Article pubs.acs.org/jpr
N‑Glycoprotein Analysis Discovers New Up-Regulated Glycoproteins in Colorectal Cancer Tissue Annalisa Nicastri,*,† Marco Gaspari,† Rosario Sacco,‡ Laura Elia,‡ Caterina Gabriele,† Roberto Romano,‡ Antonia Rizzuto,‡ and Giovanni Cuda† †
Proteomics@UMG, Department of Experimental and Clinical Medicine and ‡Surgery Unit, Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, viale Europa, 88100 Catanzaro, Italy S Supporting Information *
ABSTRACT: Colorectal cancer is one of the leading causes of death due to cancer worldwide. Therefore, the identification of high-specificity and -sensitivity biomarkers for the early detection of colorectal cancer is urgently needed. Posttranslational modifications, such as glycosylation, are known to play an important role in cancer progression. In the present work, we used a quantitative proteomic technique based on 18O stable isotope labeling to identify differentially expressed N-linked glycoproteins in colorectal cancer tissue samples compared with healthy colorectal tissue from 19 patients undergoing colorectal cancer surgery. We identified 54 up-regulated glycoproteins in colorectal cancer samples, therefore potentially involved in the biological processes of tumorigenesis. In particular, nine of these (PLOD2, DPEP1, SE1L1, CD82, PAR1, PLOD3, S12A2, LAMP3, OLFM4) were found to be up-regulated in the great majority of the cohort, and, interestingly, the association with colorectal cancer of four (PLOD2, S12A2, PLOD3, CD82) has not been hitherto described. KEYWORDS: colorectal cancer, biomarker discovery, N-linked glycoproteins, 18O stable isotope labeling
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INTRODUCTION Colorectal cancer (CRC) is one of the most common neoplastic diseases in industrialized countries and worldwide the fourth most common cause of death from cancer.1 It derives that the early detection of CRC is crucial to reduce the mortality. For this reason, scientific interest has been turned to the identification of new biomarkers able to effectively discriminate the presence of CRC-related disease.2,3 The need to identify new biomarkers stems from the assumption that the greatest benefits for cancer patients are obtained during the monitoring and management of early stage disease rather than the treatment of more advanced stages. This can be achieved through the identification and use of biomarkers with high sensitivity and specificity, with the ultimate goal of achieving the early diagnosis and thus counteract the development of cancer.4 In addition, being able to identify a new biomarker specific for a disease means to have a wider knowledge of the biological mechanisms related to the development and progression of the disease itself. This could be useful not only to promote early diagnosis, but also to determine the prognosis and finally predict the patient response to specific therapies.5,6 Moreover, a biomarker with elevated sensitivity and specificity might be useful for verifying early suspect of CRC in those patients who cannot undergo colonoscopy (absolutely contraindicated or refused by the patient), which is presently one of the most reliable diagnostic tools for CRC available in clinical practice, or when colonoscopy is relatively contraindicated and thus the additional information on a diagnostic test could help in decision making. © 2014 American Chemical Society
Several studies aimed at the identification of new biomarkers associated with CRC,7 including studies about either genes or proteins involved in the development and progression of cancer. However, gene expression studies are not able to give reliable information about the amount of protein involved. In fact, many proteins undergo post-translational modifications that influence the activation, interactions, and function within a cell. In this regard, the study of proteomics can be a more efficient alternative for the study of global proteins and their post-translational modifications. One of the most important post-translational protein modifications is glycosylation, which may be attributable to physiological and pathological cellular events.8 Moreover, the majority of cellular proteins expressed on the cell surface or in the extracellular space are known to be glycosylated and play many biological processes such as signal transduction, cell adhesion, and cell migration.9 Furthermore, many studies associated abnormal expression of N-linked glycoproteins with various diseases, such as cancer.10 In this particular case, the presence of aberrant glycolipids and glycoproteins has been reported,8,11 and previous studies have shown that glycoprotein changes can be used as biomarkers for cancer diagnosis.12,13 In fact, several Special Issue: Proteomics of Human Diseases: Pathogenesis, Diagnosis, Prognosis, and Treatment Received: June 26, 2014 Published: September 23, 2014 4932
