Multifucosylated Alpha-1-acid Glycoprotein as a Novel Marker for

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Multifucosylated alpha-1-acid glycoprotein as a novel marker for hepatocellular carcinoma Kazuhiro Tanabe, Kae Kitagawa, Nozomi Kojima, and Sadayo Iijima J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.5b01145 • Publication Date (Web): 29 Jun 2016 Downloaded from http://pubs.acs.org on July 12, 2016

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Journal of Proteome Research

Multifucosylated alpha-1-acid glycoprotein as a novel marker for hepatocellular carcinoma Kazuhiro Tanabe1*, Kae Kitagawa2, Nozomi Kojima2, Sadayo Iijima3 1

Advanced Technology Center, Medical Solution Segment, LSI Medience Corporation, Tokyo,

Japan. 2

Biotechnology Laboratory, Mitsubishi Chemical Group Science and Technology Research

Center, Inc., Yokohama, Japan 3

International Sales Department, LSI Medience Corporation, Tokyo, Japan

*Correspondence: E-mail: [email protected] Tel: +81-3-5994-2228

Keywords Fucosylation, Lewis X, Hepatocellular carcinoma, Aleuria aurantia lectin, N-glycan, Glycopeptide, Mass Spectrometry, Proteomics, Glycomics

ACS Paragon Plus Environment

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Abstract

High-sensitivity and specificity diagnostic techniques to detect early-stage hepatocellular carcinoma (HCC) are in high demand. Screening with serum HCC markers, such as alphafetoprotein, is not practical as they possess poor sensitivity and specificity. As such, we focused on glycan alterations of glycoproteins found in patient sera in an attempt to discover novel HCC markers that are more specific and sensitive than current HCC markers. Sera from 42 HCC patients and 80 controls, composed of 27 chronic hepatitis B patients, 26 chronic hepatitis C patients, and 27 healthy volunteers, were analyzed in this study. Glycopeptides obtained from serum proteins by trypsin digestion were enriched by ultrafiltration and Aleuria aurantia lectinbased affinity chromatography, followed by analysis using liquid chromatography time-of-flight mass spectrometry. The data were analyzed by our newly developed software, which calculates peak intensities and positions (m/z and elution time), aligns all sample peaks, and integrates all data into a single table. HCC markers were extracted from more than 30,000 detected glycopeptide peaks by t-test, mean-fold change, and ROC analyses. As a result, we revealed that alpha-1-acid glycoprotein with multifucosylated tetraantennary N-glycans was significantly elevated in HCC patients, whereas the single fucosylated derivative was not.


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Introduction Hepatocellular carcinoma (HCC), generally caused by viral hepatitis B1 or C2,3 infections, is one of the leading causes of death worldwide4,5. As the five-year survival rate after diagnosis at an advanced stage is less than 5%6, novel diagnostic techniques allowing for detection at earlystage HCC are in high demand. Imaging techniques, such as ultrasonography, computed tomography, or magnetic resonance imaging, are used for early-stage cancer detection; however, they are not useful for distinguishing benign hepatic diseases such as dysplastic nodules and cirrhotic macronodules from HCC7. Furthermore, they are too expensive to be used in routine medical checks. Conversely, serum tumor markers have been used for detecting malignant tumors8. In particular, serum alpha-fetoprotein (AFP) is a widely used marker for the diagnosis of HCC or follow-up after surgery or chemotherapy9,10. However, AFP levels are often elevated in patients with benign liver diseases, such as chronic hepatitis or liver cirrhosis, resulting in false negatives in 30–40% of HCC patients11. Aberrant glycosylation is often observed in serum proteins of cancer patients12-14, with increased fucosylation due to activation of fucosyltransferases observed in various serum proteins. Excessive core fucosylation of alpha-fetoprotein (AFP-L3)15 is a widely used HCC marker that is able to distinguish HCC from benign liver diseases. Aberrant outer arm (sialylLewis X or A type) fucosylation of highly branched N-glycans has also been reported as a promising HCC marker16,17. Such abnormal glycans associated with cancer development were intended as alternatives to current HCC markers; however, difficulties in developing antibodies with high-affinity and high-selectivity against glycans have prevented this approach.


