Distinct Protein Expression Profiles of Solid-Pseudopapillary

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Distinct Protein Expression Profiles of Solid-Pseudopapillary Neoplasms of the Pancreas Minhee Park,† Jong-Sun Lim,§ Hyoung-Joo Lee,§ Keun Na,§ Min Jung Lee,§ Chang Moo Kang,‡ Young-Ki Paik,§ and Hoguen Kim*,† †

Departments of Pathology and BK21 for Medical Science and ‡Department of Hepatobiliary and Pancreatic Surgery, Yonsei University College of Medicine, Seoul 120-752, Korea § Yonsei Proteome Research Center and Department of Integrated Omics for Biomedical Science, World Class University Program, Yonsei University, Seoul 120-749, Korea S Supporting Information *

ABSTRACT: Solid-pseudopapillary neoplasm (SPN) is an uncommon pancreatic tumor with mutation in CTNNB1 and distinct clinical and pathological features. We compared the proteomic profiles of SPN to mRNA expression. Pooled SPNs and pooled non-neoplastic pancreatic tissues were examined with high-resolution mass spectrometry. We identified 329 (150 up-regulated and 179 down-regulated) differentially expressed proteins in SPN. We identified 191 proteins (58.1% of the 329 dysregulated proteins) with the same expression tendencies in SPN based on mRNA data. Many overexpressed proteins were related to signaling pathways known to be activated in SPNs. We found that several proteins involved in Wnt signaling, including DKK4 and βcatenin, and proteins that bind β-catenin, such as FUS and NONO, were up-regulated in SPNs. Molecules involved in glycolysis, including PKM2, ENO2, and HK1, were overexpressed in accordance to their mRNA levels. In summary, SPN showed (1) distinct protein expression changes that correlated with mRNA expression, (2) overexpression of Wnt signaling proteins and proteins that bind directly to β-catenin, and (3) overexpression of proteins involved in metabolism. These findings may help develop early diagnostic biomarkers and molecular targets. KEYWORDS: solid-pseudopapillary neoplasm, protein expression profiles, mRNA expression

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INTRODUCTION SPN is an uncommon pancreatic tumor. In the WHO classification, all solid-pseudopapillary neoplasms (SPNs) are classified as low-grade malignant neoplasms. Typically, SPNs are solitary and found in young women; they sometimes show direct extension to the stomach, duodenum, and spleen, and metastasis to the peritoneum or liver is reported in 5−15% cases. At present, however, no biological or morphological markers have proven to be predictive of outcomes for SPNs.1 Genetically, most SPNs exhibit somatic mutations in exon 3 of CTNNB1 and rare alterations in KRAS, TP53, CDKN2A, and SMAD4, which are common in pancreatic adenocarcinomas.2 Mutations in exon 3 of CTNNB1, which encodes β-catenin,2,3 result in the activation of the Wnt/β-catenin pathway, abnormal β-catenin nuclear translocation, and the activation of transcription factors through the formation of a β-catenin−Tcf/Lef complex.45 These molecular alterations are implicated in the pathogenesis of solid-pseudopapillary neoplasm. The characteristic nuclear accumulation of β-catenin in SPN is used in the diagnosis of SPNs.6 To date, two studies have reported molecular regulatory networks in SPNs using mRNA expression profiling studies.7,8 Both studies demonstrated activation of the Wnt/β-catenin © 2015 American Chemical Society

signaling pathways. Notch signaling was reported as an additional signaling pathway that was activated in one study, and Hedgehog and androgen receptor signaling pathways were reported to be activated in SPN in another study.8 Although several genetic or mRNA expression studies of SPNs have been reported in SPNs, proteome profiling has not yet been reported. Moreover, the direct comparison of the mRNA and proteome expression profiles of SPN, which may provide new insights for the molecular pathogenesis of SPNs, has not been performed. In this study, we performed an integrative analysis of proteome expression in SPNs and matched non-neoplastic pancreatic tissues to discover the relationships between proteomes and transcriptomes.9−11 We identified protein expression profiles of SPNs and compared these results to those of the mRNA expression. These findings may be useful for the development of early diagnostic biomarkers and molecular therapeutic targets for SPNs. Received: July 9, 2014 Published: July 7, 2015 3007

DOI: 10.1021/acs.jproteome.5b00423 J. Proteome Res. 2015, 14, 3007−3014

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Journal of Proteome Research

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MATERIALS AND METHODS

was used for peptide separation. LTQ-Orbitrap mass spectrometry (Thermo Fisher San Jose, CA, USA) was used to identify and quantify peptides. Xcalibur (version 2.1, Thermo Fisher Scientific, MA, USA) was used to generate peak lists.