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Protein Digestion and Enrichment for N-Linked Glycopeptides by Solid-Phase Extraction of Glycopeptides (SPEG) Protocol24
biomarkers widely employed in oncology such as carbohydrate antigen (CA)-19-9,14 prostate-specific antigen (PSA),15 and CA12516 are N-linked glycosylated proteins. In the past few years, various studies have been addressed to the identification of glycoproteins associated with a disease by means of mass spectrometry-based glycoproteomics. For this purpose, glycoproteins can be enriched using several techniques that include, after the enrichment phase, three different strategies. The first strategy concerns the glycan analysis, in which the glycan moieties are analyzed after their removal from the corresponding peptides.17,18 The second strategy consists of the isolation of formerly N-linked glycopeptides by either lectin affinity19,20 or by coupling the glycopeptides to a hydrazide support21,22 to achieve the quantitative analysis of N-linked glycoproteins. The third strategy is characterized by glycopeptide enrichment using zwitterionic hydrophilic interaction chromatography, followed by direct mass spectrometric analysis of the glycopeptides using a dedicated fragmentation workflow combining higher-energy C-trap dissociation (HCD) with electrontransfer dissociation (ETD) able to characterize both the primary sequence of the peptides and their glycan modifications.23 In the present study, we used a proteomic approach to identify and quantify glycoproteins up-regulated in tumor compared with healthy colon tissue. In a cohort of 19 patients undergoing CRC surgery, healthy and diseased tissue samples were both obtained from each participant and analyzed in pairs (healthy vs diseased). As a result, we identified 54 up-regulated glycoproteins, many of which are known to be extracellular or membrane proteins.
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Four mg of proteins extracted from each tissue sample was digested as follows: First, proteins were reduced by 10 mM dithiothreitol (DTT) for 1 h at 37 °C; then, cysteine alkylation was carried out in the dark with 24 mM of iodoacetamide (IAA) for 1 h at 37 °C. Excess IAA was quenched by adding an extra 2 mM DTT and incubating for 20 min at 37 °C. Before the addition of the trypsin enzyme, 0.2% RapiGest (Waters) was brought to a concentration of 0.1% by dilution with water. Then, 40 μg of trypsin (Sigma-Aldrich, Saint Louis, MO) was added to the samples, and the reaction was allowed to proceed at 37 °C for 16 h with gentle shaking. Peptides were desalted and concentrated using 3 cm3 Sep-Pak C18 cartridges (Waters) and concentrated by speedVac and then resuspended in 100 μL of 8 mM sodium meta-periodate, 20 mM sodium acetate, 100 mM sodium chloride, pH 5.0, and oxidized for 1 h at 6 °C in the dark with gentle shaking. A second desalination of peptides, followed by concentration, was done, and afterward the peptides were coupled overnight to Affi-Prep Hz hydrazide resin beads (Bio-Rad, Hercules, CA) with gentle head-over rotation at room temperature. Next, beads were washed 10 times with 5 M sodium chloride, 5 times with water, 10 times with 80% acetonitrile (ACN)/20% water, 10 times with methanol, 10 times with water, and 10 times with 100 mM disodium−hydrogen phosphate buffer, 25 mM EDTA, pH 7.1, to remove nonglycosylated peptides. One μL of the PNGaseF enzyme (500 U/μL; New England Biolabs, Ipswich, MA) was added to release N-linked glycopeptides from the beads during overnight incubation at 37 °C and gentle head-over rotation. Finally, 1 cc Sep-Pak C18 cartridges were used to desalt the de-N-glycosylated peptides previously released from beads. After evaporation, peptides were resuspended in 40 μL of 2% ACN, 0.1% formic acid (FA) before MS analysis. One μL of each sample was injected in nanoLC−MS/MS to test the efficiency of the Solid-Phase Extraction of Glycopeptides (SPEG) protocol.