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Dramatic improvements have been made in mass spectrometry in the past decade with respect to sensitivity, dynamic range, and scan speed. These improvements have revolutionized the approach to biomarker research, enabling analysis of numerous compounds within a limited time period. In this study, our goal is to discover novel serum HCC markers that are able to distinguish HCC patients from not only healthy individuals but also chronic hepatitis B or C patients. Our strategy is to analyze global glycoproteins in serum samples using time-of-flight (TOF) LC-MS and comparing among HCC patients, chronic hepatitis patients, and healthy volunteers. Prior to LC-MS analysis, the serum glycoproteins were fragmented by trypsin into peptides including glycopeptides, which were then enriched by ultra-filtration and lectin affinity chromatography. Glycopeptide profiles were obtained by our newly developed software, “Marker Analysis,” which can analyze more than 100,000 peaks for every patient simultaneously. The advantage of this strategy is that it not only investigates sugar chain alterations but also assesses the combination of sugar chain alterations with their “host” proteins. Although screening of glycopeptides for specific proteins has been previously conducted18,19, screening targeting global serum glycoproteins of HCC patients has not been performed. Using this approach, we attempted to discover novel HCC markers as an alternative to current HCC markers.


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Experimental section Participants We obtained the sera of 42 HCC patients, 27 chronic hepatitis B patients, and 26 chronic hepatitis C patients from the Leading Project for Personalized Medicine of the Ministry of Education, Culture, Sports, Science and Technology (Tokyo, Japan), as well as the sera of 27 healthy volunteers from SOIKEN (Osaka, Japan), with informed consent from each patient or volunteer. Prior to conducting the study, we received approval from IRB in Mitsubishi Chemical Corporation. We confirmed that none of the chronic hepatitis patients had developed cancer at the time of blood sampling and all healthy volunteers received medical checks prior to blood sampling. Any patient whose results exceeded normal criteria in any test did not participate in the study (Table 1).

Serum preparation Cooled acetone (400 µL) containing 10% trichloroacetic acid (Wako Pure Chemical Industries, Ltd, Osaka, Japan) and fetal calf fetuin (50 µg, Sigma, St. Louis, MO, USA) as an internal standard were added to serum (100 µL), which was mixed in a -20 °C refrigerator for 90 min to remove serum albumin. After centrifuging at 12,000 rpm for 20 min at 4 °C, the supernatant was removed and cooled acetone (400 µL) was added to wash the precipitate. It was then centrifuged at 12,000 rpm and the supernatant was removed. The precipitate was mixed with a denaturing solution composed of urea (0.4 g, Wako Pure Chemical Industries), 1 M Tris-HCl buffer (500 µL, pH 8.5), 0.1 M EDTA solution (50 µL), 1 M tris(2-carboxyethyl)phosphine hydrochloride


