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Article pubs.acs.org/jpr Proteomics Analysis Reveals Involvement of Krt17 in Areca NutInduced Oral Carcinogenesis Chang-Hsu Chiang,†,‡,# Chih-Ching Wu,†,‡,§,∥,# Li-Yu Lee,⊥ Yi-Chen Li,† Hao-Ping Liu,∇ Chia-Wei Hsu,§ Ya-Ching Lu,† Joseph T. Chang,*,⊗ and Ann-Joy Cheng*,†,‡ † Department of Medical Biotechnology and Laboratory Science, College of Medicine, Chang Gung University, Kwei-Shan, Tao-Yuan 333, Taiwan ‡ Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Kwei-Shan, Tao-Yuan 333, Taiwan § Molecular Medicine Research Center, Chang Gung University, Kwei-Shan, Tao-Yuan 333, Taiwan ∥ Department of Otolaryngology-Head & Neck Surgery, Chang Gung Memorial Hospital, Linkou 333, Taiwan ⊥ Department of Pathology, Chang Gung Memorial Hospital, Tao-Yuan 333, Taiwan ∇ Department of Veterinary Medicine, National Chung Hsing University, Tai-Chung 402, Taiwan ⊗ Department of Radiation Oncology, Chang Gung Memorial Hospital, Tao-Yuan 333, Taiwan S Supporting Information * ABSTRACT: The areca nut is a known carcinogen that causes oral cancer in individuals in Southeast Asia, but the molecular mechanism that leads to this malignancy is still unclear. To mimic the habit of areca nut chewing, our laboratory has established four oral cancer cell sublines (SAS, OECM1, K2, C9), which have been chronically exposed to areca nut extract (ANE). To elucidate the molecular basis of areca nut-induced oral carcinogenesis, the differential proteomes between oral cancer cells and the ANE-treated sublines were determined using isobaric mass tag (iTRAQ) labeling and multidimensional liquid chromatography−mass spectrometry (LC−MS/MS). Over 1000 proteins were identified in four sublines, and 196 proteins were found to be differentially expressed in at least two ANEtreated sublines. A bioinformatic analysis revealed that these proteins participate in several pathways, and one of the most prominent pathways was the regulation of epithelial to mesenchymal transition (EMT). In all, 24 proteins including Krt17 were confirmed to be differentially expressed in the ANE-treated sublines. To reveal additional information on the mechanism of ANE-induced carcinogenesis, Krt17 was further investigated. Krt17 knockdown significantly suppressed ANE-induced cell migration and invasion and modulated the EMT process. Furthermore, in a murine model of carcinogen-induced (arecoline cocktail, an active compound of ANE) oral cancer, Krt17 was significantly up-regulated in all hyperplastic tissues and in carcinoma tissues (p < 0.001). In conclusion, we have identified a proteome of oral cancer cells that is associated with chronic areca nut exposure. Krt17 was demonstrated to contribute to areca nut-induced oral malignancy. The results of this study contribute to risk assessment, disease prevention and other clinical applications associated with areca nut-induced oral cancer. KEYWORDS: areca nut, oral carcinogenesis, iTRAQ, epithelial to mesenchymal transition, Krt17 ■ INTRODUCTION Oral cancer is one of the most common cancers worldwide; it is prevalent in Southeast Asia1,2 and is more common in middleaged males.1−3 Epidemiologic studies have shown a strong association between oral cancer and environmental carcinogens, especially as a result of the use of tobacco and the areca nut.1,3,4 In Southeast Asia, the areca nut is a primary cause of oral precancerous and cancerous lesions, and an estimated 28-fold higher incidence of oral cancer has been observed among areca nut chewers compared with nonchewers.3 In 2004, the International agency for Research on Cancer recognized the areca nut as a Group 1 carcinogen.5 Previously, many studies have reported on the possible mechanisms of how the areca nut leads to the genesis of oral © 2016 American Chemical Society cancer. For example, the areca nut induces DNA damage, epigenetic alterations and elicits cytotoxicity in human epithelial cells.6,7 The areca nut also triggers inflammatory responses through the up-regulation of several meditators, including prostaglandin E2, cyclooxygenase-2, and IL-6.8−10 Areca nut exposure also induces the production of reactive oxygen species (ROS),11−13 which further results in cell growth arrest, apoptosis or autophagy.13−16 However, most reports on the molecular and cellular effects of the areca nut often used short-term and highdose cellular treatments as research models, which do not Received: February 16, 2016 Published: July 19, 2016 2981 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research between being used by areca nut chewers and cellular administration in this study, we estimated that the highest treatment concentration (500 μg/mL) was comparable to the exposure by a light chewer per day (10 pieces of extract in 1 mL), and 20% of the exposure by a heavy chewer per day (50 pieces of extract in 1 mL). recapitulate the conditions of patients with long-term, habitual exposure to areca nuts. Recently, proteomic approaches have been successfully used to analyze the proteomes of individuals with many malignant diseases, including oral cancer.17,18 Methods of quantitative proteomics, such as isobaric tag for relative and absolute quantification (iTRAQ) with tandem mass spectrometry (MS), provides more advanced technology for a global survey of differential proteomes between samples.19 The iTRAQ technique employs a 4-plex set of amine reactive isobaric tags for the derivatization of peptides at the N-terminus and at lysine side chains. During MS analysis, the same peptides that are labeled with any of the isotopic tags are indistinguishable (isobaric). Upon fragmentation during MS/MS, signature reporter ions are produced, which provide quantitative information on the peptides that originate from the different protein samples. The application of iTRAQ technology has been shown to be relevant in the discovery of promising cancer biomarkers.20,21 Previous studies have attempted to conduct a global survey to identify the molecular profiles that are associated with areca nutinduced oral diseases. Transcriptomic microarray analyses have been used to compare cancer cell lines treated with areca nut integrants,22,23 or to directly compare normal mucosa with oral cancer tissues from areca nut chewing patients.24−27 However, a major disadvantage of clinical sample investigation lies in the heterogeneity among individuals. In addition, the transcriptomic alterations are not directly linked to cellular function. Thus, far, to our knowledge, no proteomic assay that addresses areca nutinduced oral pathogenesis has been developed. To reduce heterogeneity and to obtain proteomic data associated with areca nut-induced oral cancer, we performed the iTRAQ-based analysis with four areca nut-exposed oral cancer cell sublines in this study. These sublines were chronically exposed to areca nut extract (ANE) over the long-term (3 months) and at a low-dose (IC30, dose for 30% growth inhibition) to recapitulate the experience of oral cancer patients with areca nut chewing habit. A software suite was used to identify statistically significant pathways, and RT-PCR was applied to confirm potential molecules. The most promising molecule was Krt17, which was further validated via cell and animal-based studies with respect to its critical role in the regulation of malignant phenotypes. Our study and the subsequent discovery of novel biomarkers provide additional knowledge and a foundation for further application of the identified molecules in the management of areca nut-induced oral diseases. ■ Preparation of Cell Extracts and Digestion of Protein Mixtures for Proteome Analysis Cells were lysed in buffer containing 100 mM triethylammonium bicarbonate (TEABC, Sigma-Aldrich, St. Louis, MO, USA) and 0.1% RapiGest SF (Waters Corporation, Milford, MA, USA) on ice for 15 min. The cell lysate was collected, and the protein mixtures were subjected to in-solution tryptic digestion in a manner similar to that described in the Supporting Information.30 iTRAQ Reagent Labeling The peptides were labeled with iTRAQ reagent (Applied Biosystems, Foster City, CA, USA) as described in the