPF, CYP2R1, AHCY, C10orf90, PYGL, JAG1, SH3PXD2B, C20orf31, PFKFB4, PSD3, SEMA4D, PRIM1, EGR2, CTXN1, EPPB9, NR1H3, ALDH2, NETO2, MAOA, RAB7L1, SLC6A15, PLXDC1, APCDD1, EYA4, DLX1, LITAF, KLHL3, PRR7, TMEPAI, MGST2, ITPKA, SH3RF2, TSPAN13, MTHFD2L, SOCS3, LOC389289, PLA2G4A, PITPNM1, SHMT1, PTGER4, CYFIP2, ABCA8, HES6, HMFN0839, LOC401233, TMEM51, HSD17B8, ABCA1, SLC25A37, URP2, GCHFR, E2F5, TNFRSF21, APOC1, PDGFRB, TFAP2C, SCD, C1QTNF6, PLTP, CD82, PHKA1, PLAU, MLPH, ATP6V0E2L, GPR56, IGF2BP3, CYGB, NEIL3, RNF144, VGF, IL24, TMEM100, HSP90B1, TNFAIP2, C10orf58, ZC3H12A, HPCAL1, HEYL, SPON1, CHRNA5, IL11, COL7A1, TNFRSF19, IMPA2, SLC44A1, HYAL1, CFD, ZIC2, GBP2, HES1, ITPR1, EGR1, C9orf140, LIF, TNS3, SERPIND1, E2F2, EPHX1, C9orf58, HYOU1, TNNT1, CCL3, SNF1LK, HSPA5, RASD1, TDO2, CDCA7, CD82, ETV4, CCL20, C21orf63, KIAA1598, CYP26B1, MLLT11, EXO1, PDIA4, FLJ14213, TGFB3, CMTM8, SFRP2, HEY1, ASS, AIM2, DNAJC12, CXCL2, PRG1, UNG2, NMB, PTGES, ANKRD38, COL15A1, QPCT, RSPO3, DNER, TMEM118, TM4SF1, DKFZP686A01247, CH25H, ALPL, KRT18, SCARB1, IL6, PDE4D, RAB20, SLC39A8, AKR1C3, DNAJC12, PIR, RGS2, CPS1, COL4A5, TNFSF7, OLFML2A, SPP1, KYNU, LAMA5, RGS16, LOC144501, EEF1A2, AKR1B10,

the manufacturer's directions starting with ~100 ng total RNA. Samples were purified using the RNeasy kit (Qiagen, Valencia, CA). Hybridization to the Sentrix HumanRef-8 Expression BeadChip (Illumina, Inc., San Diego, CA), wash- ing and scanning were performed according to the Illumina Bead Station 500× manual (revision C). Two Bead Chips were used, each one containing eight arrays, so that each

dilution series of six samples was run on an individual

BeadChip. Three independent sets for each of the ex-

perimental conditions were analyzed to minimize intra-

sample variation. Transcripts were considered increased or

decreased only if the levels changed by greater than 2-fold

with p<0.0001 for up-regulation and p>0.9997 for down-reg-

ulation to enhance the significance. Comparative analyses

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Table 1. Continued

Genelisthighly (>4-fold) down-regulated

Symbol Definition Fold

(H460) Fold

(U87MG) Avg.

TXNIP thioredoxin interacting protein (TXNIP), mRNA. -22.52 -12.78 -17.65

GABBR2 gamma-aminobutyric acid (GABA) B receptor, 2 (GABBR2), mRNA. -6.98 -20.28 -13.63

KRTHA4 keratin, hair, acidic, 4 (KRTHA4), mRNA. -15.57 -7.53 -11.55

DKK1 dickkopf homolog 1 (Xenopus laevis) (DKK1), mRNA. -8.62 -13.84 -11.23

SERPINB2 serpin peptidase inhibitor, clade B (ovalbumin), member 2 (SERPINB2), mRNA. -4.48 -17.39 -10.94 CITED2 Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminal domain, 2

(CITED2), mRNA. -4.83 -9.39 -7.11

UNQ1940 HWKM1940 (UNQ1940), mRNA. -5.15 -8.59 -6.87

LOC388610 hypothetical LOC388610 (LOC388610), mRNA. -6.82 -6.79 -6.81

EDG1 endothelial differentiation, sphingolipid G-protein-coupled receptor, 1 (EDG1), mRNA. -4.54 -6.24 -5.39