MATERIALS AND METHODS
Specimen Collection and Processing
Nineteen patients affected by CRC and candidate for surgical resection of the colonic neoplastic mass in the Surgery Unit, University Hospital, Magna Graecia University of Catanzaro, were enrolled. The present study was approved by the Local Ethical Committee, and all patients were exhaustively informed about the purposes of the study and gave written informed consent. Inclusion criteria were: age ≥18 years, adenocarcinoma as histotype, colorectal primitive location, and absent/ unremarkable inflammation detected during anatomopathology examination. During surgery, two tissue specimens mainly consisting of mucosa and each one no less than 100 mg in weight were, respectively, obtained from the excised neoplastic mass and from a nearby healthy-looking colonic area. The latter was obtained from healthy-looking tissue located at the edge of the surgically excised specimen, where no disease was found by further anatomopathology analysis. Both samples were immediately stored at −80 °C until use. Before tryptic digestion, 100 mg of each tissue specimen was washed four times with prechilled (4 °C) PBS. Each tissue sample was cut into smaller pieces while keeping it on ice and added with 1000 μL of lysis buffer (50 mM Tris pH 7.4, 300 mM NaCl, 1 mM Na3VO4, 1 mM NaF, 0.2% RapiGest (Waters, Milford, MA)), homogenized, and kept on ice for 30 min. Afterward, each sample was sonicated for 1 min (power of about 180 W in rounds of 10 s sonication/10 s rest for each cycle) and kept on ice during the sonication. The obtained lysates were centrifuged at 14 000g for 30 min at 4 °C. Finally, the supernatant was collected and its protein amount was measured by the bicinconinic acid (BCA) assay (Pierce BCA, ThermoFisher Scientific, Rockford, IL)
Enzymatic Labeling with 18O/16O
The 18O/16O labeling protocol by trypsin embedded on magnetic beads, Mag-Trypsin (Clontech Laboratory, Mountain View, CA), was applied on all 38 samples (19 controls, 19 tumor tissue extracts). Ten μL of each sample from the 19 neoplastic tissues was evaporated and resuspended in 50 μL of solution containing H218O (>98% purity, Spectra2000, Roma, Italy), 20 mM of ammonium acetate and 20% of ACN, and 10 μL of each 19 sample from control tissues were evaporated and resuspended in 50 μL of solution containing H216O 20 mM of ammonium acetate and 20% of ACN. To each sample, 100 μL of Mag-trypsin beads previously washed with the corresponding digestion buffer was added, and overnight labeling was allowed to proceed in a thermomixer (1100 rpm, 37 °C). Samples were then separated from the magnetic beads. The supernatant was collected and placed in a separate Eppendorf tube. To inactivate any residual amount of trypsin, we incubated supernatants incubated for 1 h at 56 °C first and then for 10 min at 100 °C. The labeling procedure resulted in the addition of two 18O atoms at the C-terminus of the peptides labeled with 18O-containing water (tumor tissues), inducing a mass shift of 4 Da (“heavy” peptides). On the contrary, the labeling with 16O-containing water did not produce any variation in the molecular weight of control peptides (“light” peptides); anyway, “light” samples were processed in parallel with the “heavy” samples to avoid any possible source of 4933
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bias (peptide loss on magnetic beads, etc.). Finally, “heavy”- and “light”-labeled samples from each patient’s tumor-control tissue pair were mixed and analyzed by nanoLC−MS/MS for quantitative analysis.
Statistical and Bioinformatic Analysis
Statistical analysis was performed with PASW Statistics 18. Categorical variables are presented as percentage of patients; continuous data are expressed as means ± standard deviation or as median (interquartile range, IQR), as appropriate based on distribution properties. STRAP (Software Tool for Researching Annotations of Proteins, www.bumc.bu.edu/ cardiovascularproteomics/cpctools/straP/) was used to classify proteins according to their molecular function and biological processes in which they are involved.
NanoLC−MS/MS
Each sample was analyzed by nanoLC−MS/MS analysis, performed by a EASY-nLC 1000 (Thermo Fisher Scientific, Denmark) coupled to a Q-exactive mass spectrometer (Thermo Fisher Scientific, Germany). A 4 μL amount of peptide mix was loaded onto an analytical column (length 10 cm, inner diameter 75 μm) lab packed with 3 μm C18 silica particles (Dr. Maisch, Entringen, Germany). Mobile phase A was 2% ACN, 0.1% formic acid; mobile phase B was 80% ACN, 0.1% formic acid. The used gradient was from 2% mobile phase B to 8% mobile phase B in 1 s, 5−35%B in 100 min, 35−100%B in 10 min, and 100%B in 8 min at a flow rate of 300 nL/min. The column effluent was then nanoelectrosprayed into a Q-exactive hybrid mass spectormeter operating in positive ion mode, with nESI potential at 1600 V and ion transfer tube temperature of 280 °C. A full MS scan was acquired in the Orbitrap analyzer at resolution 70 000, m/z range of 350−1800, and target AGC value of 1.00 × 106. Data-dependent MS/MS acquisition (DDA) procedure was performed by selecting the 12 most abundant peaks with more than two charges after each full scan analysis (top 12 method). Precursor ions were fragmented by HCD (high-energy collisional dissociation); HCD normalized collision energy was 25%. MS/MS analysis was carried in the Orbitrap analyzer at resolution 17 500 and target AGC value of 1.00 × 105, an ion selection threshold of 1.7 × 104; and a maximum injection time of 50 ms for the full MS scan and 60 ms for tandem MS/MS scan was applied. The dynamic exclusion time was set at 30 s for all experiments.