Supporting Information. In the first experimental set, iTRAQ 114 and 115 reagents were combined with peptide mixtures from parental and ANE-trained SAS cells, respectively. The peptides of parental and ANE-trained OECM1 cells were labeled with iTRAQ 116 and 117, respectively. For set 2, iTRAQ 114 and 115 reagents were added to the peptide mixtures of parental and ANE-trained CGHNK2 cells, respectively. The peptides from parental and ANE-trained CGHNC9 cells were labeled with iTRAQ 116 and 117, respectively. Peptide Fractionation and LC−MS/MS Analysis by LTQ-Orbitrap PQD For strong cation exchange (SCX) chromatography, the iTRAQlabeled peptides were loaded onto a BioBasic SCX column (Thermo Electron, CA, USA), as described in the Supporting Information. After SCX chromatography, each peptide fraction was loaded across a trap column (Zorbax 300SB-C18, 0.3 × 5 mm, Agilent Technologies, Wilmington, DE, USA) and separated on a resolving 10 cm analytical C18 column (inner diameter, 75 μm) with a 15-μm tip (New Objective, Woburn, MA, USA). The peptides were eluted as described in the Supporting Information. The reversed-phase LC apparatus was online coupled to a mass spectrometer. Peptides were analyzed with LTQ-Orbitrap Discovery (Thermo Fisher Scientific, CA, USA) and selected for MS/MS using the PQD operating mode as described in the Supporting Information.30 Sequence Database Search and Quantitative Data Analysis The MS/MS spectra were searched against the Swiss-Prot human sequence database (released Jun 15, 2010, selected for Homo sapiens, 20 367 entries) using the Mascot search engine (Matrix Science, London, UK; version 2.2.04). The detailed parameters for protein identification are described in the Supporting Information. Protein identification and quantification were validated using the default setting of open source transproteomic pipeline (TPP) software (Version 4.0). We used ProteinProphet probability scores ≥0.95 to ensure an overall false-positive rate of less than 1.0%. Proteins were quantified using the Libra program, which is a module within the TPP software package. Each quantified protein contained at least three LIBRA peptides. The other detailed settings for protein quantification are described in the Supporting Information. iTRAQ ratios were log2 transformed and normalized as described in the Supporting Information. Proteins with log2 ratios below the mean of all log2 ratios minus one standard MATERIALS AND METHODS Cell Lines and the Treatment of ANE Three oral cancer cell lines (OECM1, SAS, and CGHNC9) and one immortalized normal keratinocyte cell line (CGHNK2) were used in this study.28 The immortalized normal keratinocyte cells were maintained in KSFM medium (Life Technologies, Inc., Gibco BRL, Rockville, MD, USA), while cancer cell lines were grown in 100% DMEM or RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) (Life Technologies, Inc.). All cells were cultured at 37 °C in a humidified atmosphere of 5% CO2. These cell lines were chronically treated with an IC30 dose of ANE for 3 months to establish ANE-trained sublines, as previously described.29 The IC50 doses were 680, 430, 500, and 1200 μg/mL for CGHNK2, SAS, CGHNC9, and OECM1 cells, respectively. For the relevance of effective concentration of ANE 2982 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research medium supplemented with 1% FBS, cell migration toward the gap area was imaged. All experiments were performed independently at least twice, and the average results are shown. In each sample, the invasion ability was quantified via a comparison of the distance of the cell-free gap after normalization to the control group. deviation (SD) of all log2 ratios were considered to be underexpressed. Proteins above the mean plus one SD were considered to be overexpressed. Bioinformatic Analysis of Functional Pathways The analysis of the functional pathways of differentially expressed proteins affected by the areca nut was performed using MetaCore analytical suite (GeneGo Inc., St Joseph, MI, USA), as previously described.31 MetaCore was applied to calculate the statistical significance (p value) based on the probability of assembly from a random set of nodes (genes) of the same size as the input gene list. To obtain a functional network of the differentially expressed genes, we applied the network paths algorithm to establish global pathways associated with chronic areca nut exposure. Cell Invasion Assay The invasive abilities of the cells were determined after the cells were cultured on a polycarbonate membrane coated with Matrigel (Becton Dickinson Biosciences, Franklin Lakes, NJ, USA) in a Millicell invasion chamber (Millipore, Billerica, MA, USA), as previously described.36 Briefly, after transfection of the specific sh-Krt plasmids, cells in medium supplemented with 1% FBS were seeded in the upper chamber, which was precoated with Matrigel. The lower chamber contained complete culture medium (containing 10% FBS) to trap invading cells. After incubation at 37 °C, the cells that invaded the reverse side of the chamber were stained with crystal violet and imaged. All the experiments were performed at least twice independently, and the typical results were shown. In each sample, the invasion ability was quantified via a comparison of the density of the crystal violet dye after normalization to the control group. Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) Total RNA extraction and the RT-qPCR assay were performed in a manner similar to a previously described method.32 Briefly, the PCR and cDNA synthesis were performed in a Bio-Rad MiniOpticon real-time PCR detection system using SyBr Green Supermix reagents (Life Technologies, Inc.). The PCR primers used in this study are listed in Table S1. The PCR results, which were recorded as quantitation cycle (Cq) values, are presented as relative fold expression. Immunohistochemistry (IHC) and Hematoxylin and Eosin (H&E) Staining Protein Extraction and Western Blot Analysis The IHC assay was performed as previously described.37 Briefly, murine tumors were fixed in 10% neutral buffered formalin and embedded in paraffin. The slides were then stained with an antiKrt17 antibody (purchased from Santa Cruz Biotech., CA, as described above). The DakoCytomation REAL EnVision detection system (Dako, Carpinteria, CA, USA) was used for further staining and color development according to the manufacturer’s instructions. In addition, each sample was also counterstained with H&E according to the manufacturer’s suggested protocol (Zymed Laboratories Inc.). The stained sections were examined by microscopy. The immunoreactivity was evaluated using the H-scoring system, by assessment of the staining intensity and the fraction of cells.38 Protein extraction and Western blot analysis were performed as previously described.33 Briefly, cellular proteins were extracted, separated by SDS-PAGE, and transferred to a nitrocellulose membrane. The membrane was exposed to primary antibodies and then incubated with horseradish peroxidase-conjugated secondary antibodies. The membranes were developed using an ECL developing solution (Millipore, Darmstadt, Germany) followed by autoradiography. The primary antibodies used in this study are listed in Table S2. shRNA Construction and Cellular Transfection Krt17-targeted shRNA was designed as a 22-nt sense and antisense hairpin that was complementary to the Krt17 mRNA sequence 5′- GAT CCG ATC CTC ACA GCC ACC GTG GAA GCT TGC ACG GTG GCT GTG AGG ATC TTT TTT GGA AGC −3′. The shRNA was cloned into the pTOPO-U6 plasmid vector to produce the sh-Krt17 plasmid in a manner similar to that of a previously described method.34 This sh-Krt17 was subcloned into a pCI-neo plasmid and was used for the stable transfection of sh-Krt17 cells.34 For the plasmid transfection, cells were seeded at a density of 5 × 105 in a 100 mm dish and were cultured for 16 h. When the cells reached 60% confluency, they were transfected with 6 μg of shRNA plasmid or the empty vector plasmid using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) in Opti-MEM reduced serum media (Invitrogen, Carlsbad, CA, USA). After 