KIAA1913 KIAA1913 (KIAA1913), mRNA. -4.76 -5.61 -5.18

DDAH1 dimethylarginine dimethylaminohydrolase 1 (DDAH1), mRNA. -5.91 -4.19 -5.05

WNK4 WNK lysine deficient protein kinase 4 (WNK4), mRNA. -5.21 -4.75 -4.98

URB steroid sensitive gene 1 (URB), transcript variant 2, mRNA. -5.35 -4.54 -4.95 TEK TEK tyrosine kinase, endothelial (venous malformations, multiple cutaneous and

mucosal) (TEK), mRNA. -4.71 -4.39 -4.55

F3 coagulation factor III (thromboplastin, tissue factor) (F3), mRNA. -4.38 -4.32 -4.35 Down-regulated gene list;

TXNIP, KRTHA4, MARCH4, DKK1, CLDN11, GABBR2, LOC388610, DDAH1, URB, CYR61, WNK4, UNQ1940, NEXN, FOXD1, CITED2, CAV1, KIAA1913, TEK, OXTR, EDG1, SERPINB2, IGFBP3, F3, FMN2, LFNG, ARHGDIB, TIPARP, GLIPR1, SLIT2, ANPEP, EPHB1, LPXN, URB, TNFRSF11B, BAIAP2L2, GRAMD3, ARRDC4, KCNJ8, EBF3, DOK5, ATP8B1, ANGPTL4, NAV3, ADAMTS1, ASAM, LOC387882, NPAL3, TBC1D2, FZD8, PCDH18, RGMB, ACOT11, LOC90355, DOC1, PITX2, KRT7, LAYN, DOC1, UAP1, HAS2, CRIM1, ELL2, HYI, ATOH8, PDCD1LG2, HOXC8, PVRL3, MGC13057, ABHD5, PHACTR2, NGFB, RRAS2, CXCL12, WNT5B, LRRC8C, OSR1, LOC51334, HIST1H4H, Bles03, AK5, RAFTLIN, BNC1, CNTN3, SLC9A7, DSP, CCIN, HAK, RALGPS2, FCRLM2, SEMA7A, MGC17624, DUSP14, MT1M, TPM1, CDC42EP2, TMEM47, SLITL2, SHRM, VEGFC, DNASE1L3, RPS6KA2, OXCT2, EGFR, TLN2, ZNF442, PLEK2, GADD45A, MGC26963, CXCL12, S100A13, HLX1, HERC3, ARID5B, FLJ22471, HOXA9, BCAR3, CAV2, SAMD4A, TNFRSF12A, CDH2, ABCC9

of expressed genes that were either up-regulated or down-regulated under various experimental conditions were carried out using proprietary software from Database for Annotation, Visualization and Integrated Discovery (DAVID) version 6.7 and Protein ANalysis THrough Evolutionary Relationships (PANTHER) version 7 tools.

Statistical analysis

All results are presented as mean±SEM. Comparisons be- tween groups were analyzed via Student’s t-tests. P values

<0.05 were considered statistically significant.

Results ASCs promote tumor growth in vivo

To evaluate the effect of ASCs to tumor growth, ASCs were injected into the heart or tail vein of nude mice that

had received subcutaneous transplantation of cancer cells,

Fig. 3. Schematic diagram of outline of this experiment. Detail explanation is described in the paragraph.

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A-1 A-2

B-1 B-2

Fig. 4. Biological processes of differentially regulated genes. Top 10 for fold enrichment were shown. Biological processes of input genes were obtained from DAVID (4A) or PANTHER (4B). The numbers in parenthesis indicate (gene numbers/fold enrichment). The area of individual slices of pie chart represents fold enrichment of the particular biological process. Fold enrichment for a term is defined as user’s gene numbers divided by expected gene numbers in the term based on the human genome.

For example, 10% of user’s input genes are kinase versus 1% of genes in human genome are kinase. Thus, the fold enrichment is tenfold.

p

-values of all data are less than 0.05.

H460. After three weeks of injection, the weight of tumor was measured. The volume and weight of tumors in mice, which had a co-injection of ASCs, were bigger and heavier than those of control mice, which were injected with cancer cell alone (Fig. 1).