Case Selection and Protein Preparation

A total of seven SPNs and seven non-neoplastic pancreatic tissue samples were used in this study. The clinicopathological features of patients with a solid-pseudopapillary neoplasm of the pancreas are listed in Table 1 in the Supporting Information. The selected non-neoplastic tissues were obtained from grossly normal-looking pancreatic sections among the resected SPN specimens or other tumors, and all pancreatic tissues showed no evidence of chronic pancreatitis or preneoplastic lesions on histologic examination. The specimens were obtained from the archives of the Department of Pathology, Yonsei University (Seoul, Korea) and from the Liver Cancer Specimen Bank of the National Research Resource Bank Program of the Korean Science and Engineering Foundation of the Ministry of Science and Technology. Patient data were collected retrospectively from hospital records. All patients received pancreatic resection between 2002 and 2012, and fresh snap-frozen samples were obtained immediately at the time of surgery. All SPN samples consisted of more than 70% tumor cells, and none of the patients had received neoadjuvant chemotherapy. Authorization for the use of these tissues for research purposes was obtained from the Institutional Review Board of Yonsei University of College of Medicine. Tissue lysates were prepared using lysis buffer (6 M urea, 2 M thio urea, and 2% CHAPS) with ultrasonication.

Peptide Identification and Quantification

Proteome Discoverer (version 1.4, Thermo Fisher San Jose, CA, USA) software was used for protein identification and quantification as previously reported. Proteome Discover 1.4 software was used to generate quantitative values and normalization with strict criteria. The software used only unique peptides of target proteins for quantification and excluded unusual changes in quantitative values. All quantitative results were normalized using protein medians, and if not all of the quant channels were present, the quant values were rejected. Also, it measured variations in all proteins and performed normalization to eliminate a variety of possible technical errors. MASCOT (Matrix Science, London, U.K.; version 2.2.04) was used to identify peptide sequences present in the protein sequence database UniProt_HUMAN_12_06 (release date June 2012). Database search criteria were as follows: taxonomy, Homo sapiens (86 875 sequences; 36 599 288 residues); carboxyamidomethylated (+57) at cysteine residues for fixed modifications; oxidized at methionine (+16) residues for variable modifications; two maximum allowed missed cleavage; 10 ppm MS tolerance; an 0.8 Da CID, and a 50 ppm (highenergy collision dissociation [HCD]) MS/MS tolerance. Quantification was performed by calculating the ratio between the peak areas of the TMT reporter groups. To eliminate the masking of changes in expression due to peptides that were shared between proteins, we calculated the protein ratio using only ratios from the spectra that were distinct to each protein. All quantitative results were normalized using protein medians (minimum protein count of 20). If one of the quant channels was missing, the quant values were rejected.12 Reversed sequences were used for evaluation of the false discovery rate (FDR < 1%). We used FDR < 1% as a criterion for screening significant proteins in a large pool by combining mRNA expressional profiles.

In-Solution Tryptic Digestion

A total of 50−100 mg of fresh tissue samples were used for proteomics analysis. The concentration of extracted protein was about 5−15 μg/μL, depending on the weight of each tissue. Denaturation and reduction of samples were performed in 8 M urea, 25 mM dithiothreitol, and 25 mM NH4HCO3 (pH 8.0) for more than 30 min. The solution was stored at room temperature in 25 mM iodoacetamide in the dark for 20 min. The urea was diluted to a concentration of 1 M with 25 mM NH4HCO3 and then digested with Pierce trypsin protease, MS grade (Thermo Scientific Co, Rockford, IL, USA), with an enzyme-to-substrate ratio of 1:50 at 37 °C with shaking for 16 h. Tandem Mass Tag Labeling

Integration of Proteome Data with mRNA Expression Data and Functional Enrichment Analysis