16 h, the Opti-MEM media was replaced with fresh complete media. The stably transfected cellular clones were selected using G418 antibiotic solution, which is a neomycin reagent (Sigma, St Louis, MO, USA). Spontaneous Induction of Tumor in Mouse In all, 28 female C57BL/6 mice at 6 weeks of age were used in this study, which was approved by the Institutional Animal Care and Use Committee. The mice were handled in accordance with the Animal Care and Use Guidelines of the Chang Gung University. For the tumorigenic induction, arecoline, which is the active carcinogenic compound of the areca nut (Sigma-Aldrich, St. Louis, MO, USA), and 4-nitroquinoline 1-oxide (4-NQO) (Sigma-Aldrich) were given to the mice after the compounds were dissolved in the drinking water in a manner similar to what has been previously described.39,40 These 28 mice were randomly assigned to the experimental group (n = 12), which received both 4-NQO (200 μg/mL) and arecoline (500 μg/mL), the control group (n = 8), which received DMSO (1/100X), or the mock group (n = 8), which received only normal drinking water. In the first 8 weeks, the carcinogen solution was locally smeared over the inner mouth area once per week in addition to its administration through the drinking water. Thereafter, the carcinogen solution was administered only in the drinking water until week 28. The body weight of the mice and the induced oral lesions, such as hyperplasia, dysplasia, or squamous cell carcinomas, were carefully observed every week. The developed tumors were dissected, fixed in 10% formaldehyde, and subjected to a pathological examination and IHC analysis. In regards to the Cell Migration Assay Cell migration was determined using an in vitro wound healing assay, as previously described.35 After transfection of the specific sh-Krt plasmids, 3.5 × 104 cells were seeded in ibidi culture inserts (ibidi LLC, Verona, WI, USA) on top of a 6-well plate. After 8 h of incubation, the culture inserts were detached to form a cell-free gap in the cell monolayer. After changing to the culture 2983 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research Figure 1. Identification of differentially expressed proteins in areca nut extract (ANE)-trained cell sublines. (A) A schematic diagram illustrates the workflow of proteomic profiling after induction by ANE with iTRAQ-based analysis. These four ANE sublines were labeled in parallel with the corresponding iTRAQ reporters. iTRAQ labeling was performed using equal amounts of proteins from pooled parental and ANE-trained sublines followed by SCX chromatography. The fractions were subjected to LC−MS/MS analysis in an LTQ-Orbitrap Velos mass spectrometer. The data were searched and collected with the Proteome Discoverer program using Mascot software as the search engine. The numbers of identified proteins are shown as Venn diagrams. (B) The numbers of proteins identified or quantified in two iTRAQ-based experiments. (C) The numbers of proteins that were determined to be up-regulated or down-regulated in four ANE sublines, including SAS, OECM1, CGHNK2 and CGHNC9 cells. Venn diagrams show the overlap between proteins identified or quantified in the two experiments. The total number of proteins identified or quantified in each experiment is listed in brackets. (D) The relative expression levels of 196 significant proteins in ANE-trained sublines compared with the parental cells. survival analysis, a mouse was defined as “dead” when the mouse was actually dead, when the tumor size was over 7 mm in length, or when the body weight was less than half of the average body weight in the control group when food and water were available. ■ parental control cell lines (Figure 1A). As shown in Figure 1A, for experimental set 1, iTRAQ 114 and 116 reagents were combined with peptides from parental SAS and OECM1 cells, respectively. Peptides from ANE-trained SAS and OECM1 cells were labeled with iTRAQ 115 and 117 reagents, respectively (Table S3). In set 2, iTRAQ 114 and 115 reagents were added to peptide mixtures of parental and ANE-trained CGHNK2 cells, respectively. Peptides from parental and ANE-trained CGHNK2 cells were labeled with iTRAQ 116 and 117 reagents, respectively (Table S4). The iTRAQ-labeled samples were then analyzed by two-dimensional LC−MS/MS for the quantitative proteomic analysis. The two-dimensional fractionation of the labeled peptides involved the use of an offline SCX separation in the first dimension, followed by an online reverse-phase fractionation. Each fraction was analyzed using an LTQ-Orbitrap Discovery system. The resulting MS/MS spectra were analyzed using the Swiss-Prot human sequence database with the Mascot search engine. The search results were further evaluated using the open-source TPP software with stringent criteria regarding RESULTS Proteomic Profiling of Areca Nut Extract (ANE)-Trained Sublines To establish ANE-trained sublines, three oral cancer cell lines (OECM1, SAS, and CGHNC9) and one immortalized noncancerous keratinocyte cell line (CGHNK2) were treated with ANE at an IC30 dose for 3 months. These cells have been functional characterized possessing tumorigenic phenotypes,29 and the results of immortalized keratinocytes were shown as example (Figure S1). To identify proteins that were differentially expressed in ANE-treated cells compared with the controls, we included two experimental sets of iTRAQ-based quantitative proteomics analyses (set 1 and set 2), each of which contained four measurements of two ANE-trained sublines and two 2984 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research Table 1. List of Proteins Differentially Expressed in Areca Nut Extract (ANE)-Trained Cell Lines log2 ratio of ANE-trained/parental cells protein name (protein accession number, gene name) SAS OECM1 CGHNK2 CGHNC9 1.016 0.637 1.515 0.815 −0.146 0.057 1.180 0.716 0.655 0.646 0.807 0.699 0.628 1.186 0.791 0.862 1.387 0.601 3.158 −0.327 −0.193 0.281 −0.070 0.123 0.070 0.475 0.210 −0.041 0.371 0.246 −0.456 −0.026 −0.115 0.959 0.628 0.742 −0.085 −0.177 −0.085 0.030 0.381 −0.225 −0.026 −0.177 −0.130 −0.258 −0.327 0.110 0.186 −0.310 −0.130 NA 0.070 0.043 −0.292 NA 0.173 −0.209 0.781 1.492 0.814 0.937 0.748 1.022 0.877 0.966 0.907 1.180 1.461 1.253 0.877 0.765 0.276 0.472 0.276 −0.321 0.472 0.626 1.192 0.626 0.781 0.679 0.662 1.366 0.861 0.966 0.714 0.679 0.731 1.116 1.180 −0.771 0.205 0.299 0.644 0.980 0.798 0.644 1.180 0.662 0.814 0.697 0.922 0.892 1.063 0.714 0.781 1.344 −0.034 NA −0.678 −0.186 0.299 NA −1.430 −0.321 1.241 1.404 0.659 0.342 0.484 0.342 −0.278 0.051 NAb NA NA NA NA NA 0.398 0.525 0.650 0.687 0.342 0.525 0.376 0.431 0.515 0.584 0.484 0.442 0.504 0.342 0.515 0.420 0.342 0.574 1.146 −0.313 −0.065 −0.192 −0.065 −0.591 −0.065 0.146 −0.035 −0.260 0.198 0.079 0.120 −0.192 0.307 0.120 0.185 0.159 0.354 0.515 0.409 0.603 0.494 0.545 0.452 0.504 −0.343 0.245 −1.442 −1.217 3.009 2.448 1.767 −0.044 NA NA NA NA NA NA 0.161 0.666 −0.928 −0.876 1.711 0.775 −1.217 −0.002 0.970 −0.461 0.180 1.876 −1.643 0.670 −0.360 −0.044 0.880 −0.687 0.117 3.671 3.576 2.760 3.857 2.668 3.452 3.335 2.780 2.983 2.887 3.139 2.427 2.863 3.013 2.474 2.673 3.382 2.961 2.576 2.574 2.634 2.744 3.489 2.546 3.383 Overexpressed proteinsa High mobility group protein HMGI-C (P52926, HMGA2) Keratin, type I cytoskeletal 17 (Q04695, KRT17) SH3 domain-containing kinase-binding protein 1 (Q96B97, SH3KBP1) PDZ and LIM domain protein 5 (Q96HC4, PDLIM5) Bystin (Q13895, BYSL) Histone deacetylase 2 (Q92769, HDAC2) Catalase (P04040, CAT) 3-hydroxyacyl-CoA dehydrogenase type-2 (Q99714, HSD17B10) Uridine-cytidine kinase 2 (Q9BZX2, UCK2) Charged multivesicular body protein 2b (Q9UQN3, CHMP2B) Receptor expression-enhancing protein 5 (Q00765, REEP5) Ubiquitin carboxyl-terminal hydrolase 4 (Q13107, USP4) Transmembrane and coiled-coil domain-containing protein 7 (Q9C0B7, TMCO7) Cellular retinoic acid-binding protein 2 (P29373, CRABP2) Thrombospondin-1 (P07996, THBS1) Epiplakin (P58107, EPPK1) ATPase WRNIP1 (Q96S55, WRNIP1) AT-rich interactive domain-containing protein 1A (O14497, ARID1A) Plasminogen activator inhibitor 2 (P05120, SERPINB2) Serpin B5 (P36952, SERPINB5) E3 