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RESULTS
Workflow
In the present study, a quantitative strategy has been developed to analyze the differential expression of glycoproteins between tumor and adjacent normal tissue in each one of the in-study patients. Nineteen (8 women) patients affected by colorectal adenocarcinoma were included in the study. Their main characteristics are resumed in Table 1. The applied workflow Table 1. Main Clinical Characteristics of the in-Study Cohort. Comprehensive of Tumor Location and Size
Spectral Analysis and Peptide Quantification
Proteome Discoverer software version 1.3 (Thermo Fisher Scientific, Germany) was employed for the identification of proteins by means of the analysis of mass spectra obtained from digested fragments. The software compares rough mass spectrometry data with those included in the selected FASTA database and was used to calculate the mass of the monoisotopic peak mass and the correct charge state of the precursor ion starting from the rough MS data. Raw files were searched by the database search algorithm SEQUEST against the HUMAN protein sequences database (downloaded from http://www.ebi. ac.uk/uniprot). Database search parameters were set as follows: MS tolerance: 10 ppm; MS/MS tolerance: 0.02 Da; fixed modification: carbamidomethyl (C); variable modifications: oxidized methionine, deamidation of asparagine, single and double 18O substitution at the C-terminus; missed cleavage, max number: 1. Medium confidence peptides (confidence >95%) were filtered out by using Percolator,25 integrated in Proteome Discoverer. Even though medium confidence peptides were considered, to minimize missing values, it was imposed that each peptide had to be identified with high confidence (FDR < 1%) in at least one sample in order not to be discarded. Protein abundances ratios were calculated by Proteome Discoverer and were adjusted by normalization based on the median value relative to the complete subset of heavy/light ratios calculated for the identified deamidated, N-glycosylation consensus-bearing peptides to take into account any mistake made in the sample preparation steps. Proteins identified and quantified in at least 11 of the 19 in-study patients were considered for further analysis.
case cod.
sex
age
location
1 2 3 4 5 6
F M M M M F
75 62 35 56 71 72
7 8 9 10 11 12 13 14 15 16 17 18
M M F F M M F F F M F M
83 63 50 70 75 77 44 58 40 63 65 61
19
M
44
rectum cecum sigmoid colon transverse colon rectum recto-sigmoid junction hepatic flexure hepatic flexure sigmoid colon sigmoid colon descending colon ascending colon hepatic flexure ascending colon descending colon ascending colon sigmoid colon recto-sigmoid junction ascending colon
size (mm)
stage
T
N
M
G
40 50 30 60 40 50
I IIa IIa IIa IIa IIa
2 3 3 3 3 3
0 0 0 0 0 0
0 0 0 0 0 0
2 2 2 2 2 3
80 90 50 80 70 35 50 40 30 50 50 50
IIa IIb IIa IIIb IIIb IIIc IIIb IIIb IIIb IIIc IIIc IIIc
3 4 3 3 3 3 3 3 3 4 3 4
0 0 0 1 1 2 2 1 2 2 2 1
0 0 0 0 0 0 0 0 0 0 0 0
2 2 2 2 2 3 2 2 3 3 2 2
50
IIIc
4
1
0
2
consisted of four steps: (1) protein extraction from tumor and adjacent normal tissue, (2) tryptic digestion and enrichment by SPEG to select only glycopeptides, (3) 18O stable isotope labeling, and (4) nanoLC−MS/MS analysis. More specifically, tissue proteins were first extracted from paired tumor and adjacent normal tissues excised from each patient, and the amounts of proteins were quantified by BCA protein assay. Because aberrant glycosylation is a posttranslational modification appearing in pathological cellular events, SPEG was performed to specifically analyze only the tissue glycoproteins. More specifically, the identification of N-glycoproteins is based on the contained consensus N-linked glycosylation motif (NxS/T and less often, within the NxC motif, where X is any amino acid except P). PNGase F enzyme converts Asn-to-Asp (or N-to-D) in the above-mentioned motifs, generating deamidated N-linked glycopeptides. The conversion 4934
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of glycosylated asparagine to aspartic acid generates a predictable fixed mass shift (0.984 Da), which is shown in the MS and MS/MS spectra. Finally, from the quantitative proteomic data, the 18O-labeling technique was employed to identify the different expression levels of glycoproteins in each pair of tumor/normal tissue sample. Specifically, peptides from tumor samples were labeled with H218O (generating “heavy” peptides) and peptides from normal samples were labeledwith H216O (generating “light” peptides). Differently Expressed Proteins between Normal and Tumor Tissues
