Cathepsin S Mediates Gastric Cancer Cell Migration and Invasion via

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Cathepsin S Mediates Gastric Cancer Cell Migration and Invasion via a Putative Network of Metastasis-Associated Proteins Yang Yixuan,† Lim Shen Kiat,† Choong Lee Yee,† Lee Huiyin,† Chen Yunhao,† Chong Poh Kuan,† Ashktorab Hassan,‡ Wang Ting Ting,† Salto-Tellez Manuel,† Yeoh Khay Guan,§ and Lim Yoon Pin*,†,|,⊥ Cancer Science Institute of Singapore, National University of Singapore, 28 Medical Drive, Singapore 117456, Department of Medicine and Cancer Center, Howard University, Washington, D.C., Department of Medicine, National University of Singapore, Department of Biological Sciences, National University of Singapore, and Bioinformatics Institute, Agency for Science, Technology and Research Received May 18, 2010

Cancer progression is governed by multifaceted interactions of cancer cells with their microenvironment and one of these ways is through secreted compounds. Substances released by gastric cancer cells have not being profiled in a proteome-wide manner. ITRAQ-based tandem mass spectrometry was employed to quantify proteins secreted by HFE145 normal, MKN7 well-differentiated, and MKN45 poorly differentiated gastric cancer cell lines. The expression levels of 237 proteins were found to be significantly different between normal and cancer cells. Further examination of 16 gastric cell lines and 115 clinical samples validated the up-regulation of CTSS expression in gastric cancer. Silencing CTSS expression suppressed the migration and invasion of gastric cancer cells in vitro. Subsequent secretomics revealed that CTSS silencing resulted in changes in expression levels of 197 proteins, one-third of which are implicated in cellular movement. Proteome-wide comparative secretomes of normal and gastric cancer cells were produced that constitute a useful resource for gastric cancer research. CTSS was demonstrated to play novel roles in gastric cancer cell migration and invasion, putatively via a network of proteins associated with cell migration, invasion, or metastasis. Cathepsin S is member of a large group of extracellular proteases, which are attractive drug targets. The implicated role of CTSS in gastric cancer metastasis provides an opportunity to test existing compounds against CTSS for adjuvant therapy and/or treatment of metastatic gastric cancers. Keywords: gastric cancer • CTSS • cathepsin • protease • secretome • invasion • migration • proteomics

Introduction Despite decreasing incidence and mortality, gastric cancer remains the second leading cause of cancer-related deaths in the world.1 Although many oncogenes, tumor suppressors and Helicobacter pylori infection have been implicated in gastric cancer development, the molecular mechanisms underlying progression and metastasis of gastric cancer remain poorly understood. Cancer malignancy depends not only on its genotype but also on the complex interactions between cancer cells and other cell types in the tumor microenvironment. Such cell-cell communication is largely achieved through a broad spectrum of secreted substances including chemokines and cytokines.2 The class of secreted proteins, that is, secretome, is essential to the processes of differentiation, invasion, me* Correspondence: Lim Yoon Pin (e-mail: [email protected] or yoonpinlim@ hotmail.com). Cancer Science Institute of Singapore, Centre for Life Sciences, 28 Medical Drive, #02-14C, Singapore 117456. Tel: (65) 65161313. Fax: (65) 68739664. † Cancer Science Institute of Singapore, National University of Singapore. ‡ Howard University. § Department of Medicine, National University of Singapore. | Department of Biological Sciences, National University of Singapore. ⊥ Bioinformatics Institute, Agency for Science, Technology and Research. 10.1021/pr100492x

 2010 American Chemical Society

tastasis and angiogenesis of cancers.3 For example, peptidases such as Cathepsins and Matrix Metalloproteases (MMPs) are well-known to facilitate cancer invasion and metastasis through their ability to “digest” the extracellular matrix that otherwise impedes cancer cell invasion.4,5 By virtue of their localization to the extracellular compartment and the fact that most possess enzymatic properties, secreted molecules are attractive drug targets.6 In addition, they often enter the body fluids and can be measured by noninvasive assays.7,8 The cancer secretome is therefore a valuable resource for understanding the molecular mechanisms of carcinogenesis and discovery of cancer drug targets and biomarkers. Invasive tumor cells and their microenvironments are enriched with a wide range of proteases. Apart from the G-protein coupled receptors, proteases represent another major group of therapeutic targets for oncology and infectious disease. One key group of proteases are the Cathepsins, which can be divided into three subgroups according to their active-site amino acid: cysteine (B, C, H, F, K, L, O, S, V, and W), aspartic (D and E), and serine (G) cathepsins. Like the MMPs, members of the Cathepsin family of proteases are well-known to be associated with cancer metastasis and recurrence.5 Most of the members Journal of Proteome Research 2010, 9, 4767–4778 4767 Published on Web 07/13/2010

research articles of the cysteine lysosomal proteases such as Cathepsin S (CTSS) are important for the turnover of intracellular and extracellular proteins including the extracellular matrix proteins, which have emerged as important regulators of tumor growth and invasion.9 Flannery et al. showed that CTSS expression was highest in grade IV astrocytomas and inhibition of CTSS disrupted invasive property of cancer cells.10 In a mouse model of hepatocellular carcinogenesis, CTSS was the major protease specifically overexpressed during vessel sprouting.11 In another mouse model study, it was demonstrated that selective cathepsin S deficiency impaired angiogenesis and tumor cell proliferation, thereby slowing the growth of solid pancreatic tumors, whereas the absence of its endogenous inhibitor cystatin C resulted in the opposite phenotypes.12 Comparative secretomics have been performed on colorectal, pancreatic and lung cancers using 2-D PAGE,13 SILAC14 and ITRAQ,8 respectivelysall contributing to a better understanding of the mechanism of cancer regulating genes. By studying the secretome of non small cell lung adenocarcinoma cell line with a homozygous deletion of p53, modulation of exported proteins were detected.8 In another study, secretomics was exploited to understand the mechanism of SMAD4, a tumor suppressor gene primarily involved in carcinogenesis of the pancreas and colon. By re-expressing Smad4 in Smad4-deficient human colon cancer cells, 25 proteases and protease inhibitors were implicated as Smad4 regulated targets.13 Here, we compared the levels of secreted proteins in normal and gastric cancer cell lines using the iTRAQ approach. The normal gastric epithelium cell line, HFE145, was derived from normal human gastric epithelial cells following transfection with SV40 Large T-antigen and human telomerase vectors.15 MKN-7 is a well-differentiated gastric adenocarcinoma cell line while MKN-45 is a poorly differentiated cell line. Notwithstanding the fact that gastric cancers are highly heterogeneous, it is anticipated that the list of secreted proteins found to be aberrantly expressed within this 3-cell line model will be able to reflect at least some types of clinical gastric cancer and serve as a useful reference for future basic or translational cancer research. CTSS was identified to be up-regulated in gastric cancer in this study. Its role and mode of action in gastric cancer migration and invasion were investigated.