Each 100 μg sample in 20 μL of 500 mM TEAB was reduced, alkylated, digested, and labeled with tandem mass tag (TMT) reagents (Thermo Fisher Scientific) as previously reported.12 We performed proteomic-based TMT labeling of pooled SPNs and normal samples. To avoid technical errors, we split the pooled specimens into two groups, further divided into thirds for repeated experiments. In total, 126 samples in the first duplicate set, 128 samples in the second set, and 130 samples in the third set were used for labeling pooled normal samples, while 127 samples in the first duplicate set, 129 samples in the second set, and 131 samples in the third set were used for labeling pooled tumor samples. We excluded proteins that showed variations of ≥1 ± 0.2 among the pooled normal samples and then selected proteins within a ratio of triplicates ≤1 ± 0.2 for further analysis.

Preparation and statistical analysis of mRNA gene expression data were described in a previous study.8 To identify the proteome−mRNA network of SPNs, we first integrated the proteomic profiles of SPNs with the mRNA expression profiles of SPNs. Integration between proteomics and mRNA data were performed using full proteomics data (with filtered proteins with an FDR < 1% and a cutoff ratio greater than ±1.5-fold only) and mRNA data. Finally, we selected differentially expressed proteins that were also up- or down-regulated at the mRNA level. Functional enrichment analysis with the selected proteins was performed using DAVID software13 to identify gene ontology biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.14

LC−MS/MS analysis

Quantitative RT-PCR

Nano-high-performance liquid chromatography mass spectrometry (nano-LC−MS/MS) was performed as previously reported.12 Briefly, nano-LC was performed with an Easy nLC 1000 system (Thermo Fisher Scientific, MA, USA). A C18nanobore column (150 mm × 0.1 mm, 3 μm pore size, Agilent)

The quantitative reverse-transcription PCR (qRT-PCR) primer sequences for FUS (PrimerBank ID 283135172c1), NONO (PrimerBank ID 224028243c2) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH; PrimerBank ID 2282013a2) were obtained from the Primer Bank database (http://pga.mgh. 3008

DOI: 10.1021/acs.jproteome.5b00423 J. Proteome Res. 2015, 14, 3007−3014

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Figure 1. Workflow overview of proteomics. (A) Workflow includes tryptic digestion, TMT labeling, mixing, fractionation, and quantification with LC−MS/MS. Proteomic profiles were obtained for pooled non-neoplastic tissues and tumor tissues in triplicates as two sets. (B) In the proteomics data analysis workflow, there were three main parts for the analysis of LC−MS/MS data: preprocessing, statistical testing, and integration of gene expression data. Selected proteins were used for the identification of pathways that are activated in SPNs. Preprocessing involves filtering data (>FDR 1%); we finally selected 329 proteins that showed a more than ±1.5-fold change compared to that of normal tissues. We integrated these data with the mRNA expression data and selected 329 proteins that matched the mRNA data. We performed pathway analysis using DAVID and Cytoscape software.

harvard.edu/primerbank/). The reaction was performed in a final volume of 20 μL with Premix Ex Taq (Takara Bio Inc., Otsu, Japan) according to the manufacturer’s instructions. All reactions were run on an ABI Prism 7300 real-time PCR system in triplicate. Analysis of the results was conducted with quantity value using 7300 system SDS software (Applied Biosystems), and GAPDH was used for normalization. Western Blotting

Whole lysates were prepared using passive lysis buffer (Promega) with a protease inhibitor cocktail (Roche). Total protein lysates (40 μg) were loaded into each lane, sizefractionated by SDS-PAGE, and transferred to a nitrocellulose membrane that was blocked with Tris-buffered saline with Tween 20 containing 5% skim milk. Primary antibodies against β-catenin (cat no. 610154, lot 4171778, BD Biosciences), FUS (cat no. 11570-1-AP), NONO (cat no. 11058-1-AP, Proteintech), and β-actin (cat no. 4967, Cell Signaling) were incubated with the membranes for overnight at 4 °C. After washing, membranes were incubated with a secondary goat anti-rabbit or mouse IgG−HRP-conjugated antibody (Santa Cruz Biotechnology), washed, and then developed using Western blotting luminol reagent (sc-2048, Santa Cruz Biotechnology). Western blot images were analyzed with an LAS-4000 mini camera (Fujifilm, Tokyo, Japan).