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(25 µL, Sigma) solution, and water (190 µL). The proteins were denatured at 37 °C for 20 min, followed by the addition of 1 M 2-iodoacetamide (100 µL, Wako Pure Chemical Industries) solution, to protect the thiol residues of the proteins. This was then kept at 37 °C for 1 h under dark conditions. The solution was transferred into a previously washed Amicon Ultra 30 K 4 mL tube (Millipore Corp., MA, USA) and centrifuged at 4,500 rpm for 30 min to remove any denaturing reagent. The denatured proteins became trapped on the filter and were washed with 0.1 M Tris-HCl buffer (2 mL, pH 8.5), followed by centrifugation at 4,500 rpm for 40 min. Subsequently, 0.1 M Tris-HCl buffer (1 mL, pH 8.5), 0.1 µg/µL trypsin (100 µL, Wako Pure Chemical Industries) solution, and 0.1 µg/µL lysyl endopeptidase (100 µL, Wako Pure Chemical Industries) solution were added to the Amicon tube and the serum proteins were digested for 16 h at 37 °C on the Amicon Ultra filters. After digestion, the solution was centrifuged for 10 min at 4,500 rpm. The supernatant, which contains digested peptides, was transferred to a previously washed Amicon Ultra 10 K, 4 mL tube (Millipore Corp.), and centrifuged for 10 min at 4,500 rpm. Most glycopeptides were trapped on the 10 K ultra-filter, whereas most non-glycosylated peptides were removed by filtration. The trapped glycopeptide fraction was washed with a solution of 10% acetonitrile and 90% 10 mM ammonium acetate (2 mL), transferred to a 1.5 mL tube, and dried via vacuum centrifuging. Aleuria aurantia lectin (AAL) immobilized on agarose gel (1 mL containing 2 mg of lectin, Vector, CA, USA) was placed in an empty 1 mL column (Agilent, CA, USA) and washed with 0.1 M Tris-HCl buffer (30 mL, pH 7.4). Two-fifths of the dried glycopeptide fraction, corresponding to 40 µL of serum, was dissolved in water (200 µL) and loaded onto the lectin column. After washing with 10 mM Tris-HCl buffer (15 mL, pH 7.4), fucosylated glycopeptides were eluted with 100 mM fucose solution (15 mL, dissolved in 10 mM Tris-HCl buffer (pH


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7.4)). The eluted fraction was recovered with Amicon Ultra 3 K (15 mL) and centrifuged at 5,400 rpm for 90 min to remove excess fucose. The residue remaining on the filter was washed with water (10 mL) and centrifuged at 5,400 rpm for 75 min. Fucosylated glycopeptides trapped on the filter were recovered and analyzed by LC-MS. None of the labeling reagents were used for this analysis.

Liquid Chromatography and Mass Spectrometry LC-MS datasets were acquired on a liquid chromatography system (Agilent HP1200, Agilent Technologies, Palo Alto, CA) equipped with a C18 column (2 µm, 100 mm × 1.5 mm ID, Inertsil, GL Science, Tokyo, Japan) and coupled with an electrospray ionization quadrupole time-offlight (Q-TOF) mass spectrometer (Agilent 6520, Agilent Technologies, Palo Alto, CA, USA). Solvent A was composed of 0.1% formic acid in water, while solvent B was composed of 0.1% formic acid in 9.9% water and 90% acetonitrile. Glycopeptides were eluted at a flow rate of 0.1 mL/min at 40 °C with a linear gradient of 10–56% solvent B over 40 min, followed by a further 10 min hold at 100% solvent B. The mass spectrometer was operated in negative mode, which is advantageous to enhancing peak intensities of glycopeptides with sialic acids, with a capillary voltage of 4,000 V. The nebulizing gas pressure was 30 psi and the dry gas flow was 8 L/min at 350 °C. The order of measurement was randomized to minimize the specific error in each group. Quality control samples were prepared by pooling the sera of all patient and control samples and were analyzed every 15 samples to verify the measurement accuracy.


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Screening of HCC Markers All mass spectral data were analyzed by our original software, “Marker Analysis,” developed using R (R 3.2.2, R Foundation) and Excel VBA (Excel 2010, Microsoft). After LC-MS raw data were converted to CSV-type data using Mass Hunter Export (Agilent Technologies), Marker Analysis was used to distinguish peak curve shapes; smooth and differentiate them; and recognize the beginning, top, and end of the peaks (determined at the points where the differentiation curve changed from zero to positive, from positive to negative, and from negative to zero, respectively). The peak area was calculated by integrating curves from beginning to end (Table 2). The error in retention time and m/z was corrected using internal standard (fetal calf fetuin) peaks. Peak alignment was performed in such a way that the error of each peak position (retention time and m/z) was within 0.3 min and 0.06 Da, respectively. Background noise was eliminated by comparing samples with blanks, which were prepared from 100 µL of water instead of serum. All peak areas detected in HCC or controls were divided by those of a healthy individual (HEA253) to normalize them. The purpose of the normalization was to correct errors that occurred during the prolonged experimental period. The ratios between the patients and controls were not changed by this process, and this treatment did not affect the t-test or MFC analysis. Marker screening was performed by a combination of t-test statistics, mean-fold change (MFC) analysis, and ROC analysis using Marker Analysis and SPSS 17.0 (SPSS, Chicago, Ill, USA), comparing 42 CRC patients with 80 controls. A cut-off value for new markers was determined to maximize the sensitivity and specificity using an ROC curve. O-PLS-DA analysis was conducted using Multibase (ver. 2014) provided by NumericalDynamics.com (http://www.numericaldynamics.com).