Evaluation of the gene expression profile of ASCs co-cultured with cancer cells

To analysis the influence of cancer cells to global gene expression of ASCs, CM-Dil labeled ASCs were co-cultured with GFP-positive H460 or U87MG. After three days of co-culture, ASCs were sorted by FACS (Fig. 2) and gene ex- pression was quantified using microarray analysis in com- parison with control ASCs, which were not co-cultured with cancer cells. We only focused on the genes, which had more than 2-fold difference in expression, compared to control and

had same direction of expressional changes in both ASCs co-cultured with H460 (ASC-H) and ASCs co-cultured with U87MG (ASC-U) to study the general characteristics of ex- pression profile. 511 genes were up-regulated in ASC-H and 740 genes were up-regulated in ASC-U. Among them, 168 genes were commonly increased in both ASC-H and ASC-U.

323 genes were down-regulated in ASC-H and 714 genes were down-regulated in ASC-U. Among them, 121 genes were commonly decreased in both ASC-H and ASC-U. Table 1 shows the list of these commonly expressed genes.

Comparative analysis of these up and down-regulated genes was performed using DAVID or PANTHER (Fig. 3).

Biological and functional categories of differentially expressed genes

Biological processes of up-regulated genes with high fold

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A-1 A-2

B-1 B-2

Fig. 5. Molecular functions of differentially expressed genes. Molecular functions of input genes were obtained from DAVID (5A) or PANTHER (5B). The numbers in parenthesis indicate (gene numbers/fold enrichment). The area of individual slices of pie chart represents percentage of the genes belonging to the particular molecular function.

p

enrichment included tryptophan catabolic process to ace- tyl-CoA, lipid and cholesterol metabolism, amino acid me- tabolism, macrophage activation, angiogenesis, carbohydrate transport, response to toxin, and CDC42 signal transduction.

In terms of down-regulated genes, mesoderm and embry- onic development, which is characteristics of stemness, membrane raft formation, potassium ion import, PDGF re- ceptor pathway, response to progesterone, angiogenesis, mi- crotubule polymerization, nitric oxide synthesis, and cell-matrix adhesion, were significantly enriched. It is note- worthy that angiogenesis belongs to both up and down-regulated gene groups (Fig. 4). Molecular functions of differentially regulated genes were also analyzed. Lipid and phospholipid binding, cytokine activity, iron binding, ECM structure constituent, and phosphatase activity were increased. In contrast, tyrosine kinase activity, cytoskeleton structural constituent, IGF binding, syntaxin and SNARE binding, and ion channel activity were decreased.

Interestingly, intracellular (or cytoskeletal) and extracellular structural components were differentially regulated (Fig. 5).

Cellular components and protein class of differentially expressed genes

Analysis results of cellular components showed that ex-

tracellular components were up-regulated, whereas cytoske-

letal (or intracellular) components were down-regulated

(Fig. 6A). In addition, collagen which is produced by fibro-

blast was most highly enriched (fold=9.7) molecule in

up-regulated group on DAVID analysis. It should be noted

that cell adhesion and junctional molecules were major cel-

lular component of down-regulated genes (Fig. 6B). Protein

classes of differentially regulated genes were consistent with

results from biological processes. Biological processes of

up-regulated genes showed that energy metabolic processes

such as lipid and carbohydrate transport, catalytic process

of tryptophan to acetyl-CoA, which are needed for cell pro-

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A

B-1 B-2

Fig. 6. Cellular components of differentially expressed genes. (A) Cellular components of input genes obtained from PANTHER.

(B) Cellular components of input genes obtained from DAVID. The numbers in parenthesis indicate (gene numbers/fold enrichment). The area of individual slices of pie chart represents percentage of the genes belonging to the particular cellular component.

p

liferation with DNA replication, were enriched and genes related to response to toxin were also increased (Fig. 4). In protein class, primase and DNA glycosylase, which are re- lated to DNA replication and repair, were up-regulated and Hsp70 family chaperon, mediator of response to toxin and stress, was also increased. Cytoskeletal proteins were de- creased in protein class (Table 2). This is same results from cellular components analysis.

Tissue and disease related to differentially expressed genes

To determine which tissues are closely related to differ- entially regulated genes, we used Serial Analysis of Gene Expression (SAGE) of Cancer Genome Anatomy Project (CGAP) in National Cancer Institute (NCI) through DAVID.

SAGE displays the relative expression of a given gene in normal and malignant tissues of the human body.

Interestingly, normal breast tissues from breast cancer pa- tients were included in highly enriched terms in both up and down-regulated gene groups (Table 3). Diseases linked to the up-regulated gene group include dysfunction of cho- lesterol and lipid metabolism, brain aging, chronic ob- structive pulmonary disease (COPD) and cardiovascular disease. It is worthy of notice that only one disease category, bone density fracture risk, was acquired with very high fold enrichment in down-regulated gene group (Table 4).