SUMO-protein ligase RanBP2 (P49792, RANBP2) Double-strand break repair protein MRE11A (P49959, MRE11A) Adenine phosphoribosyltransferase (P07741, APRT) Histone H1.4 (P10412, HIST1H1E) Histone H1.3 (P16402, HIST1H1D) Integrin alpha-6 (P23229, ITGA6) Myristoylated alanine-rich C-kinase substrate (P29966, MARCKS) Transcription activator BRG1 (P51532, SMARCA4) Endoplasmic reticulum resident protein 44 (Q9BS26, ERP44) NACHT, LRR and PYD domains-containing protein 2 (Q9NX02, NLRP2) Pre-mRNA branch site protein p14 (Q9Y3B4, SF3B14) Telomere length regulation protein TEL2 homologue (Q9Y4R8, TELO2) Aldo-keto reductase family 1 member C2 (P52895, AKR1C2) Isocitrate dehydrogenase [NADP] cytoplasmic (O75874, IDH1) Solute carrier family 2, facilitated glucose transporter member 1 (P11166, SLC2A1) Peptidyl-prolyl cis−trans isomerase B (P23284, PPIB) 26S proteasome non-ATPase regulatory subunit 8 (P48556, PSMD8) Phosphatidylserine synthase 1 (P48651, PTDSS1) Tubulin-specific chaperone A (O75347, TBCA) Pyruvate dehydrogenase E1 component subunit beta, mitochondrial (P11177, PDHB) Integrin beta-4 (P16144, ITGB4) Probable ATP-dependent RNA helicase DDX5 (P17844, DDX5) Endoplasmic reticulum resident protein 29 (P30040, ERP29) 60S ribosomal protein L15 (P61313, RPL15) Aminoacyl tRNA synthase complex-interacting multifunctional protein 2 (Q13155, AIMP2) PCI domain-containing protein 2 (Q5JVF3, PCID2) Probable ATP-dependent RNA helicase DDX46 (Q7L014, DDX46) Hsc70-interacting protein (P50502, ST13) WD40 repeat-containing protein SMU1 (Q2TAY7, SMU1) Eukaryotic translation initiation factor 4 gamma 3 (O43432, EIF4G3) Heterogeneous nuclear ribonucleoprotein H (P31943, HNRNPH1) Mitochondrial import inner membrane translocase subunit TIM44 (O43615, TIMM44) Glucose-6-phosphate 1-dehydrogenase (P11413, G6PD) High mobility group protein B2 (P26583, HMGB2) Splicing factor, arginine/serine-rich 3 (P84103, SFRS3) Terminal uridylyltransferase 7 (Q5VYS8, ZCCHC6) Thioredoxin domain-containing protein 5 (Q8NBS9, TXNDC5) Far upstream element-binding protein 2 (Q92945, KHSRP) 2985 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research Table 1. continued log2 ratio of ANE-trained/parental cells protein name (protein accession number, gene name) SAS OECM1 CGHNK2 CGHNC9 RNA-binding protein Raly (Q9UKM9, RALY) Protein transport protein Sec31A (O94979, SEC31A) Brain acid soluble protein 1 (P80723, BASP1) PDZ domain-containing protein GIPC1 (O14908, GIPC1) Cathepsin D (P07339, CTSD) Laminin subunit beta-1 (P07942, LAMB1) ERO1-like protein alpha (Q96HE7, ERO1L) Mediator of RNA polymerase II transcription subunit 14 (O60244, MED14) Drebrin (Q16643, DBN1) Protein S100-A14 (Q9HCY8, S100A14) Testin (Q9UGI8, TES) Keratin, type II cytoskeletal 7 (P08729, KRT7) Glutathione S-transferase omega-1 (P78417, GSTO1) Spermine synthase (P52788, SMS) Copine-1 (Q99829, CPNE1) Calpain-1 catalytic subunit (P07384, CAPN1) Integrin alpha-3 (P26006, ITGA3) Succinate dehydrogenase [ubiquinone] flavoprotein subunit, mitochondrial (P31040, SDHA) Squalene synthase (P37268, FDFT1) Transgelin-2 (P37802, TAGLN2) Coronin-1B (Q9BR76, CORO1B) [Pyruvate dehydrogenase [acetyl-transferring]]-phosphatase 1, mitochondrial (Q9P0J1, PDP1) UPF0727 protein C6orf115 (Q9P1F3, C6orf115) 7-dehydrocholesterol reductase (Q9UBM7, DHCR7) Glutamate dehydrogenase 1, mitochondrial (P00367, GLUD1) Glucosidase 2 subunit beta (P14314, PRKCSH) Translation initiation factor eIF-2B subunit beta (P49770, EIF2B2) 3′(2′),5′-bisphosphate nucleotidase 1 (O95861, BPNT1) Epithelial cell adhesion molecule (P16422, EPCAM) Probable ATP-dependent RNA helicase DDX6 (P26196, DDX6) Signal recognition particle 14 kDa protein (P37108, SRP14) Transaldolase (P37837, TALDO1) Synaptic vesicle membrane protein VAT-1 homologue (Q99536, VAT1) 60 kDa SS-A/Ro ribonucleoprotein (P10155, TROVE2) D-dopachrome decarboxylase (P30046, DDT) Prostaglandin E synthase 3 (Q15185, PTGES3) Cytochrome c oxidase subunit 2 (P00403, MT-CO2) Heat shock 70 kDa protein 1A/1B (P08107, HSPA1A) Estradiol 17-beta-dehydrogenase 12 (Q53GQ0, HSD17B12) Protein NDRG1 (Q92597, NDRG1) Charged multivesicular body protein 4b (Q9H444, CHMP4B) Double-strand-break repair protein rad21 homologue (O60216, RAD21) Peroxisomal multifunctional enzyme type 2 (P51659, HSD17B4) Underexpressed proteinsa Rab3 GTPase-activating protein catalytic subunit (Q15042, RAB3GAP1) Ribonucleases P/MRP protein subunit POP1 (Q99575, POP1) Small subunit processome component 20 homologue (O75691, UTP20) Probable dimethyladenosine transferase (Q9UNQ2, DIMT1L) Methylosome protein 50 (Q9BQA1, WDR77) dCTP pyrophosphatase 1 (Q9H773, DCTPP1) Fatty acid-binding protein, epidermal (Q01469, FABP5) Lysophospholipid acyltransferase 7 (Q96N66, MBOAT7) Nuclear receptor corepressor 2 (Q9Y618, NCOR2) C-Jun-amino-terminal kinase-interacting protein 4 (O60271, SPAG9) Lamin-A/C (P02545, LMNA) Glycogen phosphorylase, liver form (P06737, PYGL) Electron transfer flavoprotein subunit alpha, mitochondrial (P13804, ETFA) Protein S100-A2 (P29034, S100A2) Squalene synthase (P37268, FDFT1) 0.148 0.515 0.515 0.505 0.505 0.505 1.343 0.534 0.465 0.371 0.424 0.371 0.455 0.413 0.403 0.495 0.646 0.655 0.966 0.534 0.673 0.655 0.424 0.403 0.327 −0.161 −0.146 0.293 0.070 0.030 −0.012 0.084 0.360 0.270 −0.026 0.186 −0.130 0.234 0.258 NA NA −0.070 1.207 0.180 1.008 1.142 −0.186 0.103 −2.678 0.845 0.814 1.560 0.765 0.626 0.697 0.781 −0.508 −0.591 −0.321 0.607 0.077 −1.678 −0.871 −0.871 −0.321 −0.591 0.299 0.697 0.626 0.830 0.765 0.922 0.845 0.922 1.142 0.877 1.430 1.008 −0.123 −0.321 −0.286 −1.286 NA NA 0.388 0.701 0.678 0.079 −0.387 0.431 0.865 0.659 −0.313 NA 0.319 −0.828 −0.081 −0.465 0.079 0.632 0.555 −0.209 −0.226 −1.050 −0.485 −0.127 0.009 −0.127 −0.209 0.247 0.319 0.331 0.319 0.079 0.093 −0.176 0.146 −0.159 −0.802 0.079 −0.527 0.331 0.641 0.319 0.331 0.594 0.442 0.564 0.861 2.693 −0.225 −1.023 0.420 1.527 1.019 2.979 NA 1.173 1.553 0.092 −0.132 1.423 0.192 0.394 2.509 2.225 2.047 2.386 2.005 3.033 4.444 2.271 2.360 0.807 1.826 −0.778 2.401 2.278 2.204 2.127 2.241 2.380 1.995 2.032 2.092 2.214 2.170 2.302 2.175 2.014 2.229 1.893 −0.310 −0.055 −0.852 −0.475 −0.475 −0.903 −0.778 −0.535 −0.475 0.258 0.392 0.360 0.002 −0.258 0.966 −1.508 −0.771 −1.155 −1.093 −1.155 −0.871 0.531 1.344 0.022 −1.034 −0.771 −0.724 −0.771 −1.034 −1.678 −0.681 −0.387 0.146 NA NA 0.515 −0.704 −0.569 −0.728 −0.387 −0.527 −0.728 −0.387 −0.728 −0.485 −0.889 −1.954 1.042 NA NA 0.742 1.327 0.245 −0.480 −0.002 0.758 −0.242 1.143 0.046 2.386 2986 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research Table 1. continued log2 ratio of ANE-trained/parental cells protein name (protein accession number, gene name) Prolyl endopeptidase (P48147, PREP) Puromycin-sensitive aminopeptidase (P55786, NPEPPS) Myosin light polypeptide 6 (P60660, MYL6) Coactosin-like protein (Q14019, COTL1) Non-POU domain-containing octamer-binding protein (Q15233, NONO) Neutral amino acid transporter B(0) (Q15758, SLC1A5) Proteasome activator complex subunit 2 (Q9UL46, PSME2) Nuclear pore complex protein Nup50 (Q9UKX7, NUP50) Mitochondrial import receptor subunit TOM40 homologue (O96008, TOMM40) Cytochrome c oxidase subunit 4 isoform 1, mitochondrial (P13073, COX4I1) Interleukin-1 alpha (P01583, IL1A) Cleavage stimulation factor subunit 2 (P33240, CSTF2) Protein S100-A10 (P60903, S100A10) Rho GDP-dissociation inhibitor 1 (P52565, ARHGDIA) Proteasomal ubiquitin receptor ADRM1 (Q16186, ADRM1) Inosine triphosphate pyrophosphatase (Q9BY32, ITPA) Serine/arginine repetitive matrix protein 2 (Q9UQ35, SRRM2) N-alpha-acetyltransferase 10, NatA catalytic subunit (P41227, NAA10) Tubulin alpha-1A chain (Q71U36, TUBA1A) V-type proton ATPase 16 kDa proteolipid subunit (P27449, ATP6 V0C) Remodeling and spacing factor 1 (Q96T23, RSF1) Histone deacetylase 1 (Q13547, HDAC1) Acyl-protein thioesterase 1 (O75608, LYPLA1) 4F2 cell-surface antigen heavy chain (P08195, SLC3A2) ATP synthase subunit O, mitochondrial (P48047, ATP5O) Ras-related C3 botulinum toxin substrate 1 (P63000, RAC1) 60S ribosomal protein L38 (P63173, RPL38) Prohibitin-2 (Q99623, PHB2) Ribosome production factor 2 homologue (Q9H7B2, RPF2) Sideroflexin-1 (Q9H9B4, SFXN1) Ubiquitin-conjugating enzyme E2 variant 2 (Q15819, UBE2V2) Mitochondrial inner membrane protein (Q16891, IMMT) LIM domain and actin-binding protein 1 (Q9UHB6, LIMA1) Dynamin-like 120 kDa protein, mitochondrial (O60313, OPA1) V-type proton ATPase subunit C 1 (P21283, ATP6 V1C1) 60S ribosomal protein L40 (P62987, UBA52) AP-2 complex subunit beta (P63010, AP2B1) Protein transport protein Sec24A (O95486, SEC24A) Elongation factor 1-beta (P24534, EEF1B2) Protein mago nashi homologue (P61326, MAGOH) Nuclease-sensitive element-binding protein 1 (P67809, YBX1) Sorbitol dehydrogenase (Q00796, SORD) Cullin-1 (Q13616, CUL1) Splicing factor 3B subunit 4 (Q15427, SF3B4) Sec1 family domain-containing protein 1 (Q8WVM8, SCFD1) Cytosolic nonspecific dipeptidase (Q96KP4, CNDP2) N-alpha-acetyltransferase 50, NatE catalytic subunit (Q9GZZ1, NAA50) Malate dehydrogenase, cytoplasmic (P40925, MDH1) 1-phosphatidylinositol-4,5-bisphosphate phosphodiesterase beta-3 (Q01970, PLCB3) AP-2 complex subunit mu (Q96CW1, AP2M1) Heat shock factor-binding protein 1 (O75506, HSBP1) Dedicator of cytokinesis protein 7 (Q96N67, DOCK7) RNA-binding protein 14 (Q96PK6, RBM14) Ras-related protein Rab-1B (Q9H0U4, RAB1B) 26S proteasome non-ATPase regulatory subunit 4 (P55036, PSMD4) 26S proteasome non-ATPase regulatory subunit 14 (O00487, PSMD14) Cytoplasmic dynein 1 light intermediate chain 1 (Q9Y6G9, DYNC1LI1) Acyl-protein thioesterase 2 (O95372, LYPLA2) 2′-5′-oligoadenylate synthase 1 (P00973, OAS1) 2987 SAS OECM1 CGHNK2 CGHNC9 0.434 0.198 0.186 0.016 −0.085 −0.292 0.043 −0.012 −0.619 −0.515 NA NA NA −0.418 NA NA −0.041 NA NA NA NA NA −0.292 −0.418 −0.292 −0.275 −0.363 −0.310 −0.418 −0.399 −0.327 −0.399 −0.327 −0.327 −0.363 −0.345 −0.275 −0.399 −0.292 −0.437 −0.292 −0.275 −0.456 −0.418 −0.292 −0.437 −0.310 −0.026 0.057 0.043 −0.209 0.465 −0.070 0.198 0.057 NA 0.002 NA NA −1.286 −0.678 −0.871 −0.724 −1.093 −1.356 −0.820 −1.356 0.626 0.205 NA NA NA −0.871 NA NA 0.205 NA NA NA NA NA −0.923 −0.771 −0.724 −0.678 −1.286 −1.034 −1.678 −0.724 0.570 −0.430 −0.469 −0.591 0.129 0.589 −0.063 0.205 0.229 −0.321 0.731 0.253 −0.186 −0.123 0.626 0.626 0.451 −0.634 −0.634 −0.678 −1.034 −1.678 −0.634 −1.219 −0.923 NA −0.123 NA NA −0.387 −0.681 −0.406 −0.465 −0.387 −0.591 −0.445 −0.802 0.223 −0.143 −0.880 −0.880 −0.387 −0.368 −0.635 −0.777 −0.387 −0.445 −0.828 −1.020 −0.704 −0.387 −0.313 0.172 −0.050 −0.331 0.172 0.023 0.331 0.079 −0.368 −0.465 −0.368 −0.387 −0.635 −0.991 −0.591 0.065 −0.081 0.235 0.223 0.051 −0.278 0.133 −0.096 −0.020 −0.192 −0.368 −0.426 −0.368 −0.295 0.272 −0.112 0.023 0.235 −0.506 −0.527 −0.569 −0.387 1.466 1.493 2.067 1.498 0.500 0.394 0.491 0.346 −0.743 −0.839 −1.250 −0.839 −0.732 −0.461 −0.876 −0.732 −0.876 −1.080 −0.790 −1.020 −2.065 −0.709 1.072 1.984 0.486 1.579 1.962 2.009 0.405 1.389 0.640 0.436 0.136 −0.155 0.319 0.524 −0.058 −0.902 −0.529 −0.352 −0.590 −0.519 −0.334 −0.369 −0.743 −0.698 −0.415 1.155 1.002 0.567 −0.405 −0.283 −0.387 −0.415 −0.424 −0.378 −0.325 −0.490 −0.396 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research Table 1. continued log2 ratio of ANE-trained/parental cells protein name (protein accession number, gene name) SAS Keratin, type II cytoskeletal 6B (P04259, KRT6B) High mobility group protein HMG-I/HMG-Y (P17096, HMGA1) Calmodulin-like protein 3 (P27482, CALML3) Galectin-7 (P47929, LGALS7) Tumor protein D52 (P55327, TPD52) Succinyl-CoA:3-ketoacid-coenzyme A transferase 1, mitochondrial (P55809, OXCT1) Brain acid soluble protein 1 (P80723, BASP1) Dual specificity mitogen-activated protein kinase kinase 1 (Q02750, MAP2K1) Tight junction protein ZO-1 (Q07157, TJP1) Ras suppressor protein 1 (Q15404, RSU1) 2,4-dienoyl-CoA reductase, mitochondrial (Q16698, DECR1) Probable ATP-dependent RNA helicase DDX60 (Q8IY21, DDX60) Protein TFG (Q92734, TFG) Endophilin-A2 (Q99961, SH3GL1) Tubulin-specific chaperone D (Q9BTW9, TBCD) Signal recognition particle 68 kDa protein (Q9UHB9, SRP68) Exportin-7 (Q9UIA9, XPO7) NSFL1 cofactor p47 (Q9UNZ2, NSFL1C) Retinoblastoma-binding protein 6 (Q7Z6E9, RBBP6) 60S ribosomal protein L12 (P30050, RPL12) Coatomer subunit gamma-2 (Q9UBF2, COPG2) NA −0.041 NA NA NA NA 0.515 NA 0.030 0.173 NA NA NA NA NA NA 1.097 NA NA 0.780 NA OECM1 NA 0.662 NA NA NA NA 1.142 NA 0.229 0.430 NA NA NA NA NA NA 0.388 NA NA 0.575 NA CGHNK2 CGHNC9 −0.527 −0.569 −0.485 −1.050 −0.465 −0.613 −0.387 −0.387 −0.465 −0.387 −0.485 −0.426 −0.506 −0.681 −0.368 −0.485 −0.569 −0.445 −0.350 0.960 0.623 −0.291 −0.611 −0.325 −0.405 −0.387 −0.433 −1.023 −0.519 −0.500 −0.452 −0.480 −0.396 −0.291 −0.369 −0.519 −0.549 −0.317 −0.490 −0.611 0.414 0.331 a Proteins with log2 ratios below the mean of all log2 ratios minus the SD of all log2 ratios were considered to be underexpressed in ANE-trained cells (−0.246, −0.614, −0.343, and −0.268 for SAS, OECM1, CGHNK2 and CGHNC9, respectively), while proteins with ratios above the mean plus the SD were deemed overexpressed in ANE-trained cells (0.366, 0.610, 0.316, and 1.932 for SAS, OECM1, CGHNK2 and CGHNC9, respectively). bProteins that have not been identified or quantified are shown as not available (NA). Functional Pathway Analysis Revealed a Broad Effect of Areca Nut Induction protein probability (≥0.95) and at least two peptide hits for one protein identification (Figure 1A). The false discovery rate (FDR) of protein detection was empirically determined through a comparison of the data set against a random database using the same search parameters and TPP cutoffs. The estimated FDR of 1.0% was calculated as the number of reverse proteins divided by the number of forward proteins. Using this approach, 1405 and 1324 proteins were identified and quantified, respectively, in set 1. In addition, 1614 and 1542 proteins were detected and quantified, respectively, in set 2 (Figure 1B). The details of these proteins are summarized in Tables S-3 and S-4. Among the identified proteins, 1166 were detected in both set 1 and set 2, while 1114 were quantified in both sets (Figure 1B). Proteins with log2 ratios below the mean of all log2 ratios minus the SD of all log2 ratios were considered to be under-expressed in ANE-trained cells (−0.246, −0.614, −0.343, and −0.268 for SAS, OECM1, CGHNK2 and CGHNC9, respectively), while proteins with ratios above the mean plus the SD were deemed to be overexpressed in ANEtrained cells (0.366, 0.610, 0.316, and 1.932 for SAS, OECM1, CGHNK2 and CGHNC9, respectively) (Table S5). On the basis of the cutoffs, 145, 187, 183, and 244 proteins were overexpressed in ANE-trained SAS, OECM1, CGHNK2 and CGHNC9 cells, respectively, compared with parental cells (Figure 1C). Additionally, 126, 184, 207, and 242 were underexpressed in ANE-trained SAS, OECM1, CGHNK2 and CGHNC9 cells, respectively (Figure 1C). Among these differentially expressed proteins, 196 proteins were found to be dysregulated in at least two ANE-trained sublines, including 101 proteins that were overexpressed and 95 that were underexpressed (Figure 1C and Table 1). The relative expression of the 196 significant proteins is summarized in Figure 1D. To obtain a global picture of the functional pathways that are induced by chronic areca nut exposure, the 196 differentially expressed proteins were imported into MetaCore for integrated network analysis, for which an intersection algorithm was used. Several pathways were significantly associated with chronic areca nut extract exposure (P < 10−8). These include pathways that involve cytoskeleton remodeling, regulation of epithelialmesenchymal transition (EMT), cell adhesion-associated cytokines, angiogenesis via IL-8 signaling, activation of adrenergic receptors by the EGFR pathway, and regulation of cell survival and apoptosis via BAD phosphorylation (Figure 2A). Apparently, areca nut exposure induced a broad effect on cellular functions. To explicitly determine the significant pathways induced by chronic areca nut exposure, the EMT regulatory pathway was surveyed. In all, 19 out of 47 (approximately 1/3) identified molecules were matched to the dominant pathway (Figure 2B). The following molecules were shown to be potentially involved: secretory cytokines