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Identification of HCC Markers To identify a target HCC marker, which was extracted by t-test statistics, MFC analysis, and ROC analysis, the target glycopeptide was isolated by ion-exchange chromatography and reversed-phase chromatography (Agilent HP1100 with a fraction collector, Agilent Technologies, Palo Alto, CA). A Poly-LC Polysulfoethyl A column (5 µm, 200 mm × 4.6 mm ID, PolyLC Inc., Columbia, USA) was used for ion-exchange chromatography. Solvent A contained 10 mM potassium hydrogen phosphate (pH 2.7) and solvent B contained 10 mM potassium hydrogen phosphate (pH 2.7) with 0.5 M potassium chloride. Mobile phase B was held at 0% for 5 min, followed by a linear gradient of solvent B from 0–30% over 25 min, and a further 10 min hold at 100% solvent B. Peptides were detected at UV wavelengths of 215 nm and 280 nm. Isolated target glycopeptides were further purified with reversed-phase chromatography. The purified marker glycopeptides were first digested by peptide N-glycosidase F (PNGase F)20 following the manufacturer’s protocol (New England Biolabs Japan Inc., Tokyo, Japan). Peptides without glycan were then analyzed by LC-MS and LC-MS/MS to identify the molecular weight and peptide sequence. The data were analyzed by the Mascot search engine (Matrix Science, version 2.3.02) using the human Uni-Prot/Swiss-Protein sequence database (October 2015, 20,266 total sequences). The cleavage option included sequences with one missed tryptic cleavage, and the mass tolerance for matches was restricted to 1.2 Da for precursor ions and 0.6 Da for fragment ions. Sugar chain compositions were proposed based on the difference in accurate molecular weight between pre- and post-PNGase F digestion of glycopeptides and the sugar chain fragmentation pattern from MS/MS analysis.


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Results and Discussion Method validation Method validation was conducted to verify the intra- and inter-day reproducibility and errors resulting from different operators and laboratories. The exogenous protein, fetal calf fetuin, was added to the serum of a healthy person, and the glycopeptide peak (m/z 1532.58, z3) derived from the protein was evaluated as a validation indicator. The coefficients of variance (CV) of intra- and inter-day reproducibility (excluding the AAL enrichment step) were 5.9% (n = 16) and 7.7% (4 days), respectively. The error between the two laboratories was less than 2.0% (n = 40 and 52, respectively), while the error among five operators was CV 8.7% (n = 16 for each operator). The reproducibility of inter- and intra-day AAL enrichment processes was CV 6.0% (n = 4) and 8.6% (6 days), respectively. The recovery of glycopeptides using 10 kDa ultrafiltration was also evaluated using 2-aminopyridine-derivatized glycan chromatography21. When tryptic digests of the serum proteins of a healthy parson were filtrated by a 10 kDa filter, 62% of Nglycans were recovered on the filter, whereas 90% of the peptides passed through the filter (evaluated by the area of TOF mass total ion chromatograms). This means that the glycopeptides were 6.2 times concentrated by 10 kDa filtration (Fig. 1). The chromatogram of 2aminopyridine-derivatized glycans obtained by normal phase liquid chromatography showed that the peptides with small-sized glycans were removed by the filter, whereas those with large-sized glycans remained on the filter.