Figure 2. Integration of proteome and mRNA expression data. (A) Venn diagrams showed overlap between the identified proteins and the mRNA expression data. Among 551 proteins matched with mRNA data, 150 proteins were up-regulated, and 179 proteins were downregulated. Among the 150 up-regulated proteins, 87 proteins (58.0%) showed the same tendency with mRNA expression. Among the 179 down-regulated proteins, 104 proteins (58.1%) showed the same tendency with mRNA expression. (B) According to the protein and mRNA expression patterns, 551 proteins formed nine clusters. Genes were clustered and used for the analysis of proteins that showed the same expressional patterns at the mRNA and protein levels; genes in clusters 2 and 8 were used for the analysis of proteins that showed changes at the protein level but not the mRNA level. Red and green denote up- and down-regulated proteins (or genes) in SPN compared to those of non-neoplastic pancreatic tissues.

Immunohistochemistry

Formalin-fixed and paraffin-embedded tissue blocks were cut into 4 μm sections. Immunohistochemical analysis was performed using a Ventana XT automated stainer (Ventana Corporation) with antibodies against FUS (1:100), NONO (1:200; Proteintech), and β-catenin (1:100; cat no. 610517, lot 3315527, BD Biosciences).

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quantitation (iTRAQ) system and TMT labeling techniques. This allowed us to quantify differences in the amounts of proteins and peptides across multiple samples in a single MS experiment.15,16 To determine the abundance of proteins that were altered in tumors, we first attempted a quantitative analysis with proteomic-based TMT labeling. Thus, three sets of SPN pools (normal and tumor sections) with seven samples each were labeled with TMT reagent (126, 128, and 130 for the three normal pools and 127, 129, and 131 for the tumor pools). Pooled normal and SPN tumor samples were mixed at a 1:1 ratio and fractionated using OFFGEL electrophoresis. Each fraction was analyzed by LC−MS/MS using sequential

RESULTS

Quantitative Analysis of Differentially Expressed Proteins in SPN Using TMT Labeling

We analyzed changes in protein abundance across biological samples with the isobaric tags for relative and absolute 3009

DOI: 10.1021/acs.jproteome.5b00423 J. Proteome Res. 2015, 14, 3007−3014

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Figure 3. Protein expression analysis of selected genes by Western blotting (A), qRT-PCR (B), and immunohistochemistry (C). (A) β-catenin, PKM2, ENO2, HK1, NONO, and FUS were increased in SPNs compared with that of paired normal tissue. (B) Boxplots for the differences between normal and SPN samples showed that the mRNA expression of FUS and NONO were up-regulated (*, P < 0.05). (C) Immunohistochemical analysis of β-catenin, NONO, and FUS. All seven analyzed SPNs showed strong nuclear expression of β-catenin, FUS, and NONO.

proteins, 24 (4.36% of 551 identified proteins) overlapped with the genes that were specifically up-regulated in SPNs on the mRNA array (1 119 genes, 3.6% of 31 334 genes from the mRNA array).8 We evaluated the signaling pathways that were activated in SPNs according to the proteome data but did not find significant pathways. Therefore, we examined the expression of proteins involved in Wnt signaling, Hedgehog signaling, and androgen receptor signaling pathways, which we have previously reported as being activated in SPNs according to mRNA expression analysis. We found that proteins involved in Wnt/β-catenin signaling [DKK4 (8.06-fold), and CTNNB1 (1.68-fold)] or proteins that bind to β-catenin [FN1 (5.9-fold), SELENBP1 (4.99-fold), DDX5 (3.09-fold), YWHAZ (2.24-fold), NONO (1.89-fold), BGN (1.80-fold), hnRNPM (1.71-fold), and FUS (1.51-fold)] were up-regulated (Figure 3A and Table. 1). We further confirmed that the mRNA levels of FUS and NONO were upregulated by quantitative RT-PCR analysis (Figure 3B). In addition to the distinct protein expression changes involved in the signaling pathways of SPN, we found that many proteins involved in metabolism were up-regulated. A large number of proteins in this category showed increased levels of protein and mRNA. For example, increased protein levels of PKM2 (6.81-fold), ENO2 (5.25-fold), and HK1 (3.37fold) were found in accordance to their relative mRNA