Materials and Methods Cell Culture. Fifteen human gastric cancer cell lines (MKN7, MKN28, N87-HCl, AGS, MKN45, SCH, KATO3, SNU1, SNU5, SNU16, IM95, NUGC3, NUGC4, OCUM1, and HS746T) were purchased from American Type Culture Collection (Manassas, VA) and Japanese Riken Cell Bank (Tsukuba, Japan). The normal gastric epithelium HFE145 was kindly provided by Professor Hassan Ashktorab (Howard University, Washington, D.C.). The cells were cultured with RPMI1640 medium containing 10% fetal calf serum (Hyclone, Logan, UT). Cell lines were authenticated by short tandem repeat (STR) profiling carried out by the suppliers. All cells were tested to be Mycoplasmafree. All experiments were carried out between passages 1 and 10. Reagents. TLN1, PYGB, HSP90B1, CSTB, KRT8, CPVL and CTSS antibodies were from Santa Cruz (Santa Cruz, CA). Enhanced chemiluminescence (ECL) detection kit was purchased from General Electric Healthcare, Bio-Sciences (Uppsala, Sweden), prestained molecular weight markers 4768

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Yixuan et al. were from Bio-Rad (Hercules, CA), and protease inhibitors cocktail were from Roche (Mannheim, Germany). Sample Preparation for Secretomics and Labeling with iTRAQ Reagents. Approximately 5 × 107 cells were grown to 80% confluence, washed two times in UltraDOMA-PF proteinfree medium (Lonza Ltd., Switzerland), and incubated for 24 h in protein-free UltraDOMA-PF medium. All cells were visually inspected to be healthy before the media containing the secreted proteins were collected and filtered using a 0.22 µm filter (Millipore, Bedford, MA) and subsequently concentrated using a 3000-Dalton molecular mass Centriplus centrifugal filter devices (Millipore). For Secretomics of CTSS knocked-down versus control cells, conditioned media was discarded 24 h after siRNA transfection to remove background proteins and replaced with protein free media for another 24 h before conditioned media was harvested and processed as above. The concentration of proteins was determined using 2D Quantification kit (Amersham Biosciences, Uppsala, Sweden). From each cell lines, 100 µg of proteins were evaporated to dryness and dissolved in the solution buffer, denatured, and then cysteines were blocked as described in the iTRAQ protocol (Applied Biosystems, Framingham, MA). Each sample was digested with 20 µL of 0.1 µg/µL trypsin (Promega, WI) solution at 37 °C overnight and then labeled with the iTRAQ tags in accordance to manufacturer’s instruction. Proteins from HFE145, MKN7, and MKN45 were labeled with iTRAQ reagents 114, 115, and 116, respectively. Fractionation of Peptides by Isoelectric Focusing (IEF) on Immobilized pH Gradient. iTRAQ labeled peptides were dissolved in 300 µL 8 M urea and 1% Pharmalyte (Amersham Biosciences), and used to rehydrate IPG strips (pH 3-10, 18 cm long, Amersham Biosciences) for 14 h at 30 V. Peptides were focused successively for 1 h at 500 V, 1 h at 1000 V, 1 h at 3000 V and 8.5 h at 8000 V to give a total of 68 kVh on an IPGphor (Amersham Biosciences). The strips were then removed and quickly cut into 36 0.5-cm pieces. Peptides extraction was performed by incubating the gel pieces in 100 µL of 2% acetonitrile, 0.1% formic acid for 1 h. Fractions were lyophilized in vacuum concentrator and subjected to C-18 cleanup using a C18 Discovery DSC-18 SPE column (100 mg capacity, Supelco, Sigma-Aldrich). The cleaned fractions were lyophilized again and stored in -20 °C prior to mass spectrometric analysis. Mass Spectrometric Analysis. Each cleaned-up peptide fraction was resuspended in 20 µL of Buffer A (0.1% formic acid in 2% acetonitrile) and 10 µL of sample was injected to the nano-LC-ESI-MS/MS system for each analysis. Mass spectrometry was performed using a QStar Elite Hybrid ESI Quadrupole time-of-flight tandem mass spectrometer, ESI-Q-TOFMS/MS (Applied Biosystems, Framingham, MA; MDS-Sciex, Concord, Ontario, Canada) coupled to an online capillary liquid chromatography system (Dionex Ultimate 3000, Amsterdam, The Netherlands). The peptide mixture was separated on a PepMap C-18 RP capillary column (Dionex) at 0.3 µL/min. A 125 min gradient was used, where the gradient started with 4% Buffer B (0.1% formic acid in 98% acetonitrile) and 96% Buffer A for 3 min, followed by 3 ramping gradients of 4% to 10% Buffer B in 7 min, 10% to 35% Buffer B for 55 min and 35% to 100% Buffer B for 25 min. This was then held at 100% Buffer B for 15 min and finally at 96% Buffer A for 20 min. The mass spectrometer was set to perform data acquisition in the positive ion mode, with a selected mass range of 300-1800 m/z. The time of summation of MS/MS events was set to be 2 s. The two most abundant charged peptides above a 20 count