To overcome tissue heterogeneity, glycoproteins were analyzed and quantified for each separate patient, without performing analysis on sample pools. A total of 1459 glycopeptides and 770 glycoproteins were identified (Table S1 and Figures S1 and S2 of Supporting Information) and quantified (Table S2 of Supporting Information). Either incomplete incorporation of two 18O atoms or the phenomenon of back exchange has been reported as potential pitfalls of the 18O-labeling strategy.26 Table S3 (Supporting Information) provides peak area values for ion signals corresponding to each 16O-, 18O1-, and 18O2-labeled peptide so that eventual incomplete labeling can be appreciated. In fact, full labeling of the vast majority of peptides was observed. (A few examples of complete and incomplete labeling are shown in Figure S3, Supporting Information.) This statement is supported by the fact that under these experimental conditions a good correlation (R2 = 0.949) was found between the heavy/ light ratios calculated by Proteome Discoverer as simply the ratio between the 18O2 peak areas over the 16O peak areas, and the heavy/light ratios were calculated according to Johnson et al.,27 who described a data analysis method able to accurately extract the contributions of the “heavy” and “light” peptides from the ion signals corresponding to the 16O-, 18O1-, and 18O2-labeled peptides.27 Two criteria were applied to consider a glycoprotein as up-regulated (protein changes by a two-fold factor or more were considered biologically significant): (a) the protein had to be detected in at least 11 of the 19 available samples and (b) the protein had to be up-regulated in at least 50% of patients. Some glycoproteins that were not quantified in each patient by Proteome Discoverer software were verified manually by looking at raw full MS data (maximum precursor ion tolerance: 10 ppm; maximum retention time tolerance: 2 min). The ratio of proteins identified only in CRC but not in normal cancer was arbitrary assigned to 100. Finally, heavy/light ratio data were corrected by protein median. Quantitative analysis indicated 54 up-regulated proteins (heavy/light ratio >2) in tumor tissues as illustrated in Figure 1, in which the “heat map” represents the level of up/down regulation of the identified protein in each tumor sample. A list including more detailed information about the proteins differently expressed in the in-study CRC samples is displayed in Tables 2 (heavy/light ratio value) and 3 (median value and IQR as well as number of patients in which each protein was quantified and the percentage of up-regulated). Finally, STRAP Software Tool was used to carry out gene ontology analysis and classify the upregulated proteins according to the biological process, cellular component, and molecular component in which they are usually involved, as shown in Figure 2.
Figure 1. “Heat map” representing the level of up-regulation of the identified protein in each tumor sample (from 1 to 19) according to the heavy/light ratio (from ≥40- to 80% of cases (Table 3). These include procollagen-lysine,2-oxoglutarate 5-dioxygenase 2 (PLOD2), dipeptidase 1 (DPEP1), protein sel-1 homologue 1 (SEL1L), CD82 antigen (CD82), proteinase-activated receptor 1 (PAR1), procollagen-lysine,2-oxoglutarate 5-dioxygenase 3 (PLOD3), solute carrier family 12 member 2 (S12A2), lysosome-associated membrane glycoprotein 3 (LAMP3), and olfactomedin-4 (OLFM4). Interestingly, four of these nine glycoproteins, namely, S12A2, PLOD2, PLOD3, and CD82, have not yet been analyzed in previous proteomic studies conducted on CRC patients, so this is the first proteomic study revealing their high tissue levels in CRC samples. 4939
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target glycoproteins in sera of CRC patients. Additional studies on larger cohorts are required to fully define the utility of the candidate markers and their correlation with disease stage. In particular, immunohistochemistry on a large tissue bank (e.g., tissue microarrays) may help in associating candidate biomarker expression to tumor staging/progression. In particular, our results related to the identification of aberrant glycoproteins that have never been so far associated with CRC might be the starting point for studies aimed to the confirmation of our results on larger CRC cohorts (e.g., tissues or sera biobank studies). Ultimately, mass-spectrometry-based protein assays such as SRM or ELISA-based assays may validate in biological fluids our results obtained by tissue proteomics, hopefully resulting in a panel of protein markers of high overall sensitivity and specificity.