Signal transduction pathway related to differentially expressed genes

Signal pathway of up-regulated genes included DNA rep-

lication, and important pathways for cell division and pu-

rine/pyrimidine production such as arginine and ser-

ine/glycine biosynthesis. Notch signaling pathway, which

is a critical signal cascade for cell-cell communication and

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Table 2. Protein class of differentially regulated genes PANTHER (UP)

PANTHER Protein Class Gene numbers Fold P-value

primase 2 200.0 3.19E-05

DNA glycosylase 2 20.0 4.34E-03

Hsp70 family chaperone 2 20.0 5.06E-03

chemokine 3 7.7 7.08E-03

interleukin superfamily 2 6.9 3.44E-02

cytokine 8 6.3 5.03E-05

hydroxylase 2 5.7 4.94E-02

phosphatase 8 4.3 5.99E-04

basic helix-loop-helix transcription factor 4 4.2 1.65E-02

chaperone 4 3.8 2.14E-02

membrane-bound signaling molecule 4 3.7 2.30E-02

peptide hormone 5 3.7 1.23E-02

growth factor 4 3.0 4.50E-02

signaling molecule 22 2.8 1.03E-05

G-protein modulator 6 2.7 2.56E-02

oxidoreductase 11 2.5 5.16E-03

PANTHER (DOWN)

PANTHER Protein Class Gene numbers Fold P-value

neurotrophic factor 1 25.0 4.25E-02

actin binding motor protein 1 20.0 4.77E-02

calcium channel 2 11.8 1.26E-02

growth factor 4 4.5 1.28E-02

transcription cofactor 5 3.6 1.30E-02

cytokine receptor 4 3.4 2.92E-02

actin family cytoskeletal protein 4 3.3 3.33E-02

homeobox transcription factor 4 3.2 3.86E-02

helix-turn-helix transcription factor 4 3.2 3.86E-02

cytoskeletal protein 7 2.9 1.05E-02

protein kinase 8 2.9 6.77E-03

ion channel 5 2.7 3.87E-02

signaling molecule 13 2.5 2.16E-03

kinase 9 2.4 1.18E-02

receptor 14 2.4 2.09E-03

cancer cell progression, was most significantly enriched (p=4.44E-05) in un-regulated genes. Ligand of notch path- way, JAG1, and target genes of notch signal such as HEY1, HES6, HES1, and HEYL were simultaneously activated. In addition, tumor suppressor p53 pathway and TGF-β signal related genes, which are important regulator of apoptosis and cell cycle [4,7], were included in down-regulated gene group (Table 5).

Discussion

Cancer associated fibroblasts (CAF) are spindle shaped mesenchymal cell and one of the important components of

the cancer niche. CAF facilitates the growth and invasion of cancer cells by modulating tumor microenvironment via direct cell-cell contact or paracrine modulation. The origins of CAF are diverse including resident normal fibroblast, epi- thelial cell, MSCs, endothelial cell and pericyte [18,34,51].

Recently, MSCs have been reported as a critical source for

the cancer associated fibroblast [2]. However, molecular

mechanism underlying tumor supportive features of MSCs

are not well understood. In this study, we used microarray

and comparative gene expression analysis by a differential

clustering approach to investigate the influence of cancer

cells on phenotypic changes of MSCs leading promotion of

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Table 3. Tissue expression of diffenretially expressed genes (Top 10 fold enrichment) 　 Category: CGAP_SAGE_QUARTILE (UP)

Term Gene# Genes (UP) Fold P-value

1983:white blood cells_plaque macro-

phage_3rd 21

EGR1, HYAL1, CCL3, EGR2, LITAF, PTGER4, SOCS3, CXCL2, APOC1, TMEM51, ABCA1, RGS2, CCL20, SLC39A8, CFD, RAB20, TNFAIP2, PLAU, PLTP, NR1H3, SPP1

4.5 2.95E-08

650:mammary gland_ductal carcinoma in situ, extensive, gradeIII, Her2+_3rd

19

EGR1, ABCA8, HYAL1, CCL3, KLF9, C21ORF63, SOCS3, CXCL2, COL15A1, RGS16, HES1, HSP90B1, RGS2, SFRP2, SLC39A8, TM4SF1, CFD, RASD1, MGST2