Novel Role of CTSS in Gastric Cancer Migration and Invasion threshold were selected for MS/MS and dynamically excluded for 30s with (50 mDa mass tolerance. Protein identification and quantification for iTRAQ samples were carried out using ProteinPilot software (version 2.0; Applied Biosystems, MDSSciex). The search was performed against International protein index (IPI) human database (version 3.41, date of release: March 2008, 72 155 sequences). Other details of database search are as previously described.16 CTSS siRNA Transfection, Wound Healing Assay, and Invasion Assay. MKN7 cells were first trypsinized in serum free media and counted before transfection with 200nM of Luciferase control siRNA (Invitrogen Carlsbad, CA) or CTSStargeted siRNA using Lipofectamine 2000 (Invitrogen Carlsbad, CA). The three CTSS siRNA sequences used were CTSS siRNA1 (5′-UAUAUCCUUCUUCACCAAAGUUGUG-3′), CTSS siRNA2 (5′-AGAAUCAGGCAAUAUCCGAUUAGGG-3′) and CTSS siRNA3 (5′-UUGUCAUGAAGCCACCAUUGCAGCC-3′). Two days following transfection, wound healing and invasion assays were conducted. For wound healing assays, cells were seeded onto a 6-well plate and grown until a confluent monolayer. A wound was incised in the cell monolayer with a p200 pipet tip. The cells were washed once with growth medium to remove the cell debris and to smoothen the edge of the scratch and then replaced with fresh growth medium. The cells were incubated at 37 °C, and their migration into the scratch area was monitored up to 16 h. Using a phase-contrast microscope, the images of the scratch at the same field were captured at 0 and 16 h after scratch. The relative width of the scratch was measured quantitatively using Adobe Photoshop 7.0. The extent of gap closure was determined as the rate of cell migration. For the invasion assay, about 1 × 105 transfected cells were added to the top chambers of 96-well trans-well plate with polycarbonate membrane chambers coated with a uniform layer of dried basement membrane matrix solution (8 µm pore size, Cell Biolabs Inc., San Diego, CA) and medium containing 10% FBS were added to the bottom chambers. Incubation was done for 24 h. Top noninvasive cells were removed, bottom invaded cells were first dissociated from the membrane, then lysed and quantified using CyQuant GR fluorescent dye at 480 nm/520 nm. In all cases, effective knock down of gene expression was verified via Western blot analysis described above. Experiments were performed at least in triplicates. Immunohistochemistry and Tissue Microarray (TMA). The 15 matched normal and gastric cancer tissues used for initial screening were obtained from National University Hospital tissue repository while matched normal and gastric cancer tissues from 115 patients used to create the tissue microarrays described below were from the Department of Pathology, National University of Singapore. In all cases, the matched normal tissues were taken from nondiseased areas (as judged by qualified pathologist) adjacent to the cancer tissue of the same patients whenever available. Approval from the Institutional Research Board at the National University of Singapore was obtained prior to the study. American Joint Committee on Cancer (AJCC) (6th edition) on gastric cancer staging system and Lauren’s classification of gastric cancer were used. Immunohistochemistry (IHC) was performed as described previously.17 Antibodies against TLN1, PYGB, CSTB, and CTSS were used at a 1:200 dilution. The TMA was constructed from representative regions of formalin-fixed paraffin-embedded samples from 115 patients following a previous protocol.18 A total of 236 samples could be derived from 115 patients since (i) normal samples were taken from the gastric cancer patients

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and (ii) while some patients had pure diffuse or intestinal type of disease, others presented both intestinal and diffuse types of gastric cancer. In the latter case, both lesions from the same patient were scored separately and treated as different samples. Protein expression was assessed both on the staining intensity (scale 0-3) and the percentage of positive cells (0-100%), which, when multiplied, generate a score ranging from 0 to 3.0. Interpretation of Hematoxylin and Eosin (H&E) sections and analysis/scoring of TMA data were all done by the same certified pathologist to maintain consistency. Statistical analysis was done using SPSS v10.0 for Windows (SPSS Inc., Chicago, IL). The t test was performed at 95% confidence.

Results Detection and Relative Quantification of Secreted Proteins in Normal and Gastric Cancer Cell Lines. The iTRAQ ratios, their respective statistical values and % coverage for a total of 1365 proteins detected with 95% confidence are shown in Supplementary Table 1 (Supporting Information). Although relative quantification analysis by ProteinPilot 2.0 software comes with statistical analysis, most methods are prone to technical variation. Examination of the average values and standard deviations of the data from duplicate experiments revealed that the overall variation was less than 30%. This is consistent with the various iTRAQ studies conducted in our laboratorysboth published and unpublished.19 Hence, included an additional 1.3-fold change cutoff for all iTRAQ ratios (ratio 1.3) for classifying proteins as up- or downregulated. Therefore, the upper and lower range worked out to be 1.3 (1 × 1.3) and 0.77 (1/1.3), respectively. In other words, proteins with iTRAQ ratios below the lower range were considered to be under-expressed, while those above the higher range were considered overexpressed. On the basis of this system, 237 proteins were found to be expressed at different levels between the secretomes of normal and gastric cancer epithelial cells (Supplementary Table 2, Supporting Information). Due to space constraints, the top 60 up- and downregulated proteins in both gastric cancer cell lines compared to normal cell line are shown in Table 1. Classification of Proteins based on Molecular and Cellular Functions. To better appreciate the data set generated, the gene list of 237 proteins was uploaded into Ingenuity Pathways Analysis (IPA) software server (http://www. ingenuity.com) and analyzed using the Core Analysis module to rank the gene list into top biological functions including diseases and disorders as well as molecular and cellular functions. Remarkably, cancer (134 molecules) and gastrointestinal disease (70 molecules) were ranked as the top 2 diseases/ disorders for the gene list. This is consistent with the biological topic (gastric cancer) of this study. On the other hand, cellular movement (63 molecules) and cell-cell signaling/interaction (53 molecules) were the top cellular functions most significantly associated with the gene list (Figure 1A). As will be revealed later, this information was important in guiding our choice of functional assays. Detailed information on the identities of proteins involved in each respective function/pathway is provided in Supplementary Table 3 (Supporting Information). The identities of all 63 proteins associated with the top molecular function of cell movement are embedded within Figure 1A. The two major groups of proteins in this list that were found to be up-regulated in the conditioned media of cancer compared to normal cells were the Laminins (A3, B3 and C2) and Cytoskeletal proteins. Laminins are important Journal of Proteome Research • Vol. 9, No. 9, 2010 4769

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Table 1. Partial List of Proteins Identified to Be Expressed at Different Levels between Normal and Gastric Cancer Cell Lines by iTraq Analysisa accession

gene sym

IPI:IPI00029723.1 IPI:IPI00002714.1 IPI:IPI00216138.6 IPI:IPI00005292.1 IPI:IPI00418471.6 IPI:IPI00021033.2 IPI:IPI00027780.1 IPI:IPI00296534.1 IPI:IPI00749179.2 IPI:IPI00299738.1

FSTL1 DKK3 TAGLN SPOCK1 VIM COL3A1 MMP2 FBLN1 C1S PCOLCE

IPI:IPI00007118.1 IPI:IPI00219219.3 IPI:IPI00643384.2 IPI:IPI00297646.4 IPI:IPI00029568.1 IPI:IPI00033466.5

SERPINE1 LGALS1 BGN COL1A1 PTX3 CLEC11A

IPI:IPI00304962.3 IPI:IPI00014572.1 IPI:IPI00022200.2 IPI:IPI00019372.1 IPI:IPI00291136.4 IPI:IPI00296165.5 IPI:IPI00304840.4

COL1A2 SPARC COL6A3 SRGN COL6A1 C1R COL6A2

IPI:IPI00009890.1 SERPINE2 IPI:IPI00018219.1 TGFBI IPI:IPI00220815.1 EFEMP1 IPI:IPI00016112.6 IPI:IPI00012119.1 IPI:IPI00299024.9 IPI:IPI00009308.1