Even more interesting are the results about PLOD2 and PLOD3, which are part of a family of membrane-bound homodimeric proteins localized to the cisternae of the endoplasmic reticulum. PLODs are involved in fibrotic processes and tissue remodeling.36 Furthermore, a recent work has shown that hypoxia stimulates the expression of PLODs by via the HIF-1 pathway,37,38 and high levels of PLODs in tissue can depend on the fact that hypoxia-inducible factor 1 (HIF-1) activates transcription of the PLOD3 and PLOD2 genes in CRC tissues. This theory may be supported by the fact that HIF-1 is overexpressed in CRC, thus presuming an increase in the expression of PLODs proteins. Remarkably, our study is the first showing the up-regulation of PLOD2 and PLOD3 in CRC tissue. With regard to CD82, we obtained apparently, conflicting results. There is large body of evidence suggesting CD82 as a suppressor in invasion and metastasis of various tumors, and clinically advanced cancers have been associated with the downregulated expression of CD82.39,40 Nevertheless, we quantified CD82 in 13 patients of our cohort and found it to be up-regulated in >90% of them. A possible explanation could be that the present study is focused on revealing changes in glycoprotein levels; thus, even if the total levels of CD82 might not increase, its corresponding glycoform could possibly be up-regulated in CRC. Additional validations are needed to confirm these findings. Our study design has a major strength. We deliberately focused our study on glycoproteins not only because they have been linked to cancer development/progression but especially because, as a biomarker-research study, we are looking for proteins potentially identifiable in blood, and most cellular proteins expressed on the cell surface or shed in the extra-cellular space are glycosylated.41 In the last years, many studies used techniques of glycoprotein enrichment coupled to mass-spectrometry-based protein assays such as SRM (select reaction monitoring) to obtain a panel of serum/plasma protein markers that provided higher sensitivity and specificity for cancer diagnosis than conventional markers already in use in the clinical practice.42,43 Against this background, a candidate cancer biomarker must be identified as aberrantly expressed in tumor tissue prior to be investigated in blood, as we have done in our pilot study. The 22 and 19% of the 54 glycoproteins quantified in our study are, respectively, plasma membrane and extracellular proteins, which, in turn, can be shed into the bloodstream, thus highlighting their potential role as novel CRC protein biomarkers. Another strength of our study is the histological homogeneity of our cohort of patients, all being affected by colorectal adenocarcinoma. This avoided any bias deriving from the inclusion of patients affected by different histotypes of CRC or precancerous lesions. A potential limit of our study is the lack of data validation. Although data validation might be useful when translating findings into a clinical scenario, we have presently opted to focus more on the identification of new, suitable CRC-related proteins, as for a first step of biomarker discovery. Our findings might therefore be the starting point for the assessment of the potential role of specific CRC-related glycoproteins as candidate biomarkers.
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ASSOCIATED CONTENT
S Supporting Information *
Table S1: List of glycopeptides detected in 19 CRC patients. The glycosylation site is depicted as red “N” within the NxT/S motif and, less often, within the NxC motif. Table S2: Quantitative data of glycopeptides of 19 CRC tissue patients. Table S3: Peak areas of signals from each 16O-, 18O1-, and 18O2-labeled peptide. These values have been used to calculate more precise heavy/light ratios which take into account incomplete labeling (and thus, 18O1) according to the formulas described by Johnson et al.27 Figures S1: MS/MS spectra of 1759 glycopeptides. Figures S2: MS/MS spectra of glycopeptides of 54 up-regulated glycoproteins. Figure S3: Four examples of heavy/light pairs of O18/O16, illustrating both fully and partially labeled peptides. This material is available free of charge via the Internet at http://pubs.acs.org.
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AUTHOR INFORMATION
Corresponding Author
*Phone: +39 0961 3694224. Fax: +39 0961 3694090. E-mail:
[email protected]. Author Contributions
The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes
The authors declare no competing financial interest.
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REFERENCES
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CONCLUSIONS We quantified 54 glycoproteins in tissue samples of CRC, and four of those have been found to be up-regulated in CRC tissue for the first time. This is a first, essential step in the process of oncology-applied biomarker discovery, consisting of the detection in tumor tissue of aberrant glycoproteins with high cancer-specificity. A further step should be the detection of these 4940
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dx.doi.org/10.1021/pr500647y | J. Proteome Res. 2014, 13, 4932−4941