3.1 2.98E-05

183:mammary gland_normal breast

stroma_3rd 21

EGR1, ABCA8, CCL3, EGR2, LITAF, SOCS3, CXCL2, APOC1, OLFML2A, CPS1, QPCT, PITPNM1, KRT18, RGS2, COL7A1, SFRP2, PDGFRB, TM4SF1, CFD, RASD1, PLTP

3.1 1.12E-05

1984:white blood cells_plaque macro-

phage_3rd 21

EGR1, CCL3, EGR2, LITAF, PTGER4, SOCS3, CXCL2, APOC1, TMEM51, ABCA1, RGS2, CCL20, SLC39A8, CFD, RAB20, TNFAIP2, PLAU, PLTP, GBP2, NR1H3, SPP1

3.0 1.48E-05 647:mammary gland_normal breast

tissue from a breast cancer patient (corresponding to IDC7)_3rd

16

EGR1, HYAL1, CCL3, PTGER4, SOCS3, CXCL2, COL15A1, TMEM51, HES1, HSP90B1, RGS2, SFRP2, SLC39A8, TM4SF1, CFD, RASD1

3.0 2.62E-04 683:mammary gland_recurrent phyll-

odes tumor, malignant, high grade, poorly differentiated_3rd

14 HYAL1, CCL3, CXCL2, NMB, RGS16, TNS3, HSP90B1,

WISP1, COL7A1, RGS2, PTGES, SLC39A8, PDGFRB, SPP1 2.9 9.04E-04 676:mammary gland_Grade I, ER+,

PR+, Her2- invasive ductal carcino- ma_3rd

17

EGR1, HYAL1, CCL3, LOC401233, KLF9, SOCS3, CXCL2, JAG1, RGS16, HES1, WISP1, C1QTNF6, RGS2, SFRP2, PDGFRB, TNFRSF19, PLAU

2.9 2.20E-04

646:mammary gland_invasive breast cancer ER+, PR+, Her2-, grade II_3rd 16

EGR1, CCL3, PTGER4, KLF9, MLPH, CXCL2, COL15A1, LIF, HSP90B1, C1QTNF6, RGS2, SFRP2, SLC39A8, TM4SF1, RAB20, RASD1

2.8 4.75E-04

420:mammary gland_normal breast tissue from a breast cancer patient (corresponding to IDC7)_3rd

24

EGR1, KYNU, CCL3, LITAF, PTGER4, KLF9, C21ORF63, SOCS3, CXCL2, COL15A1, EPHX1, TMEM51, RGS16, ITPR1, HES1, QPCT, HSP90B1, RGS2, SFRP2, SLC39A8, TM4SF1, CFD, RAB20, RASD1

2.7 2.50E-05

703:mammary gland_extensive LCIS_3rd 13 EGR1, HYAL1, CCL3, PTGER4, MLPH, RGS16, HSP90B1,

COL7A1, RGS2, SLC39A8, TM4SF1, PLAU, SPP1 2.6 0.004366986 645:mammary gland_invasive breast

cancer, ER+, PR+, Her-, grade II_3rd 15

EGR1, HYAL1, CCL3, LITAF, MLPH, CXCL2, IL11, LIF, PITPNM1, HSP90B1, DNAJC12, CYP2R1, TM4SF1, RASD1, ETV4

2.4 0.004200298

malignancy. Differentially altered gene expression in ASCs

co-cultured with cancer cells compared to transcripts of ASCs cultured alone were analyzed in diverse aspects in- cluding biological process, molecular function, cellular com- ponent, protein class, tissue expression, disease, and signal pathway. Our data presented various potential evidences that cancer cells manipulated MSCs to accelerate the tumor progression. Highly up-regulated (>4-fold) genes in ASCs co-cultured with cancer cells included laminin (LAMA5) and collagen (COL4A5, COL15A1) (Table 1) which are important ECM components to constitute physical microenvironment

for cancer cell survival and progression [16,19,23,33].