PXDN DCN BASP1 CCL2

name

p-Val p-Val MKN7:HFE145 MKN7:HFE145 MKN45:HFE145 MKN45:HFE145

Top 30 proteins down-regulated in gastric cancer Follistatin-related protein 1 precursor 0.05 Dickkopf-related protein 3 precursor 0.02 Transgelin 0.08 Testican-1 precursor 0.04 Vimentin 0.09 Isoform 1 of Collagen alpha-1(III) chain precursor 0.09 72 kDa type IV collagenase precursor 0.11 Isoform D of Fibulin-1 precursor 0.05 Uncharacterized protein C1S 0.05 Procollagen C-endopeptidase enhancer 1 0.08 precursor Plasminogen activator inhibitor 1 precursor 0.08 Galectin-1 0.14 Uncharacterized protein BGN 0.09 Collagen alpha-1(I) chain precursor 0.09 Pentraxin-related protein PTX3 precursor 0.23 C-type lectin domain family 11 member A 0.11 precursor Collagen alpha-2(I) chain precursor 0.10 SPARC precursor 0.08 alpha 3 type VI collagen isoform 1 precursor 0.10 Serglycin precursor 0.10 Collagen alpha-1(VI) chain precursor 0.09 Complement C1r subcomponent precursor 0.10 Isoform 2C2 of Collagen alpha-2(VI) chain 0.11 precursor Glia-derived nexin precursor 0.08 Transforming growth factor-beta-induced protein 0.10 ig-h3 precursor Isoform 4 of EGF-containing fibulin-like 0.16 extracellular matrix protein 1 precursor Isoform 1 of Peroxidasin homologue precursor 0.13 Isoform A of Decorin precursor 0.11 Brain acid soluble protein 1 0.49 C-C motif chemokine 2 precursor 0.02

Top 30 proteins up-regulated in gastric cancer Complement component 1 Q subcomponent2.04 binding protein, mitochondrial precursor IPI:IPI00022442.2 NDUFAB1 Acyl carrier protein, mitochondrial precursor 3.49 IPI:IPI00001922.1 ST14 Suppressor of tumorigenicity protein 14 3.35 IPI:IPI00401264.5 TXNDC4 Thioredoxin domain-containing protein 4 1.33 precursor IPI:IPI00009268.1 ACY1 Aminoacylase-1 2.58 IPI:IPI00328703.1 OAF Out at first protein homologue precursor 2.51 IPI:IPI00027444.1 SERPINB1 Leukocyte elastase inhibitor 2.18 IPI:IPI00855775.1 RNASET2 Ribonuclease T2 1.74 IPI:IPI00003362.2 HSPA5 HSPA5 protein 4.20 IPI:IPI00004358.4 PYGB Glycogen phosphorylase, brain form 1.46 IPI:IPI00877157.1 AKR1C3 aldo-keto reductase family 1, member C3 1.77 IPI:IPI00411765.3 SFN Isoform 2 of 14-3-3 protein sigma 6.35 IPI:IPI00301395.4 CPVL Probable serine carboxypeptidase CPVL precursor 13.50 IPI:IPI00299404.1 LAMB3 Laminin subunit beta-3 precursor 4.24 IPI:IPI00005563.1 TINAGL1 Isoform 1 of Tubulointerstitial nephritis 5.74 antigen-like precursor IPI:IPI00377045.3 LAMA3 LAMA3 Alpha3A 3.76 IPI:IPI00783625.1 SERPINB5 Serpin B5 precursor 6.94 IPI:IPI00743064.1 LCN2 Uncharacterized protein LCN2 6.70 IPI:IPI00186338.1 LOC645870 similar to barrier-to-autointegration factor 2.88 IPI:IPI00216222.1 ITGA6 Isoform Alpha-6 × 1B of Integrin alpha-6 3.40 precursor IPI:IPI00015117.2 LAMC2 Isoform Long of Laminin subunit gamma-2 5.01 precursor IPI:IPI00021828.1 CSTB Cystatin-B 1.99 IPI:IPI00479145.2 KRT19 type I cytoskeletal 19 1.87 IPI:IPI00376403.2 SPINT1 Isoform 1 of Kunitz-type protease inhibitor 1 3.27 precursor IPI:IPI00554788.5 KRT18 type I cytoskeletal 18 3.07 IPI:IPI00299150.4 CTSS Cathepsin S precursor 1.72 IPI:IPI00554648.3 KRT8 type II cytoskeletal 8 4.08 IPI:IPI00744889.2 CDH1 E-cadherin 2.77 IPI:IPI00641640.2 LSR LISCH protein isoform 1 3.41 IPI:IPI00014230.1 C1QBP

9.78 × 10-5 0.000209366 1.12 × 10-6 2.70 × 10-7 1.23 × 10-6 1.39 × 10-11 2.12 × 10-11 0.000105517 0.001482739 1.79 × 10-10

0.02 0.02 0.02 0.03 0.03 0.04 0.04 0.04 0.04 0.04

7.13 × 10-17 4.98 × 10-6 6.81 × 10-7 3.94 × 10-18 2.49 × 10-29 2.25 × 10-29 2.70 × 10-30 5.63 × 10-8 4.08 × 10-7 1.70 × 10-25

0 1.61 × 10-11 1.14 × 10-5 5.32 × 10-44 7.90 × 10-22 0.0004792

0.05 0.05 0.05 0.05 0.05 0.05

0 2.23 × 10-15 3.19 × 10-11 0 1.05 × 10-24 3.39 × 10-10

2.14 × 10-19 5.73 × 10-27 5.90 × 10-22 5.68 × 10-5 0 6.69 × 10-14 6.17 × 10-11

0.05 0.06 0.06 0.06 0.06 0.06 0.06

9.81 × 10-45 8.18 × 10-34 0 1.06 × 10-7 0 9.70 × 10-26 1.16 × 10-18

3.05 × 10-17 3.42 × 10-15

0.07 0.07

1.67 × 10-34 3.45 × 10-26

3.82 × 10-5

0.07

0.000329399

-6

1.97 × 10 0.003670503 2.55 × 10-8 0.004075632

0.07 0.07 0.07 0.08

1.57 × 10-10 2.04 × 10-5 2.53 × 10-6 0.000256421

0.001335592

3.33

5.55 × 10-6

0.009460836 0.022442684 0.022650229

3.50 3.56 3.60

0.009357804 0.0170392 4.17 × 10-9

0.01286175 0.038267709 1.41 × 10-6 0.007651277 0 0.00210026 0.026647395 0.00011823 4.43 × 10-5 7.63 × 10-14 0.000254306

3.62 3.64 3.66 3.68 3.72 3.83 3.87 3.94 4.08 4.28 4.52

0.00596599 0.015070822 3.31 × 10-9 0.000341502 0 1.30 × 10-8 0.003842266 0.001440401 0.001928787 2.38 × 10-14 0.000588412