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Screening of candidate markers The schematic strategy for enrichment and analysis of glycopeptides is shown in Figure 2A, followed by the steps for marker screening and identification shown in Figure 2B. In Step 1, a matrix using 30,000 AAL-enriched peptide peaks for 122 samples was established as a peak list by aligning all patient and control peaks. In Step 2, the 12 best HCC marker candidates were obtained from over 30,000 peaks using t-test statistics (448 peaks whose p-values were less than 10-6) (Fig. 3A), followed by MFC analysis (119/448 peaks whose mean values were 3.5 times greater) (Fig. 3B) and ROC analysis (12/119peaks whose AUC were greater than 88%) (Figs. 3C and 4). A1711, one of the 12 HCC marker candidates, displayed the second-best profile (Fig. 4A), demonstrating 89% AUC between HCC and HBV hepatitis patients, 86% between HCC and HCV hepatitis patients, and 95% between HCC patients and healthy volunteers. When the cut-off was set as 2.3 U/mL, the sensitivity and specificity were 83% and 79%, respectively.

Structure Analysis of A1711 Glycopeptide A1711 was extracted using ion-exchange chromatography and reversed-phase chromatography. Isolated A1711 was then digested by PNGase F, which separates asparaginelinked (N-linked) glycans from peptides, transforming asparagine to aspartic acid after digestion20. The peptide without N-glycans was analyzed by the targeted MS/MS. The precursor was set to m/z 965.96 (z3), the Q1 width mode was “Medium,” the mass range m/z was 100– 3000, the collision energy was 35 V, and acquisition rate was 1.02 spectra/s. The data were compared with the protein database, MASCOT (Step 3 of Fig. 2B). As a result, the MS/MS spectrum corresponded to the peptide sequence SVQEIQATFFYFTPNKTEDTIFLR of AGP,


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which includes one N-glycan binding site (Asn72) (Fig. 5A, Fig. S-1). The identified peptide included one lysine residue adjacent to Asn72, which can be cleaved by trypsin; however, the bulky sugar chain prevents the approach of trypsin. Considering the theoretical molecular weight (2894.45) of the peptide without N-glycans and the molecular weight of A1711 (6849.71, calculated from the monoisotopic signal (z4) 1711.42), the molecular weight of the sugar chain was estimated to be 3973.27. This corresponded to the combined molecular weight of seven hexoses, six hexNAcs, four N-acetylneuraminic acids, and three fucoses . The theoretical m/z (z = 4) of the proposed glycopeptide, 1711.45, corresponded to the observed m/z 1711.42 (delta = 0.03). Since the MS/MS spectrum of A1711 showed a strong m/z 803.4 fragment (Fig. 5B), which indicated that the glycan contains sialyl-lewis X (or A) moieties, the sugar chain structure was proposed as A4G4S4F3 (A: number of antennary, G: number of galactose, S: number of sialic acid, and F: number of fucose16), a tetraantennary N-glycan with three fucoses, including at least two sialyl-lewis X/A moieties (Fig. 5C). A1711 was also compared with trypsin-digested standard AGP, purchased from SIGMA (purified from human plasma), and it was confirmed that the A1711 spectrum was coincident with that of standard AGP (Fig. S-2).

Key alterations of sugar chains in alpha-1-acid glycoprotein To understand the key alterations of the sugar chain in AGP, we analyzed all fucosylated glycopeptides generated from AGP (Fig. 6). Since the glycopeptides were enriched by AAL lectin, non-fucosylated glycopeptides were not analyzed. UniPlot (http://www.uniprot.org/) showed that human AGP has five N-glycan binding sites (Asn33, Asn56, Asn72, Asn93, Asn103), for which we detected 19 fucosylated glycopeptides attached to Asn56, Asn72, Asn93,