and growth factors (Wnt, TGF-β, EGF/ FGF2, IL-1β, TNF), cell membrane receptors (Fizzled, EGFR/ TGFR, E-cadherin), cytoplasmic proteins (Occludin, PAI1, ZO1, Tropomyosin, MMP9), and nuclear transcription factors (CREB1, SP1, SMAD2, E2A, c-Jun). This indicates that EMT was markedly induced through multiple molecular pathways. Confirmation of 29 Molecules with Altered Expression in ANE-Treated Cell Sublines To further assess the significance of these molecules in response to chronic areca nut exposure, 29 molecules were independently examined by RT-qPCR, and the results from these four oral parental cell lines were compared with those of the ANE-trained sublines. The results of each molecule in the four paired samples 2988 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research Figure 2. Bioinformatic analysis of the functional pathways of the 196 significant proteins induced by chronic areca nut exposure. (A) Functional classification of 196 significant proteins using Metacore analysis software. (B) Illustration of the pathway of TGF-beta-induced EMT via MAPK. (C) Verification of 29 molecules that were differentially expressed among four paired oral parental (PT) and ANE-trained subline cells by RT-qPCR. (D) Overall view of the differential expression of 29 molecules between parental and ANE-exposed subline cells, with the average fold change (x-axis) and statistical P value (y-axis) for each. are summarized in Figure 2C and Table 2. Twenty-four molecules were confirmed to exhibit more than a 1.5-fold change in expression. To obtain an overall view of the differential expression of these molecules, Figure 2D was plotted to illustrate both the fold change and the statistical P values for each molecule. As shown, most molecules exhibited significant differential expression (>1.5-fold), and the difference observed in ANE-trained sublines was significant (P < 0.05). These results suggest a common set of molecules that are crucially affected by chronic areca nut exposure. Of the 24 molecules, the 8 proteins with most highly upregulated and the 4 proteins with most highly down-regulated were further assessed via a cell-based study. Two oral cancer cell lines (SAS and OECM1) were treated with various doses (0 to 100 μg/mL) of ANE, and the molecular expression levels were determined by RT-qPCR analysis. The relative expression levels of the 8 up-regulated genes are shown in Figure 3A and are summarized in Figure 3B, whereas the relative expression levels of the 4 down-regulated genes are shown in Figure 3C and are summarized Figure 3D. As shown, Krt17, ERP44, WRNIP1, DBN1, PDLIM5, ITGB4, HDAC2, and ITGA6 were all significantly up-regulated in two ANE-trained sublines in a dose-dependent manner (Figure 3A,B). Out of these, Krt17 demonstrated the highest increase, with 5.1-fold and 27.9-fold elevations in SAS and OECM1 cells, respectively. Similarly, although various levels were detected among different cell lines, ARHGDIA, S100A2, SLCIA5 and RAB3GAP1 showed a consistent trend of down-regulation in two ANE-trained sublines (Figure 3C,D). These results further confirmed the significance of these proteins in that they may play important roles associated with areca nut-induced carcinogenesis. Krt17 Contributed to ANE-Induced Cell Mobility via an EMT-Associated Pathway Because Krt17 showed a prominent level of up-regulation in ANE-trained sublines, this molecule was selected for further investigation. First, its expression status and protein level were confirmed in four ANE-trained sublines. As shown in Figure 4A, Krt17 was significantly elevated in four sublines, from 1.6- to 4.7fold. To further validate the effects of this molecule, oral cancer cell lines were treated with various doses of ANE, and the protein levels were determined. Krt17 was consistently induced in a dose-dependent manner, and 2.4- to 8.1-fold increases were observed in three cell lines tested at a dose of 500 μg/mL (Figure 4B). To examine whether the phenotypic alterations induced by areca nut exposure were associated with the Krt17 molecule, a gene knockdown strategy using specific shRNA was applied. Because cytoskeletal remodeling was a prominent mechanism according to the bioinformatic network analysis (Figure 2A), the function of cell mobility was examined. After confirmation of Krt17 silencing in two oral cancer cell lines with stable knockdown of Krt17 (Figure 5A), the effects of ANE induction 2989 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research (E-cadherin) and increased the levels of mesenchymal biomarkers (N-cadherin and Slug) in two cell lines. However, Krt17 knockdown caused resistance to this induction by ANE. In either cell line, no significant alteration was observed in the expression levels of any of these molecules. These results suggested that ANE-induced EMT process was through a Krt17dependent manner. It has been known that TGF-β participates in EMT process; we therefore determined whether the ANE-Krt17-EMT regulatory axis may be mediated by TGF-β. The expression of TGF-β in response to ANE treatment was examined in both parental oral cancer cell lines and Krt17-silencing cancer cell lines. As shown in Figure 5F (for CGHNC9 cells) and Figure 5G (for OECM1 cells), TGF-β was increased after ANE treatment in oral cancer cells. Nevertheless, unlike EMT marker molecules, Krt17 knockdown has no effect on TGF-β expression. These results indicated that ANE rendered a broad effect to induce several molecules including Krt17 and TGF-β. However, this TGF-β induction was Krt17-independent and may not cross talk with Krt-17 induced EMT mechanism. Taking together, our results suggest that Krt17 contributed to ANE-induced cell mobility, which was via an EMT-associated mechanism. Table 2. List of 29 Molecules Differentially Expressed in ANE Sublines As Analyzed by Q-PCR gene symbol KRT17 ERP44 WRNIP1 DBN1 PDLIM5 ITGB4 HMGA2 ITGA6 HMGCS1 SH3KBP1 NDUFAF2 GPRC5A BYSL CRNKL1 NDRG1 THBS1 IQGAP1 IL1A WDR7 HDAC2 VIM NT5E TPM2 STAT1 RAB3GAP1 S100A2 ARHGDIA SLC1A5 POP1 protein name Overexpressed proteins Keratin, type I cytoskeletal 17 Endoplasmic reticulum resident protein 44 ATPase WRNIP1 Drebrin PDZ and LIM domain protein 5 Integrin beta-4 High mobility group protein HMGI-C Integrin alpha-6 Hydroxymethylglutaryl-CoA synthase, cytoplasmic SH3 domain-containing kinase-binding protein 1 Mimitin, mitochondrial Retinoic acid-induced protein 3 Bystin Crooked neck-like protein 1 Protein NDRG1 Thrombospondin-1 Ras GTPase-activating-like protein IQGAP1 Interleukin-1 alpha Methylosome protein 50 Histone deacetylase 2 Vimentin 5′-nucleotidase Tropomyosin beta chain Signal transducer and activator of transcription 1 Underexpressed proteins Rab3 GTPase-activating protein catalytic subunit Protein S100-A2 Rho GDP-dissociation inhibitor 1 Neutral amino acid transporter B Ribonucleases P/MRP protein subunit POP1 average P value 8.110 7.718 7.431 4.860 4.203 3.905 3.349 3.032 2.797 −0.009 −0.627 −0.749 −0.363 −0.223 −0.549 −0.364 −0.063 −0.040 2.783 −0.167 2.770 2.632 2.578 2.530 2.228 2.223 1.963 −0.522 −0.102 −0.087 −0.426 −0.075 −0.032 −0.039 1.853 1.817 1.310 1.078 1.033 0.990 0.673 −0.574 −0.669 −0.260 −0.039 −0.990 −0.856 −0.430 −2.896 −0.214 −2.473 −1.940 −1.636 3.159 −0.250 −0.376 −0.982 −0.074 Krt17 Was Elevated in Oral Lesions in a Mouse Model of Spontaneous Tumor Induction To authenticate the molecular effect of Krt17 on ANE-induced oral tumors, a mouse model of spontaneous tumorigenesis was established. After carcinogen exposure for 28 weeks, all mice were sacrificed, and the tongue tissues were dissected for pathologic examination. In the mock or control group, no obvious lesions were found in the tongue or in the inner mouth area. However, in the arecoline treatment group, malignant lesions were observed in the right or upper lateral tongue, some of which had invaded deep into the skeletal muscle. In this group, the incidence rate in the mice was 83% (10/12), including 42% (5/12) that developed hyperplasia or dysplasia and 42% (5/12) with verrucous hyperplasia (VH) or squamous cell carcinoma (SCC) (Table 3). No lesions were observed in any other tissues, such