combination scanning, CID, and HCD mode. All raw data were subjected to Proteome Discoverer for quantitative analysis (Figure 1A). We identified 551 proteins with high confidence (FDR < 1%). Among them, 329 proteins (150 up-regulated and 179 down-regulated) were further selected for quantitative analysis (Figure 1B). We chose 329 differentially expressed proteins with an SPN/normal ratio of 1.5 because we wanted to include more proteins for the identification of altered molecular pathways in SPNs. Additionally, the ratio for β-catenin, a key altered protein in SPNs, was 1.68. Our purpose of this study was to identify altered molecular pathways by proteome analysis. Therefore, the analysis of concomitant alteration proteins along with β-catenin protein expression was an essential part of our study. We evaluated the characteristics of the differently expressed genes in SPNs. Among the 551 identified proteins that matched with a gene symbol on the mRNA expression array, 150 upregulated proteins were involved in metabolism (29.2%), cellular processes (18.6%), localization (9.9%), and developmental processes (9.9%). Comparison of Protein and mRNA Expression in SPNs

Among the identified 551 proteins, 150 proteins were upregulated in the proteomics data, and 87 (58.0%) proteins were up-regulated in both the proteome and the mRNA expression analyses (Figure 2A,B). Furthermore, among the up-regulated 3010

DOI: 10.1021/acs.jproteome.5b00423 J. Proteome Res. 2015, 14, 3007−3014

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Journal of Proteome Research Table 1. List of Dysregulated Proteins in SPNs1 (A) up-regulated proteins in SPN gene symbol

protein SPN_T/N

mRNA _SPN

interact

gene symbol

protein SPN_T/N

mRNA _SPN

interact

CYB5B NOTUM DKK4 PKM2 CAPS GSN ENO2 SELENBP1 PDXK ACSS3 MFAP2 LSAMP HK1 IGFBP7 SLC25A13 DDX5 PGK1 NUMA1 CTSB TUBB

8.93 8.49 8.06 6.81 6.49 5.93 5.25 4.99 4.82 3.80 3.77 3.75 3.37 3.21 3.20 3.09 3.07 3.00 2.81 2.79

4.71 52.98 329.26 10.10 5.10 3.28 21.11 13.47 12.12 4.90 3.03 11.35 7.01 4.69 3.29 2.53 2.67 1.33 3.84 3.09

AR β (B) down-regulated

NPNT DLD RAN HSP90AA1 NONO MATR3 PSME2 HNRNPA1 SFPQ HNRNPA2B1 CTNNB1 NPM1 TPM4 NOV PDHB ALDH7A1 GPI PAICS FUS

2.47 2.21 2.17 1.90 1.89 1.84 1.84 1.81 1.78 1.74 1.68 1.67 1.64 1.63 1.61 1.61 1.59 1.55 1.51

4.47 −1.39 6.46 1.79 1.30 1.98 2.34 2.75 1.01 1.02 4.38 1.07 1.98 50.86 1.10 1.42 1.60 4.51 2.19

AR AR β AR AR β/AR

proteins in SPN

gene symbol

protein SPN_T/N

mRNA _SPN

interact

gene symbol

protein SPN_T/N

mRNA _SPN

interact

GCG TSC22D1 GSTA1 INS P4HB KRT8 KRT7 TPSAB1 KRT18 FGG HSPA5 DDT ADH1B TXNDC5 CYB5A RPN1

0.03 0.04 0.05 0.09 0.11 0.12 0.13 0.14 0.16 0.16 0.25 0.32 0.33 0.37 0.38 0.38

−85.00 6.04 −126.01 −19.92 −4.79 −8.42 −2.20 −3.69 −3.44 −1.13 −3.81 −1.48 −1.88 −2.98 −9.94 −1.10

β β AR β β/AR AR AR β/AR AR AR AR AR β β -

IDH2 HBB YBX1 HBA1 AARS CALR BLVRB RPL22 PGM1 MSN LDHB IQGAP1 COL1A1 C4A PDIA3

0.39 0.41 0.43 0.46 0.47 0.53 0.54 0.57 0.63 0.63 0.63 0.63 0.64 0.64 0.66

−3.99 −1.85 1.35 −3.00 −2.08 −1.24 −2.50 −1.74 1.43 1.14 −1.56 1.31 −1.45 −1.05 −1.05