Phenotypic and functional changes of ASCs to CAF-like cells

were shown in analysis data of differentially expressed

genes. The characteristics of fibroblast such as collagen pro-

duction, weakness of cell-cell adhesion, enrichment of ECM

structural components were obvious in expression profile of

ASCs co-cultured with cancer cells. In the same context, de-

crease of mesodermal lineage development and membrane

lipid raft formation, which is important for maintaining mul-

tipotency [25,28], in biological processes could be explained

by loss of stemness of ASCs undergoing phenotypic changes

(11)

Table 3. Continued

Category: CGAP_SAGE_QUARTILE (DOWN)

Term Gene# Genes (DOWN) Fold P-value

647:mammary gland_normal breast tissue from a breast cancer patient

(corresponding to IDC7)_3rd 13 UAP1, CAV1, TIPARP, CXCL12, ELL2, VEGFC, DUSP14,

TEK, HAS2, GADD45A, ANGPTL4, BCAR3, CYR61 4.0 7.07E-05 203:prostate_normal prostate epi-

thelium, senescent cells_3rd 16 TXNIP, CAV2, CAV1, UAP1, WNT5B, PLEK2, TNFRSF12A, TIPARP, TPM1, ELL2, VEGFC, DUSP14, F3, BNC1,

GADD45A, ANGPTL4 3.5 2.92E-05

131:mammary gland_breast my-

oepithelium_3rd 18 FZD8, CAV1, UAP1, ARID5B, LRRC8C, OXTR, GRAMD3, TPM1, ELL2, DUSP14, F3, ACOT11, DSP, IGFBP3,

GADD45A, SAMD4A, TBC1D2, CYR61 3.5 9.92E-06

608:cartilage_Dedifferentiated chon-

drosarcoma lung metastasis_3rd 17 TXNIP, CAV1, PHACTR2, OXTR, ARRDC4, CDH2, CLDN11, TPM1, CITED2, VEGFC, TNFRSF11B, LAYN,

CNTN3, IGFBP3, GADD45A, ANGPTL4, BCAR3 3.4 2.24E-05 650:mammary gland_ductal carcino-

ma in situ, extensive, gradeIII,

Her2+_3rd 12 VEGFC, CAV1, UAP1, DUSP14, TEK, GADD45A, CXCL12,

SAMD4A, SLIT2, CYR61, BCAR3, ELL2 3.3 8.98E-04 420:mammary gland_normal breast

tissue from a breast cancer patient

(corresponding to IDC7)_3rd 16 CAV1, UAP1, WNT5B, TIPARP, ARRDC4, ELL2, VEGFC, CDC42EP2, DUSP14, TEK, HAS2, GADD45A, SAMD4A,

ANGPTL4, BCAR3, CYR61 2.9 2.50E-04

drosarcoma lung metastasis_3rd 19 CAV1, TNFRSF12A, TIPARP, ANPEP, CLDN11, CITED2, ELL2, LPXN, RGMB, TNFRSF11B, OSR1, LAYN, GLIPR1,

ATOH8, IGFBP3, FOXD1, ANGPTL4, ARHGDIB, CYR61 2.9 5.19E-05 646:mammary gland_invasive breast

cancer ER+, PR+, Her2-, grade II_3rd 9 VEGFC, UAP1, TIPARP, GADD45A, CXCL12, CITED2,

CYR61, BCAR3, ELL2 2.6 0.019074118

182:mammary gland_normal breast

organoid_3rd 14 FZD8, UAP1, CAV1, ARID5B, TIPARP, TPM1, SLIT2, ELL2,

CDC42EP2, DUSP14, KRT7, GADD45A, BCAR3, CYR61 2.6 0.002235451 9:brain_normal fetal astrocyte, GFAP

positive, cerebral cortex_3rd 17 CAV1, TLN2, OXTR, CDH2, CLDN11, TPM1, PDCD1LG2, MARCH4, CITED2, ELL2, GLIPR1, KRT7, F3, IGFBP3,

DDAH1, ANGPTL4, CYR61 2.6 5.95E-04

drosarcoma lung metastasis_3rd 14 UAP1, CAV1, ABHD5, TIPARP, HERC3, AK5, CITED2, VEGFC, TMEM47, CDC42EP2, TNFRSF11B, HAS2, FOXD1,

GADD45A 2.5 0.002831589

to fibroblast. In addition, disease states related to up-regu- lated genes included COPD in which fibrosis of air way is important pathogenesis of shortness of breath (Table 4) [43].

Our data also showed that lipid and cholesterol metabo- lism were highly up-regulated in ASCs co-cultured with can- cer cells (Fig. 4). Disease states related to up-regulated genes also indicated that abnormal lipid metabolism was closely related to phenotypic changes of ASCs affected by cancer cells (Table 4). This means that fat metabolites are important energy source for cancer cell progression supported by sur- rounding MSCs. Actually, it had been reported that cancer cells mainly utilize fat derivatives for its survival, and lipid metabolism is up-regulated in cancer patient [27,41].