as the esophagus, liver, colon, kidney, spleen or stomach. The dissected tongue tissues were subjected to immunostaining and pathological review. To examine whether Krt17 was associated with arecoline-induced oral tumorigenesis, we analyzed Krt17 protein expression in the tongue tissues of mice by H&E stain and IHC. Examples of Krt17 levels in various pathological states are shown in Figure 6A. In the nontreatment group (mock or control), all tissues showed normal (i.e., negative) Krt17 expression (tissues a, b). In the arecoline treatment group, the level of Krt17 expression varied among the mice but was dependent on the pathologic status. In general, grossly normal tissue (tissues c, d) showed weak Krt17 staining, hyperplastic or dysplastic tissues (tissues e, f) displayed moderate staining, and areas with verrucous hyperplasia or squamous cell carcinoma (tissues g, h) showed strong Krt17 staining. The Krt17 expression level was quantified by H-scoring system, which assessed the staining intensity and the percentage of positive cells in each tissue section. The results are shown in Figure 6B. Compared with the mock group, the H-score of the control group was slightly elevated (1.34 fold), but no significant difference was observed (P = 0.201). However, the scores were substantially higher in all tissues from the arecoline treatment group, and these exhibited an increasing trend following pathologic progression. On average, Krt17 was up-regulated by 4.54, 5.28, and 5.84 fold in normal tissues, hyperplastic/ on cellular migration and invasion were determined by in vitro wound healing and Matrigel invasion assays, respectively. As shown, the migratory ability of the cells was enhanced with an increase in ANE treatment dose in CGHNC9 (Figure 5B) and OECM1 (Figure 5C) cancer cell lines. The knockdown of Krt17 expression substantially inhibited ANE-induced cell migration, as approximately 50% inhibition was observed at the highest dose of ANE treatment in these two cell lines. Similarly, Krt17 knockdown significantly suppressed ANE-induced cell invasion, as 95% and 55% suppression was observed at the highest ANE dose in CGHNC9 (Figure 5D) and OECM1 cancer cell lines, respectively (Figure 5E). These results indicated that ANE promoted the migration and invasion of these cancer cells through a Krt17 regulatory pathway. Recent reports have indicated that EMT plays a significant role in the facilitation of cell invasion.41,42 Therefore, we investigated whether the contribution of Krt17 to ANE-induced cell mobility is associated with EMT. The epithelial marker E-cadherin, the mesenchymal marker N-cadherin, and the EMT regulator Slug were examined in two oral cancer cell lines. As shown in Figure 5F for CGHNC9 cells and in Figure 5G for OECM1 cells, although various levels of different molecules were detected, ANE treatment reduced the expression of epithelial biomarkers 2990 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research Figure 3. Effects of the expression of 12 proteins in response to ANE treatment. The oral cancer cells SAS and OECM1 were treated with 50 or 100 μg/ mL of ANE for 24 h. The mRNA was extracted and subjected to qRT-qPCR analysis for each molecule. Relative expression levels were determined after normalization to GAPDH expression (internal control). (A) The differential expression of 8 up-regulated genes induced by ANE: KRT17, ERP44, WRNIP1, DBN1, PDLIM5, ITGB4, HMGA2, and ITGA6. (B) Relative expression levels of 8 up-regulated genes in SAS and OECM1 cells. (C) Differential expression levels of 4 down-regulated genes induced by ANE: ARHGIA, S100A2, SLCIA5, and RAB3GAP1. (D) Relative expression levels of 4 down-regulated genes in SAS and OECM1 cells. 2991 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research Figure 4. Krt17 protein expression is up-regulated by ANE. (A) Krt17 expression is up-regulated in response to chronic areca nut exposure, as observed in four pairs of parental (PT) and ANE sublines by Western blot analysis. (B) Krt17 expression is up-regulated in a dose-dependent manner in three oral cancer cell lines (OECM1, CGHNC9 and SAS) and one immortalized normal keratinocyte cell line (CGHNK2) upon short-term (24 h) ANE treatment (0−500 μg/mL). In each sample, the relative expression of Krt17 was determined after normalization to the GAPDH expression level, as shown in the right panel. Over 1000 proteins were identified in four areca nut-trained oral cell sublines. Out of these, 196 proteins were found to be dysregulated in at least two sublines (Table 1), suggesting the importance of the participation of these proteins in areca nutinduced pathogenesis. The 196 differentially expressed proteins were imported into MetaCore for an integrated network analysis by using an intersection algorithm. Unlike previous reports,6,7,23 the mechanisms that involve DNA damage or epigenetic alterations were less significant in our study. This may be explained by the lack of cytotoxicity and genotoxicity elicited by this chronic areca nut-trained cellular model.29 However, the regulation of cell migration, including cytoskeletal remodeling and EMT, was the most prominent (Figure 2A). This result was consistent with that of previous reports in that biological pathways that mediate cell adhesion or tumor invasion were commonly found to be dysregulated in patients with oral cancer.25−27 Thus, the functional changes in cell mobility may be a crucial effect of areca nuts during oncogenic transformation. Currently, the molecular mechanism of ANE leading to oral carcinogenesis is still unclear. Thus, far, there is no ANE associated receptor found in cancer cells. However, arecoline, the major abundant component of ANE, has been reported exerting its effects through M1, M2, and M3 muscarinic acetylcholine receptors in neuron and muscular cells.43−45 It may be worthy to determine that whether ANE contributing to oral carcinogenesis through muscarinic acetylcholine receptors as well. For molecular pathways, our integrated network analysis (Figure 2A) revealed that ANE may render a broad effect of molecular activations, including IL-8, EGFR, TGF-β, IGF-1, HSP70, and TLR signaling pathways. Thus, ANE facilitates oral carcinogenesis may be through multisignaling mechanisms. This postulate is awaited to be investigated. Twenty-nine molecules were subjected to an independent examination by RT-qPCR. Although various levels of these proteins were observed among the different cell lines, 24 molecules were confirmed to exhibit more than a 1.5-fold differential expression on average in the four cell lines tested dysplastic issues, and in the case of VH/SCC, respectively, according to gross pathology (P < 0.001 in all groups). This result suggests that Krt17 was induced by arecoline and that it contributed to the carcinogen-induced tumorigenesis in these mice. This elevated expression of Krt17 during carcinogenesis in mice was further supported with clinical investigation. The Krt17 levels in oral cancer tissues from various clinical stages have been determined using IHC method. As shown in Figure S2, the Krt17 expression in the early stage (stage I−II) cancers was lower than that in the late-stage (stage III−IV) cancers. Since it is well accepted that cancers with advanced stage are correlated with poor prognosis, these results suggest that Krt17 expression may be associated with poor prognosis in oral cancer patients. ■ DISCUSSION Oral cancer is one of the most common malignant diseases worldwide, and the areca nut is a primary carcinogen that causes this cancer in Southeast Asia. Previously, several screening methods, such as microarrays, have been used to globally survey the gene alteration profiles of individuals with oral cancer caused by areca nut exposure.22,24−27 However, across various studies, little overlap among genes has been observed. These diverse results may be due largely to the heterogeneity that is present in different patient samples, various gene screening modalities or underlying multifaceted patho-etiological mechanisms among different sample sets. To reduce sample heterogeneity of areca nut-induced oral carcinogenesis, in this study, we used four isogenic sublines of oral cells that were chronically exposed to areca nut extract, which were then compared with the corresponding parental cells. To reveal the molecular basis of the carcinogenic mechanism involved, we performed quantitative proteomic analyses to globally determine the proteomic profile and thus elucidate the relevant biological processes. To our knowledge, this is the first report on proteomics relevant to areca nut-associated diseases. 