2.91 × 10-21 9.31 × 10-8 0 0.024750967 0.001430068

4.69 4.88 4.89 5.05 5.30

7.13 × 10-25 3.78 × 10-6 0 0.020400673 0.004443585

1.09 × 10-16

5.35

3.99 × 10-14

2.25 × 10-12 9.90 × 10-7 0.000370764

5.65 6.00 7.63

1.77 × 10-24 6.27 × 10-18 2.68 × 10-6

9.06 × 10-12 0.021520112 1.21 × 10-23 6.08 × 10-5 0.000374193

7.66 8.89 11.61 11.77 12.20

3.99 × 10-19 2.43 × 10-5 3.17 × 10-29 1.71 × 10-11 7.14 × 10-7

a Only the top 30 up- and down-regulated proteins in gastric cancer cells are shown. MKN7:HFE145 and MKN45:HFE145 refer to relative level of protein expression in MKN7 and MKN45 gastric cancer cells with respect to normal HFE145 gastric epithelial cells. Statistical calculation for iTRAQ-based detection and relative quantification were calculated using the Paragon Algorithm in the ProteinPilot software.

component of the basal lamina, influencing cell differentiation, migration, adhesion as well as phenotype and survival.20 They 4770

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are secreted and incorporated into cell-associated extracellular matrices. Laminin is vital for the maintenance and survival of

Novel Role of CTSS in Gastric Cancer Migration and Invasion

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Figure 1. (A) Bar chart showing the ranking of the top 10 most significant diseases, disorders and molecular functions associated with the data set by Ingenuity Pathway Analysis. The identities of the 63 proteins associated with the cellular movement, the top molecular function, are shown. Proteins highlighted in red and green refer to up- and down-regulated proteins, respectively in the conditioned media of cancer compared to normal cells. (B) Western blot analyses on selected candidates (CTSS, PYGB, TLN1, HSP90B1, CSTB, KRT8, and CPVL) in the conditioned media of HFE145 normal, MKN7 and MKN45 gastric cancer cells. Total protein Syproruby stain was included to show equal loadings. (C and D) Immunoblotting of PYGB, TLN1, CSTB and CTSS in the conditioned media and lysates of HFE145 normal gastric cell line and 15 gastric cancer cell lines, respectively.

tissues. On the other hand, the cytoskeletal proteins [e.g., E-cadherin (CDH1), Cortactin (CTTN) and Integrin alpha 6 (ITGA6)] play important roles in cell-cell adhesion and focal adhesion, both being processes that have a positive impact on cell migration. One prominent group of proteins that were down-regulated was comprised of various types of proteases inhibitorssTissue Inhibitors of MetalloProteases (e.g., TIMP1 and 2) and Serine Protease Inhibitors (e.g., Serpine 1 and 2). The TIMP and SERPINE are inhibitors of MMPs and Serine proteases, which largely play positive roles in cancer migration and invasion. Down-regulation of these inhibitors in the cancer secretome is likely to favor cancer aggression. Validation of iTRAQ Data on Selected Candidates. To verify the iTRAQ data, Western blot analyses were performed on selected candidates of interest to our laboratories. They included Cathepsin S (CTSS), a member of the Cathepsin family implicated in cancer invasion and metastasis. These rest are proteins known to be associated with cancer migration, invasion or metastasis [e.g., Keratin 8 (KRT8),21 Talin 1 (TLN1),22 Cystatin B (CSTB),23 Glycogen Phosphorylase B (PYGB)24] or

represent emerging drug targets [e.g., Heat Shock Protein 90 (HSP90)25]. The relative levels of most of these proteins in the conditioned media of cancer versus normal cells could be found in Table 1. Figure 1B shows that the up- or downregulation trend of candidate proteins between normal and cancer cells provided by the immunoblotting data are largely congruent with that by iTRAQ. To provide original and new insights into gastric cancer, four candidates namely, PYGB, TLN1, CSTB, and CTSS were chosen for further analysis in a larger panel of gastric cancer cell lines since they have not been previously reported to be aberrantly expressed in gastric cancer. In contrast, the other proteins shown in Figure 1B such as HSP9026 and KRT827 have been previously implicated in gastric cancer. Remarkably, CSTB and CTSS were detected in the conditioned media of two-thirds or more of the gastric cancer cell lines but not in that of normal cells (Figure 1C). In contrast, expression of TLN1 was detected in the normal gastric epithelial cell line and only 2 well-differentiated gastric cancer cell lines. When compared to the nonmetastatic gastric cancer cell lines such as AGS, IM95, and SNU1, the expression level of CTSS Journal of Proteome Research • Vol. 9, No. 9, 2010 4771

research articles was significantly higher in gastric cancer cell lines with high metastatic potential, especially in MKN7, MKN45, SNU5, and NUGC3 cell lines. Consistent with the data obtained from analysis of the conditioned media, Figure 1D shows that intracellular CTSS was detectable in 10 out of 15 gastric cancer cell lines but not HFE145 normal and a small number of other gastric cancer cell lines. Collectively, these data support the notion that CTSS and possibly CSTB and PYGB may be important to the cancer phenotype. Relevance of TLN1, CSTB, PYGB and CTSS to Clinical Gastric Cancer. Since cell lines represent in vitro system that lack the physiological context of solid tumors, the clinical relevance of TLN1, CSTB, PYGB and CTSS was assessed by screening their expressions in 15 matched adjacent normal and gastric cancer tissues using Immunohistochemistry (IHC). CSTB, PYGB and CTSS were up-regulated in tumor compared with normal tissues in 7 out of 15 (47%), 6 out of 15 (40%), and 8 out of 15 (53%) matched cases, respectively. Conversely, TLN1 was down-regulated in 6 out of 15 (40%) matched normal and gastric cancer tissues. Figure 2A shows the representative IHC images of the candidates tested. The subcellular locations of these protein candidates as revealed by IHC were consistent with their biological functions - The speckled staining observed for CTSS (red arrows) is indicative of lysosomal localization, the diffuse staining patterns observed for CSTB and PYGB implied cytosolic localization while fibrous like pattern, sometimes outlining cell membrane, is characteristic of cytoskeletal proteins like TLN1. While the other 3 candidates were also interesting, CTSS was selected for further studies because it is a protease and potential drug target. To further explore the novel role of CTSS in gastric cancer, the expression of CTSS was examined in TMA comprising normal tissues, intestinal, diffuse and mixed type of gastric cancers from 115 gastric cancer patients. A summary of the characteristics of the clinical samples is provided in Supplementary Table 4 (Supporting Information). Briefly, the age ranges from 30-87 and majority of the cohort (68%) are male. There is a similar % of cases from the various clinical stages but majority of the histology grade (63%) is of the poorly differentiated type. As shown in Figure 2C, a significant number of intestinal, diffuse and mixed types of gastric cancer expressed CTSS at levels higher than that of normal tissues (p < 0.01). For illustration, 8/58 (14%) cases of normal samples as opposed to 37/87 (43%) of intestinal, 14/57 (25%) of diffuse and 16/34 (47%) of mixed type gastric cancers have IHC scores of >0. Figure 2B shows the representative images of the IHC of CTSS in TMA. Raw IHC scores for CTSS are provided in Supplementary Table 4 (Supporting Information). No statistically significant differences in the expression of CTSS between the gastric cancer subtypes were observed. Correlation studies of CTSS expression with other clinical parameters such as clinical staging, overall and disease free survival also did not yield significant results (data not shown). The data may seem disappointing especially when the current data supported a role of CTSS in gastric cancer aggression. However, the sample size used in this study is relatively small compared to most clinical studies. A larger sample size would be necessary to clarify the association of CTSS expression with clinical parameters. For now, the data suffice to support the notion that CTSS is relevant to the etiology of clinical gastric cancers. CTSS Plays a Role in Gastric Cancer Cell Migration and Invasion. Coupled to the existing knowledge on the function of the members in the Cathepsin family, the bioinformatics and 4772