It has been reported that cancer cells increases surround- ing fibroblast proliferation and enhances invasive properties of carcinoma cells through integrin signal [39,44]. Our data

also included the up-regulation of collagens which are major

ligands for integrin signal, and enrichment of cell pro-

liferation related processes such as expression of CDC42, de-

crease of p53 and TGF-beta related pathway, increase of en-

ergy metabolism, DNA replication and repair, and biosyn-

thesis for cell division (Fig. 4, Table 2, 4, 5). In addition,

Dudley et al [12] and Patocs et al [36] reported that attenu-

ation of the tumor suppressor p53 in stromal cells of tumor

microenvironment contributes to lymph node metastases

and decreased sensitivity to chemotherapy. Furthermore,

loss of TGF-beta signal in prostate stroma was correlated

with carcinogenesis of prostate epithelium [14]. Notably,

notch pathway was most significantly enriched (p=4.44E-05)

in over-expressed gene group. This indicates that cancer cell

promotes cell-cell interaction with MSCs to manipulate and

utilize it [17]. Simultaneous activation of notch signaling li-

(12)

Table 4. Disease list related to differentially expressed genes Genes (UP)

Category Term Gene# Genes (UP) Fold P-value

GENETIC_ASSOCIATION_

DB_DISEASE_CLASS METABOLIC 26

ALPL, APOC1, NMB, ABCA1, AKR1C3, WISP1, APOE, SCARB1, PLTP, NR1H3, SPP1, EGR1, SHMT1, IL6, SOCS3, SCD, MAOA, EPHX1, PDE4D, QPCT, HYOU1, PLA2G4A, RGS2, ALDH2, SLC27A5, PLAU

1.4 0.035078824

DB_DISEASE_CLASS CARDIOVASCULAR 22

ALPL, SHMT1, KYNU, IL6, EGR2, HPCAL1, AHCY, APOC1, TGFB3, EPHX1, PDE4D, ABCA1, CPS1, QPCT, RGS2, PTGES, APOE, ALDH2, SCARB1, PLAU, NR1H3, SPP1

1.5 0.039810518

DB_DISEASE cholesterol, HDL choles-

terol, LDL 4 APOE, SCARB1, ABCA1, PLTP 15.5 0.001729486

DB_DISEASE bone density 8 ALPL, QPCT, IL6, APOE, ALDH2, PDE4D,

PLAU, SPP1 3.5 0.006019936

DB_DISEASE chronic obstructive pul-

monary disease/COPD 5 IL6, APOE, EPHX1, PDE4D, IL11 4.3 0.026057004 GENETIC_ASSOCIATION_

DB_DISEASE cholesterol, LDL 3 APOE, APOC1, SCARB1 10.8 0.029528444

DB_DISEASE hypertension 9 KYNU, IL6, HPCAL1, RGS2, PTGES, APOE,

ALDH2, TGFB3, SPP1 2.3 0.034788894

DB_DISEASE

cholesterol cholesterol, HDL cholesterol, LDL

lipoprotein triglycerides 3 APOE, APOC1, ABCA1 9.3 0.038690047 GENETIC_ASSOCIATION_

DB_DISEASE Alzheimer's disease apo-

lipoprotein E levels 2 APOE, ABCA1 46.6 0.041832229

DB_DISEASE Brain aging 2 IL6, APOE 46.6 0.041832229

DB_DISEASE familial dysbetalipopro-

teinemia 2 APOE, APOC1 46.6 0.041832229

DB_DISEASE alcohol abuse; smoking

behavior 3 GSTM3, ALDH2, EPHX1 8.7 0.043625186

DB_DISEASE triglycerides 3 APOE, SCARB1, ABCA1 8.2 0.048780974

OMIM_DISEASE Common variants at 30 loci contribute to poly-

genic dyslipidemia 4 APOE, APOC1, ABCA1, PLTP 6.7 0.020274613 Genes (DOWN)

Category Term Gene# Genes (DOWN) Fold P-value

DB_DISEASE bone density fracture

risk 2 TNFRSF11B, SEMA7A 84.1 0.022622085

gand, JAG1 and downstream target genes including HEY1, HES6, HES1 and, HEYL implied that the autocrine activation of notch pathway in MSCs were as important as its paracrine effects to cancer cells for tumor supportive function of MSCs (Table 5).