2992 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research Figure 5. Krt17 knockdown suppresses the effects of ANE-induced cell migration and invasion. (A) Krt17 expression was knockdown by Krt17-specific shRNA in CGHNC9 and OECM1 cells. In each sample, the relative expression of Krt17 was determined after normalization to the GAPDH expression level, as shown in the right panel. (B,C) The effects on cell migration as determined by an in vitro wound healing assay in CGHNC9 cells (B) and in OEM1 cells (C). Cells in which Krt17 was stably knocked down and control cells were treated with various doses of ANE (0−100 μg/mL) for 24 h, and the cell migration ability was determined, as described in the Methods section. Cell migration toward the gap was observed, imaged, and quantified at the indicated times. (*p < 0.05, **p < 0.01, t test). (D,E) The effects on cell invasion as determined by a Matrigel invasion assay in CGHNC9 cells (D) and in OEM1 cells (E). Cells in which Krt17 was stably knocked down and control cells were treated with various doses of ANE (0−100 μg/mL) for 24 h, and the cell invasion ability was determined, as described in the Methods section. The cells that invaded through the Matrigel-coated membranes to the lower chamber were stained, imaged, and quantified after 16 h. (*p < 0.05, **p < 0.01, t test). (F,G) Krt17 knockdown abolished ANE-induced epithelial-mesenchymal transition (EMT). Relative expression levels of EMT-associated molecules in Krt17 knockdown cells and the control (parental) CGHNC9 cells (F) and OECM1 cells (G). In each sample, the expression levels of an epithelial biomarker (E-cadherin) and mesenchymal biomarkers (N-cadherin and Slug) were determined by real-time RT-PCR after normalization to GAPDH expression (internal control). Krt17, which exhibited the most prominent alteration in response to chronic areca nut treatment, was further investigated. Krt17 is part of the keratin family and is multifunctional. In epithelial tissues, a network of proteins links the nucleus to the cell membrane through keratin filaments, in which transmembrane proteins provide the basis for cell−cell and cellextracellular matrix adhesion.46,47 Krt17 belongs to the type-1 (Figure 2C,D). These results suggest a high probability that the proteomic analysis is valid in the present study. Among the 24 proteins, 12 molecules were further confirmed via cell-based assays, and all of these showed a consistent trend of altered expression in two cell lines (Figure 3). These results suggest a panel of proteins that may participate in the mechanisms of areca nut-induced pathogenesis. 2993 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research acidic epithelial keratin family, the members of which heterodimerize with Krt6b to form an intermediate filament network that attaches to desmosomes at points of cell−cell contacts.46,47 In addition to its scaffolding function, Krt17 has also been reported to be highly expressed in carcinomas, presumably taking part in tumorigenesis via the regulation of wound healing and cell growth, two processes that require rapid cytoskeletal remodeling. For examples, Krt17 has been reported overexpressed in malignant lesions of the cervix,48 associated with the aggressiveness of head-neck cancer,49,50 and promotes Table 3. Summarization for Incidences of Tumorigenesis Induced by Arecoline treatment a tissue status mock (n = 8) DMSO (n = 8) Arecoline (n = 12) health hyperplasia VH/SCCa 100% (8/8) 0% (0/8) 0% (0/8) 100% (8/8) 0% (0/8) 0% (0/8) 16.6% (2/12) 41.7% (5/12) 41.7% (5/12) VH, verrucous hyperplasia; SCC, squamous cell carcinoma. Figure 6. Krt17 is elevated in the oral lesions of mice with spontaneous tumor induction. (A) Immunohistochemistry (IHC) for the Krt17 protein in the tongue tissues from mice with spontaneous tumor induction. The groups of mice were classified according to treatment condition and pathological status as indicated above. The representative normal tissues without treatment (a, b), normal tissues with arecoline cocktail treatment (c, d), hyperplasia or dysplasia (e, f), and verrucous hyperplasia (VH) or squamous cell carcinoma (HCC) (g, h) are shown. For each sample, the H&E stains are also presented below. The immunoreactivity was evaluated by subjective assessment of the median staining intensity, as negative (0: no staining), weak (+1), moderate (+2), or strong (+3). (B) The scatter dot plot of the H-scoring for Krt17 protein expression in each tongue specimen from mice with spontaneously induced tumors. The groups of mice were classified according to treatment condition and pathological status as indicated below. (***p < 0.001, t test). 2994 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research skin hyperplasia.51 Consistently, our data showed that Krt17 contributes to the malignant phenotype, as the knockdown of this protein attenuated areca nut-induced cell migration (Figure 5B,C) and cell invasion (Figure 5D,E) in two oral cancer cell lines. Moreover, this phenotypic alteration is accompanied by EMT (Figure 5F,G), which may be associated with the regulation of the Krt17 molecule by phosphorylation.52 The function of Krt17 is further demonstrated in our animal study. It was shown to be induced by arecoline cocktail and was associated with tumorigenesis (Figure 6). Moreover, the elevation of Krt17 in grossly normal tissue (Figure 6B) indicates that this molecular change occurred much earlier than cellular alterations. Therefore, Krt17 may play a role in early tumor initiation during oral carcinogenesis. This implies that Krt17 may serve as a biomarker for early screening or for susceptibility assessment of areca nut-induced oral cancer. Nevertheless, the mechanism by which Krt17 contributes to areca nut-induced malignant transformation is currently unclear. Further characterization of the Krt17 signaling pathway is needed to discover its molecular network and to provide additional insights concerning the regulatory mechanism of areca nut-induced cancer progression. In conclusion, we have identified a proteome that is associated with chronic areca nut exposure in oral cancer cells. We found 196 proteins that were commonly expressed in more than two cell lines. An algorithm-based analysis revealed a broad cellular effect of areca nut induction, while cytoskeletal remodeling via EMT was the most significant. Twelve molecules, including Krt17, were confirmed to be highly associated with areca nut induction; Krt17 was further demonstrated to contribute to oral transformation. 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Supporting methods, tables, and figures (PDF) ■ REFERENCES AUTHOR INFORMATION Corresponding Authors *Tel: 886-3-3281200, ex 7008. Fax: 886-3-2118247. E-mail: [email protected]. *Tel: 886-3-2118800, ex 5085. Fax: 886-3-2118247. E-mail: [email protected]. Author Contributions # C.-H.C. and C.-C.W. contributed equally. Notes The authors declare no competing financial interest. ■ ACKNOWLEDGMENTS This work was supported by grants to Ann-Joy Cheng from the Ministry of Science and Technology (MOST), Taiwan (1012314-B-182A-121-MY3) and Chang Gung Memorial Hospital (CGMH), Taiwan (CMRPD1A0643 and CMRPD1E0032-3) and grants to Chih-Ching Wu from the MOST (102-2320-B2995 DOI: 10.1021/acs.jproteome.6b00138 J. Proteome Res. 2016, 15, 2981−2997 Article Journal of Proteome Research high sensitivity and specificity for early detection of oral cancer. Clin. Biochem. 2015, 48 (3), 115−21. (33) Lee, L. Y.; Chen, Y. J.; Lu, Y. C.; Liao, C. T.; Chen, I. H.; Chang, J. T.; Huang, Y. C.; Chen, W. 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