AR AR AR β AR AR AR AR β AR β

T/N, tumor/normal; AR, androgen receptor; β, β-catenin. Protein expression values were compared with those of non-neoplastic tissues, including fold changes in proteomics and mRNA expression. 1

found only strong membrane expression of β-catenin and faint nuclear expression of FUS and NONO (Figure 3C) in the acinar and ductal cells.

expression levels (10.1-fold, 21.1-fold, and 7-fold, respectively), as shown in Table 2 of the Supporting Information). However, we also found that some proteins showed mismatched protein and mRNA expression patterns. For example, the protein levels of ASAH1 (8.68-fold), FN1 (5.9-fold), and C1QBP (4.73-fold) were increased, although the mRNA levels were not changed in SPNs (Table 2 in the Supporting Information). Among these molecules, we validated β-catenin, FUS, and NONO expression by immunohistochemistry. We included all seven pooled SPN cases in our immunohistochemical analysis and assessed an additional 20 SPNs for the validation set. Interestingly, we found consistent expressional changes in all 27 cases: We found strong nuclear β-catenin expression in all 27 SPNs. FUS and NONO showed nuclear expression in the tumor cells and normal pancreatic acinar cells (Figure 3C). We found increased expressions of FUS and NONO in 24 (89%) and 21 (78%) SPNs, respectively. In the non-neoplastic pancreatic tissue, we

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DISCUSSION In this study, we performed protein expression profiling of SPNs. We found that several proteins involved in Wnt signaling or the proteins that bind β-catenin were overexpressed. We also found that many proteins involved in metabolism were dysregulated in SPNs. On the basis of our proteomic data, we tried to identify significantly activated signaling pathways in SPNs. We previously found that Wnt/β-catenin, Hedgehog, and androgen receptor signaling were activated in SPNs on the basis of the mRNA expression analysis. When we compared the associated signaling pathways with those of the 150 upregulated proteins, we did not find significantly activated signaling pathways. This result may be due to differences in the 3011

DOI: 10.1021/acs.jproteome.5b00423 J. Proteome Res. 2015, 14, 3007−3014

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Figure 4. Schematic of the protein expression profile of SPN for the genes known to interact with the key genes of the Wnt/β-catenin, androgen receptor, Notch, and Hedgehog signaling pathways. The key genes of the four signaling pathways are marked by the blue area. The intensity of red color in the box represents the expressional value compared with those of non-neoplastic tissues. The gray color in the box denotes that protein was not identified by our proteomics analysis. Among the 150 up-regulated proteins in SPNs, we selected 45 proteins that are known to interact to the 15 key molecules of the four activated signaling pathways. Although our proteome analysis of SPN could not identify many key molecules of the four signaling pathways, we identified many up-regulated proteins that directly interact with β-catenin. We also identified that many interacting proteins for the key proteins of Wnt/β-catenin, AR, Notch, and Hedgehog signaling pathways are up-regulated in SPNs.

genes and proteins that were identified by mRNA and proteome analyses, respectively. More than 4000 of 40 000 genes examined in our DNA microarray showed expression changes. However, only 329 proteins were identified as altered in the proteomic analysis. The smaller number of proteins that were identified may have prevented identification of signaling pathways that are specifically activated in SPNs. Although we did not identify specific signaling pathways activated in SPN by proteome analysis, we found the overexpression of a large number of important genes in the Wnt/β-catenin signaling pathways. Our proteomic analysis directly demonstrated increased levels of CTNNB1 (1.68-fold) and the concomitant up-regulation of proteins belonging to the Wnt/β-catenin signaling pathway [DKK4(8.06-fold)] and proteins that bind to β-catenin [FN1 (5.9-fold), SELENBP1 (4.99-fold), DDX5 (3.09-fold), YWHAZ (2.24-fold), NONO (1.89-fold), and FUS (1.51-fold)] , shown in Figures 3A and 4. A total of 12 proteins have been identified in the β-catenincontaining complex (TOP2A, DDX1, DDX5, DDX9, FUS, PARP1, NONO, GEMIN5, PRPF6, hnRNPA2B1, hnRNPM, and RBMX).17 Among these proteins, six proteins [DDX5 (3.09-fold), NONO (1.89-fold), hnRNPA2B1 (1.74-fold), hnRNPM (1.71-fold), DDX1 (1.63-fold), and FUS(1.51fold)] were up-regulated in our SPNs. FUS and NONO have been reported to be intranuclear proteins that bind to RNA and participate in transcriptional regulation and RNA splicing.18,19