Immune cells have been considered as key regulator of tumor microenvironment. Especially, tumor associated mac- rophage (TAM) and regulatory T-cells are critical because

they support tumor progression by regulating immune re-

sponse, inflammation, angiogenesis, and cancer invasion

[9,50]. Our data showed that ASCs regulated by cancer cells

express macrophage activation related genes and TGF-β

(Fig. 4, Table 1). TGF-β signaling related genes such as

RRAS2, FOXD1, and CITED2 [5,8,13] were decreased in

ASCs but expression of TGF-β itself was increased more

than 7-fold on average in our data and this might affect the

(13)

Table 5. Signal pathway of differentially expressed genes PANTHER (UP)

Pathway Gene# Gene (UP) Fold P-value

Arginine biosynthesis 2 ASS, CPS1 33.3 1.97E-03

Phenylethylamine degradation 2 ALDH2, MAOA 25.0 3.04E-03

Serine glycine biosynthesis 1 SHMT1 25.0 3.94E-02

Notch signaling pathway 5 JAG1,HEY1,HES6,HES1,HEYL 13.2 4.44E-05

5-Hydroxytryptamine degredation 2 ALDH2, MAOA 12.5 1.16E-02

DNA replication 2 PRIM1, SPP1 11.8 1.27E-02

Adrenaline and noradrenaline biosynthesis 2 MAOA, SLC6A15 7.7 2.78E-02

Inflammation mediated by chemokine and cyto-

kine signaling pathway 8 ITPR1,PLA2G4A 3.5 2.21E-03

Integrin signalling pathway 5 HSD17B8, COL15A1, COL4A5, COL7A1, CPS1 3.4 1.60E-02 Heterotrimeric G-protein signaling pathway-Gi al-

pha and Gs alpha mediated pathway 4 PYGL, RGS2, PHKA1, RGS16 3.0 4.58E-02

PANTHER (DOWN)

Pathway Gene# Gene (DOWN) Fold P-value

Axon guidance mediated by Slit/Robo 2 CXCL12, SLIT2 18.2 5.43E-03

Blood coagulation 2 F3, SERPINB2 7.7 2.84E-02

Cadherin signaling pathway 6 CAV2, PCDH18, FZD8, EGFR, WNT5B, CDH2 7.5 1.63E-04

Angiogenesis 7 CAV2, F3, FZD8, EPHB1, TEK, LPXN, WNT5B 6.7 8.95E-05

p53 pathway 3 TMEM47, GADD45A, IGFBP3 4.9 2.40E-02

Alzheimer disease-presenilin pathway 3 CAV2, FZD8, WNT5B 4.5 2.92E-02

TGF-beta signaling pathway 3 RRAS2, FOXD1, CITED2 3.8 4.49E-02

Wnt signaling pathway 6 CAV2, PCDH18, FZD8, WNT5B, DKK1, CDH2 3.5 7.85E-03

TGF-β signal of surrounding cells of ASCs. TGF-β converts effector T-cells, which attack cancer, into regulatory T-cells, which suppress the inflammatory reaction and favor tumor growth [6,47,49]. Interestingly, angiogenesis were enriched in both up and down-regulated gene groups. This result was consistence with our previous report in which vessel devel- opment of tumor cells co-injected with ASCs to nude mice was not different from those of tumor cells injected alone [52].

Bone fracture risk was the only proposed disease linked to down-regulated genes (Table 4). Osteoclast, which is key cell types dissolving bone matrix and decreasing bone den- sity, is activated by IL-6 potently, which is also up-regulated cytokine in myeloma and oral cancer niche [15,20,40]. IL-6 is a protumorigenic molecule which promotes cancer surviv- al by up-regulating survival related genes and confers tumor cells resistance to chemotherapeutic agents [11,24,30]. IL-6 was increased more than 7-fold on average in our data (Table 1). Lastly, analysis result of tissue expression showed that none-cancerous tissues surrounding breast cancer were highly enriched tissue types in both up and down-regulated gene groups (Table 3). This information further supported the possibility that features of MSCs co-cultured with cancer

cells are closely similar to cancer associated fibroblast.

Recently, the significance of targeting CAFs in cancer ther- apy has been increasingly emphasized in many reports [29,32].

In conclusion, our comparative expression data analysis show that cancer cells change the behavior of MSCs into cancer supportive cells by affecting its energy metabolism, stemness, cellular structure components, proliferation and secretory function in variety levels. Our understanding from this study could not only be applied to CAFs targeting can- cer therapy but also improve safety of MSCs utilizing cancer treatment and regenerative medicine.

Acknowledgement

This work was supported for two years by 2009 Pusan National University Research Grant.

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