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and Asn103 in HCC patient sera. Since there were a few fucosylated glycans attached to Asn3322, no glycopeptides attached to Asn33 were detected in this study. O-PLS-DA analysis revealed that seven glycopeptides, A4G4S3F2-Asn72, A4G4S4F3-Asn93, A4G4S4F3-Asn72 (A1711), A4G4S4F2-Asn72, A4G4S4F2-Asn93, A4G4S3F3-Asn103, and A4G4S4F2-Asn103, contributed to PC 1, which showed clear separation between HCC patients and controls (Fig. 7). The common structure between the seven glycopeptides was multifucosylated (two or three fucose binding) tetraantennary glycans (MF-AGP). In addition, the AUCs between the 42 HCC patients and 80 controls exceeded 75%. Twelve of the 19 AGP-related glycopeptides, whose glycan structures were single fucosylated or bi-, triantennary glycans, showed less than 70% AUC, indicating that these glycan structures did not change significantly upon HCC development. The glycan-binding site (Asn56, Asn72, Asn93, or Asn103) and number of sialic acids in AGP did not affect the sensitivity and specificity. The ratio between the sum of seven MF-AGPs and the other 12 glycopeptides (single fucosylated or bi-, triantennary glycans) showed much higher sensitivity and specificity when HCC patients were compared with controls. The AUCs of the ratio reached 93%, 86%, and 98%, which were compared between HCC patients and HBV, HCV hepatitis patients, and healthy controls, respectively (Fig. 8). Sensitivity and specificity were 93% and 86%, respectively, when the cut-off was set to 2.0 U/mL. The amounts of glycopeptides were affected by not only sugar chain alterations but also protein concentrations; however, the ratio between two glycopeptides (or two glycopeptide groups) derived from the same host proteins negates the changes in protein concentration and reflects only the sugar chain alteration. Considering that AGP is one of the major acute-phase proteins and its serum concentration increases two- to five-fold during an


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acute-phase response, the ratio between two glycopeptides derived from AGP was quite effective in quantifying the changes in sugar chain structures.

Excessive fucosylation of serum glycoproteins is widely observed in patients with various cancers such as pancreatic23, colon24, and lung cancer25. We previously reported that outer arm (Lewis X or A type) fucosylation was notably elevated in glycans from HCC patient sera16 and was quite significant compared to not only chronic hepatitis patients but also liver cirrhosis patients, which was also reported by Lin et al.17. These findings highlighted N-linked glycan fucosylation as a promising HCC marker. However, serum glycans separated from their host proteins lost protein-specific information; therefore, the marker performance was not sufficient for clinical practice. Alpha-1-acid glycoprotein, a 41 kDa glycoprotein whose peptide moiety is a single chain of 183 amino acids with two disulfides, contains five highly sialylated complex-type N-linked glycans26. It is synthesized primarily in hepatocytes with a typical plasma concentration of between 0.6 and 1.2 mg/mL (1–3% plasma protein)27. AGP is a major acute-phase protein and its serum concentration increases in response to systemic tissue injury28, inflammation, or infection29. While the biological function of AGP remains unknown, a number of possible activities, such as the ability to bind and carry basic and neutral endogenous hormones, have been reported30. Glycosylation of AGP has been investigated with respect to liver cancer development. Hashimoto et al. developed a “crossed affinoimmunoelectrophoresis” technique with concanavalin A and AAL, revealing that highly fucosylated AGP was significantly elevated in HCC patient sera compared to the healthy control group31. Kuno et al. showed that a multiple


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lectin-antibody sandwich immunoassay targeting AGP enabled monitoring of disease progression in chronic hepatitis patients at risk of developing hepatocellular carcinoma32. Although this lectin-based approach is easily performed, detailed molecular-level analysis was limited due to the low selectivity for sugar chain alterations. In this research, we introduced a novel glycopeptide profiling technique for HCC maker screening and confirmed that AGP containing multifucosylated tetraantennary N-glycans is a novel HCC marker, whereas those containing single-fucosylated glycans or bi-, triantennary N-glycans were not. However, some challenges remain that require further investigation. First, establishing a robust and high-throughput assay system for A1711 is essential to verify and generalize this marker, as glycopeptide profiling based on LC-MS is not sufficient. Second, more patients and controls should be evaluated to verify A1711 efficiency. To conduct a large-scale study, the development of a robust and high-throughput assay system for A1711 is inevitable. We need to not only increase the number of participants but also recruit participants from various facilities. Third, comparing A1711 with current HCC markers, AFP or PIVKA-II, and assessing its properties as a clinical diagnostic marker is required. If the relationship between A1711 and current HCC markers is complementary, the combination assay will increase the accuracy of HCC diagnosis. Fourth, identifying the other 11 HCC marker candidates found in this study is also important. However, unlike common proteomics, identification of glycoprotein from only one target glycopeptide is quite challenging because the complete purification of glycopeptide is inevitable to identify the molecular weight of sugar chain and to identify the protein by MS/MS analysis. Furthermore, since there were fewer candidates, we often lost the target glycopeptides during several purification steps by adsorption or degradation. To find more novel HCC markers, a more efficient identification method is required.