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Yixuan et al. experimental data contained herein hinted that CTSS plays a role in gastric cancer migration and invasion. To test this hypothesis, MKN7 cells were transfected with 3 CTSS-specific siRNA sequences and 1 control siRNA. Effective silencing of the expressions of intracellular CTSS (Ic-CTSS) and extracellular CTSS (Ec-CTSS) was demonstrated by the CTSS-specific sequences but not the control siRNA (Figure 3A). Control and CTSS-knock-down cells were then subject to invasion and migration assays. Silencing of CTSS by the 3 gene-specific siRNAs inhibited the invasion of MKN7 cells by 22 to 38% compared to control siRNA (p < 0.01) (Figure 3B). Similarly, CTSS knockdown led to a 33-42% reduction on the ability of the MKN7 to close a scratch wound compared to control cells (Figure 3C). While the partial inhibition could be due to incomplete silencing of CTSS expression, it may also highlight that cooperation of other molecules with CTSS are required for these cellular processes. Out of curiosity, we also examined the proliferation of CTSS-silenced versus control MKN7 cells using MTT assay. CTSS-silenced cells exhibited only 8-12% less cells compared to control cells after Day 4 indicating that CTSS plays, if any, only a marginal role in gastric cancer cell proliferation (data not shown). Silencing CTSS Modulated the Expression of a Network of Proteins Involved in Cellular Movement. To understand how CTSS might contribute to migration and invasion of cancer cells and since secreted proteins are strong proponents of these cellular processes, the relative expression of proteins in the conditioned media from CTSS-knocked down (sequence #3) and control MKN7 cells were compared using iTRAQ. The raw iTRAQ data and details of sample labeling by iTRAQ reagents are provided in Supplementary Table 5 (Supporting Information). The duplicate sets of data were consistent in revealing that 165 and 32 proteins in CTSS-knocked down cells showed lower and higher levels, respectively, compared to normal cells. A representative set of data is shown in Supplementary Table 6 (Supporting Information). Ingenuity Pathway Analysis of the 197 proteins revealed that cellular movement was the feature most statistically correlated with the data set (data not shown). This is consistent with the earlier functional assays that indicated a considerable role of CTSS in cell migration. Therefore, we decided to examine the cell movement proteins closer. Fifty-eight proteins, including CTSS, were assigned by IPA into the group of proteins involved in cellular movements. iTRAQ data for this selected group of proteins is shown in Table 2. CTSS was down regulated by about 75% (siRNA: control iTRAQ ratio of 0.26) demonstrating that the knock down of CTSS by siRNA was not complete but substantial (Table 2). The incomplete silencing of CTSS may explain why the expressions of most of the targets in Table 2 were affected by only 0.3-1 fold. Nevertheless, it is conceivable that the collective disruption of these proteins as a consequence of CTSS silencing was strong enough to produce a phenotype observed in Figure 3. Table 2 reveals that CTSS potentially regulated a signaling network involving (i) receptor tyrosine kinases such as c-Met and AXL; (ii) peptidases including members of the Cathepsin [e.g., Cathepsin D (CTSD)], ADAM metallopeptidases, Matrix Metallopeptidases (MMPs) and Kallikrein families, (iii) Chemokines/cytokines encompassing IL11 and CXCL16 and (iv) cytoskeletal and adhesion proteins like cortactin and integrins, respectively. Many of them are important regulators of tumor biology. For example, IL11 cytokine was recently discovered to be required for gastric cancer development.28 CXCL16 is a

Novel Role of CTSS in Gastric Cancer Migration and Invasion

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Figure 2. (A) Representative images of the immmunohistochemistry of CTSS, CSTB, PYGB and TLN1 in 15 matched normal and gastric cancer samples. (B) Representative immunohistochemistry images of CTSS in normal tissue, intestinal and diffuse type of gastric cancers from tissue microarrays. (C) Bar chart distribution of IHC scores for CTSS in individual samples. Statistically significant higher expression of CTSS was observed in gastric cancer samples compared to normal gastric tissues (p < 0.01, independent sample t test). Journal of Proteome Research • Vol. 9, No. 9, 2010 4773

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Figure 3. Functional studies of CTSS in gastric cancer cell migration and invasion. (A) Effective silencing of the expression of intracellular CTSS (Ic-CTSS) and extracellular CTSS (EcCTSS) by CTSS-specific but not control siRNAs. (B and C) Inhibition of MKN7 invasion and migration, respectively, following CTSS knock down compared to control transfected cells. * ) p < 0.01

ligand for CXC chemokine receptor 4 (CXCR4) recently reported to be a key molecule in the formation of peritoneal carcinomatosis in gastric cancer.29 On the other hand, CTSD and c-Met are well-known players in gastric cancer aggression.30,31 In addition, clinical trials involving c-Met as drug targets for gastric cancer have also been conducted.32 Due to their prominence in cancer, we proceeded to investigate whether the expressions of CTSD and c-Met are authentically affected by CTSS silencing. As shown in Figure 4A, both CTSS and c-Met expressions were reduced when CTSS was knocked down. Although the fold changes were not great (as in the iTRAQ data), the effects were evident. It would be interesting to investigate the functional significance of the novel relationship between CTSS and c-Met in gastric cancer biology in future. To create biochemical context out of otherwise static proteomics data, all 57 cellular movement-related proteins (excluding CTSS) were imported and analyzed using the Core Analysis module of IPA. Of the 57 proteins, 33 proteins could be mapped into a network using the Network Explorer function of IPA. The rest could not be mapped most likely as a result of the lack of relevant information in the IPA’s database. Figure 4B shows a network of interactions between the putative CTSS target proteins or with other migration-associated proteins in the IPA knowledgebase. Such biological interaction network is useful for the formulation of testable hypotheses. For example, it is evident from the network that links exist between PDGF, AKT and MMP3. The PDGF/AKT pathway is well-known to regulate cell migration. Since MMP3 is implicated as a putative target of CTSS in this study, one could postulate and examine the involvement of CTSS in PDGF/AKT-mediated regulation of MMP3 expression. The biological interaction network may be a useful platform upon which new knowledge on gastric cancer could be built.