Oncogenic roles of these proteins have been reported in liposarcoma and papillary renal cell carcinomas.20,21 Interactions between β-catenin and FUS have been reported to regulate the differentiation and proliferation of intestinal epithelial cells and to play important roles in colorectal carcinogenesis.17 Interestingly, FUS and NONO have also been reported to bind to the androgen receptor, which we reported to be overexpressed in SPN; thus, they may be related to the activation of the androgen receptor pathway in SPNs. In addition to the up-regulation of FUS and NONO, their abilities to directly bind to β-catenin and the androgen receptor suggest that these proteins might be related to the activation of Wnt/βcatenin and androgen receptor signaling. Additionally, we identified that many proteins that interact with the key molecules of Wnt/β-catenin, AR, Notch, and Hedgehog signaling pathways are up-regulated in our proteomic analysis (Figure 4). Therefore, our proteomic results provided detailed information for the activation of pathways induced by β-catenin mutations in SPN. These results provide clues regarding a possible link between the activation of these four activated pathways (Wnt/β-catenin, AR, Notch, and Hedgehog signaling pathways) in SPN. Our proteomic analysis identified up-regulated proteins that are involved in metabolism. We identified 9 proteins that were up-regulated (PKM2, ENO2, HK1, SLC25A13, PGK1, DLD, 3012

DOI: 10.1021/acs.jproteome.5b00423 J. Proteome Res. 2015, 14, 3007−3014

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Journal of Proteome Research PDHB, ALDH7A1, and GPI). One of the interesting findings of our study was the overexpression of ENO2. ENO2 expression was up-regulated at the mRNA and protein levels, whereas ENO1 was down-regulated at the mRNA and protein levels. A previous study showed ENO2 expression in SPN by immunohistochemical analysis.22 Because ENO2 is normally expressed in the central nervous system, these findings suggest a neuronal origin or neuronal differentiation of SPN. In our proteomics data, we identified 47 up-regulated proteins and 69 down-regulated proteins, which showed no changes at the mRNA level (Figure 2B); a large number of proteins involved in RNA slicing and metabolic process belonged to this group. Most of them were overexpressed at the protein level without showing up-regulation at the mRNA level. The expression of mRNA does not always predict protein abundance.23 Our proteomics-based study focused on changes in protein profiles associated with the process of SPN tumorigenesis. The identification of altered protein expression without changes in mRNA expression is important because these findings provide clues regarding potential RNA processing or metabolic changes in protein expression in SPN. Among the proteins involved in RNA slicing, hnRNP proteins (hnRNPA0, hnRNPA2B1, hnRNPC, hnRNPH1, hnRNPH3, and hnRNPU) were upregulated only at the protein level, not at the mRNA level. The central role of hnRNP proteins is the post-transcriptional regulation of RNAs, such as pre-mRNA splicing and polyadenylation, as well as the transport of mRNA, inhibition of apoptosis, angiogenesis, cell invasion, and epithelial− mesenchymal transition (EMT).24,25 In addition, hnRNP proteins regulate PKM2 expression via the regulation of cMyc.26 In light of our results, we suggest a correlation between hnRNP protein levels and the up-regulation of the metabolic proteins involved in glucose metabolism in SPNs. Furthermore, our proteomic analysis results help explain the high detection rate of SPN by glucose-based metabolic imaging, even though most SPNs are benign.27 The basis for the difference in the glucose metabolism of SPNs, compared to that of other malignant pancreatic tumors, should be examined in future studies.

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Health & Welfare, Republic of Korea (grant nos. HI14C0068 and HR14C0005).

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ASSOCIATED CONTENT

S Supporting Information *

Table S1. Clinicopathological features of patients with solidpseudopapillary neoplasm of the pancreas. Table S2. List of proteins that are dysregulated in protein level and unchanged in mRNA level. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/ acs.jproteome.5b00423.

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel: +82 2 2228 1761. Fax: +82 2 363 5263. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was supported by a grant of Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of 3013

DOI: 10.1021/acs.jproteome.5b00423 J. Proteome Res. 2015, 14, 3007−3014

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DOI: 10.1021/acs.jproteome.5b00423 J. Proteome Res. 2015, 14, 3007−3014