Discussion Making inroads into early detection and treatment are key measures to improve the outcome of gastric cancer patients. Secretomics is valuable from 2 view points. One, it permits 4774

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Yixuan et al. discovery of novel cancer effectors that are easily accessible drug targets. Two, it allows expression profiling in a specific functional compartment which is most relevant to the protein detected. Profiling the intracellular expression of a protein whose function is extracellular is not only meaningless but, as will be discussed later, misleading since their intracellular and extracellular levels need not necessarily correlate. It has become clear that the secretome is not comprised only of proteins secreted through the classical pathway but also proteins released through other various mechanisms including the nonclassical pathway and extrusion of exosomes.33,34 Indeed, bioinformatics revealed that among the 237 differentially expressed proteins detected; only 49% were classified as secreted proteins based on Swiss-Prot database while 55% were considered to be secreted proteins based on the presence of a signal peptide, as predicted by SignalP. One recent observation showed that exosomes contained mRNA, microRNA, ubiquitously expressed molecules such metabolic enzymes and cytoskeletal proteins.35 The latter group of proteins such as KRT8, KRT18, and KRT19 was found in our study to be upregulated in the secretomes of gastric cancer cells compared to normal cells. In recent years, cytokeratins such as KRT8 was shown to correlate with poor prognosis and drug resistance.21,36 Godfroid et al. demonstrated the presence of cytokeratins KRT8, KRT18, and KRT19 on the outer surface of established human mammary carcinoma cells but not normal mammary cells.37 Consistent with our observation, they also detected KRT8, KRT18, and KRT19 in the culture medium albeit in mammary carcinoma and not gastric cancer cells. The association of CTSS with gastric cancer is novel. Although a few Cathepsins were detected in our data set, CTSS emerged as the Cathepsin that was up-regulated in both MKN7 and MKN45 gastric cancer cells compared to normal cells. In contrast, Cathepsin L and B were down-regulated in the gastric cancer secretome profiled in this study. One study reported on the up-regulation of Cathepsin B but was conducted on gastric cancer epithelial cells not its secretome.38 Interestingly, the observed down-regulation of Cathepsins B and L was concomitant with the up-regulation of CSTB, a known inhibitor of Cathepsin B, H and L, in our data set. This implies that CTSS may be a key Cathepsin in gastric cancer biology. Simultaneous examination of the expression level of all the Cysteine Cathepsins in multiple cell lines and tissues is needed to confirm this. Protein-wide expression profiling of the secretome following knockdown of Cathepsin members in cancer cells has never been reported. Here, RNA interference of CTSS expression revealed that CTSS potentially targets a considerable number of proteins, which altogether may collaborate with CTSS toward achieving the migratory and invasive phenotype of gastric cancer cells. It is unclear whether the observed effect of CTSS silencing on protein expression was due to the disruption of intracellular and/or extracellular function of CTSS. While follow-up studies would be needed to establish the mechanism as well as the significance of the various relationships between CTSS and its putative targets, the preliminary data obtained is interesting and warrants some discussion. The substantial representation of the CTSS target network by the peptidases, for example, Cathepsins, MMPs and Kallikreins, is illuminating but perhaps not surprising given their well-known association with cancer aggression. On the other hand, reductions in the levels of extracellular receptor tyrosine kinases such as c-Met as a consequence of suppression of CTSS expression are novel

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Novel Role of CTSS in Gastric Cancer Migration and Invasion a

Table 2. Partial List of Protein Expression Changes Following CTSS Knock Down N

accession

gene symbol

name

siRNA:Ctrl

p-value

1 2 3 4 5 6 7 8 9

IPI:IPI00299150.4 IPI:IPI00027166.1 IPI:IPI00759755.1 IPI:IPI00745872.2 IPI:IPI00556155.2 IPI:IPI00296461.4 IPI:IPI00412410.1 IPI:IPI00410240.3 IPI:IPI00914871.1

CTSS TIMP2 B4GALT1 ALB IGFBP3 SMPD1 GAS6 RARRES1 SERPINE2

0.26 0.50 0.52 0.56 0.58 0.59 0.59 0.59 0.61

6.65 × 10-6 4.41 × 10-3 6.87 × 10-3 9.78 × 10-15 7.20 × 10-10 1.43 × 10-3 3.90 × 10-2 9.16 × 10-3 3.97 × 10-7

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55

IPI:IPI00004946.8 IPI:IPI00783987.2 IPI:IPI00021834.1 IPI:IPI00377045.3 IPI:IPI00292150.4 IPI:IPI00921401.1 IPI:IPI00015117.2 IPI:IPI00029601.6 IPI:IPI00023845.1 IPI:IPI00024741.1 IPI:IPI00299404.1 IPI:IPI00478864.1 IPI:IPI00007118.1 IPI:IPI00397361.6 IPI:IPI00235354.1 IPI:IPI00018274.1 IPI:IPI00000070.1 IPI:IPI00014572.1 IPI:IPI00008580.1 IPI:IPI00305380.3 IPI:IPI00029235.1 IPI:IPI00011229.1 IPI:IPI00011656.1 IPI:IPI00884102.1 IPI:IPI00783665.3 IPI:IPI00025820.3 IPI:IPI00013897.1 IPI:IPI00032292.1 IPI:IPI00374563.3 IPI:IPI00027782.1 IPI:IPI00643623.1 IPI:IPI00013976.3 IPI:IPI00220978.3 IPI:IPI00872375.2 IPI:IPI00021842.1 IPI:IPI00909303.1 IPI:IPI00553177.1 IPI:IPI00903213.1 IPI:IPI00013744.1 IPI:IPI00298281.4 IPI:IPI00019590.3 IPI:IPI00007117.1 IPI:IPI00296099.6 IPI:IPI00646281.1 IPI:IPI00011564.1 IPI:IPI00795482.1

CXCL16 C3 TFPI LAMA3 LTBP2 ITGA6 LAMC2 CTTN KLK6 EREG LAMB3 TNFSF15 SERPINE1 AXL TGFB2 EGFR LDLR SPARC SLPI IGFBP4 IGFBP6 CTSD B4GALT5 MET LAMA5 IL11 ADAM10 TIMP1 AGRN MMP3 LCN2 LAMB1 APLP2 SLC2A1 APOE CTSB SERPINA1 IGF2R ITGA2 LAMC1 PLAT SERPINB2 THBS1 L1CAM SDC4 STAT1

0.61 0.62 0.62 0.63 0.63 0.63 0.63 0.64 0.64 0.64 0.64 0.65 0.66 0.66 0.66 0.66 0.67 0.68 0.68 0.68 0.68 0.68 0.68 0.68 0.69 0.69 0.70 0.70 0.70 0.70 0.70 0.70 0.71 0.71 0.71 0.74 0.74 0.74 0.75 0.76 0.76 0.76 0.76 0.76 0.77 1.34

4.13 × 10-2 0.00 × 1000 2.96 × 10-3 5.17 × 10-18 9.99 × 10-4 4.38 × 10-2 2.13 × 10-24 1.73 × 10-4 4.95 × 10-3 3.63 × 10-3 2.93 × 10-16 5.10 × 10-4 3.70 × 10-6 3.10 × 10-2 5.28 × 10-3 3.37 × 10-2 1.01 × 10-7 9.35 × 10-3 3.44 × 10-3 2.29 × 10-8 4.16 × 10-2 0.00E+00 2.24 × 10-2 5.34 × 10-5 2.78 × 10-11 2.63 × 10-2 2.02 × 10-2 1.04 × 10-7 1.37 × 10-18 4.70 × 10-2 0.00E+00 3.24 × 10-6 2.66 × 10-7 7.75 × 10-3 3.47 × 10-6 4.36 × 10-2 1.51 × 10-10 1.81 × 10-2 2.55 × 10-4 9.26 × 10-10 2.18 × 10-4 2.97 × 10-12 8.05 × 10-14 2.96 × 10-6 2.15 × 10-3 6.42 × 10-3

56 57 58

IPI:IPI00017184.2 IPI:IPI00604620.3 IPI:IPI00419258.4

EHD1 NCL HMGB1

Cathepsin S Metalloproteinase inhibitor 2 Isoform Short of Beta-1,4-galactosyltransferase 1 Isoform 1 of Serum albumin insulin-like growth factor binding protein 3 isoform a precursor Isoform 1 of Sphingomyelin phosphodiesterase Isoform 1 of Growth arrest-specific protein 6 Isoform 2 of Retinoic acid receptor responder protein 1 plasminogen activator inhibitor type 1, member 2 isoform c precursor chemokine (C-X-C motif) ligand 16 Complement C3 (Fragment) Isoform Alpha of Tissue factor pathway inhibitor Laminin alpha-3 chain variant 1 Latent-transforming growth factor beta-binding protein 2 Isoform 9 of Integrin alpha-6 Isoform Long of Laminin subunit gamma-2 Src substrate cortactin Isoform 1 of Kallikrein-6 Proepiregulin Laminin subunit beta-3 Isoform 2 of Tumor necrosis factor ligand superfamily member 15 Plasminogen activator inhibitor 1 AXL receptor tyrosine kinase isoform 2 Isoform A of Transforming growth factor beta-2 Isoform 1 of Epidermal growth factor receptor Low-density lipoprotein receptor SPARC Antileukoproteinase Insulin-like growth factor-binding protein 4 Insulin-like growth factor-binding protein 6 Cathepsin D Beta-1,4-galactosyltransferase 5 met proto-oncogene isoform b precursor Laminin subunit alpha-5 Interleukin-11 ADAM 10 Metalloproteinase inhibitor 1 Agrin Stromelysin-1 Putative uncharacterized protein LCN2 Laminin subunit beta-1 Isoform 3 of Amyloid-like protein 2 Putative uncharacterized protein SLC2A1 (Fragment) Apolipoprotein E Cathepsin B Isoform 1 of Alpha-1-antitrypsin Cation-independent mannose-6-phosphate receptor Integrin alpha-2 Laminin subunit gamma-1 Isoform 1 of Tissue-type plasminogen activator Plasminogen activator inhibitor 2 Thrombospondin-1 L1 cell adhesion molecule isoform 3 precursor Syndecan-4 Isoform Alpha of Signal transducer and activator of transcription 1-alpha/beta EH domain-containing protein 1 Nucleolin High mobility group protein B1

1.35 1.64 2.08

4.50 × 10-2 2.80 × 10-5 2.63 × 10-3

a Proteins included here represent those involved in cell migration, a function most statistically correlated with the data set as determined by IPA analysis.

and noteworthy observations. This may appear conflicting at first since c-Met is a membrane bound receptor tyrosine kinase and not commonly known to exert its function as a secreted

protein. Yet, c-Met has been detected in the conditioned media of cancer cells.39 Reduction of c-Met in the conditioned media of pancreatic cancer cells has been observed when these cells Journal of Proteome Research • Vol. 9, No. 9, 2010 4775

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Figure 4. (A) Immunoblotting of extracellular CTSD and cMet in the absence or presence of CTSS silencing. Keratin 8 (KRT8) was used as a loading control since our data set indicated that its expression was not affected by that of CTSS. (B) Biochemical interaction network of proteins involved in cellular movement potentially targeted by CTSS. Thirty three proteins extracted from Table 2 (filled icons) were mapped into a network along with 37 other proteins extracted from the IPA database (nonfilled icons). Various relationships between these protein components are indicated with colored lines.

lost their metastatic potential as result of CD44v6 knocked down.39 C-Met and many receptor tyrosine kinases are first synthesized as precursors. It is conceivable that CTSS may be 4776

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required for proteolytic maturation and secretion of c-Met into the extracellular compartment of gastric cancer cells. It is not known what the role of extracellular c-Met is, but according

Novel Role of CTSS in Gastric Cancer Migration and Invasion to a model proposed by Greco et al., it is possible that released exosomes (also known as Argosomes) containing c-Met can be internalized by neighboring cells and act as potential vehicle for transporting morphogens/oncogenes through epithelia.40 In conclusion, we produced the first proteome-wide comparative secretomes between normal and gastric cancer conditions that represent a useful resource for basic and translational cancer research. This is exemplified by the demonstration that CTSS plays a novel role in gastric cancer cell migration and invasion, potentially by regulating the expression of a network of extracellular proteins involvement in cellular movement. Although this study focused on CTSS, many proteins identified in this study are potential biomarkers (either alone, in combination with each other or with other established markers), drug targets or cancer effectors. This is a viable idea since many secreted proteins such as CTSS are also plasma proteins. Others such as TLN1 and CSTB are novel gastric cancer-associated proteins. Their functions in gastric cancer biology and potential exploitation for gastric cancer management remain to be elucidated. Finally, the CTSS target genes are potential drug targets and might facilitate a combinatorial approach to adjuvant therapy and treatment of metastatic gastric cancers. Abbreviations: CTSS, Cathepsin S; iTRAQ, isobaric tags for relative and absolute quantification; LC-ESI-MS/MS, liquid chromatography-electrospray ionization tandem mass spectrometry.

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