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Conclusion In this study, we aimed to discover novel serum markers able to distinguish HCC patients from not only healthy individuals but also chronic hepatitis patients. We introduced a novel, comprehensive glycopeptide profiling technique using liquid chromatography time-of-flight mass spectrometry (LC-TOF-MS), which is advantageous not only for detecting enormous numbers of glycopeptides but also for elucidating sugar chain alterations with their “host” proteins. We detected and analyzed more than 30,000 glycopeptide peaks from 100 µL of serum within a single run, and as a result, we determined that AGP with multifucosylated tetraantennary glycans is a potential HCC marker.

Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteo-me. Figure S-1. A1711 identification using MS/MS and Mascot search (PDF). Figure S-2. Spectrum comparison between A1711 and glycopeptides of purified AGP (PDF). Table S-1. The raw data of HCC candidate markers analyzed by LC-TOF-MS (XLSX).


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Figure legend Figure 1. Enrichment of glycopeptides by 10 KDa ultrafiltration A) Recovery of glycopeptides by 10 KDa ultrafiltration N-glycans released from tryptic digests of a healthy person’s serum were labeled with 2aminopyridine and analyzed by a liquid chromatography-equipped fluorescence detector. Upper: N-glycans of tryptic digests without filtration, Lower: N-glycans of 10 KDa trapped tryptic digests. B) Concentrating efficiency of 10 KDa filtration Tryptic digests of a healthy person were analyzed by LC-MS (TOF). Upper: Total ion chromatogram of the tryptic digests without filtration. Lower: Total ion chromatogram of tryptic digests trapped on 10 KDa filter. Figure 2. Marker screening strategy A) Scheme of HCC marker screening After the serum proteins were digested by trypsin, glycopeptides enriched by 10 KDa ultrafiltration and AAL lectin were analyzed by LC-MS (TOF). B) Identification of HCC markers The data obtained from LC-MS were analyzed by our originally developed software “Marker Analysis.” All peak information was integrated into one table, and HCC marker candidates were screened by t-test statistics, MFC analysis, and ROC analysis. The candidate molecular structures (protein and sugar chain) were analyzed by LC-MS/MS, and the relation between HCC and sugar chain structures, attaching site specificity, fucosylation, sialylation, or branching, were analyzed Figure 3. HCC marker screening A) Screening by t-test statistics Glycopeptide peaks whose p-values of the t-test were less than 10-6 were extracted. B) Screening by mean-fold change (MFC) analysis Glycopeptide peaks whose ratios were over 3.5 were screened from the extracted glycopeptides by conducting the t-test. C) Screening by ROC analysis HCC marker candidates whose AUCs were above 0.88 were obtained from the screened peaks by MFC analysis. Figure 4. The box-and-whisker plot and ROC analysis of 12 HCC marker candidates Box-and-whisker plots with HCC (n = 42), HBV (n = 27), HCV (n = 26), and healthy volunteers (n = 27). ROC curves compared HCC (n = 42) with controls (n = 80). “A” with number showed the ID of HCC marker candidates.


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Figure 5. Structure analysis of A1711 A) MS/MS spectrum of the A1711 peptide after PNGaseF treatment. After A1711 was digested by PNGaseF, the peptide was analyzed by LC-MS/MS. B) MS/MS spectrum of A1711 glycopeptide. A1711 (with N-glycan) was analyzed by LC-MS/MS. C) Proposed structure of A1711 Figure 6. AUCs of ROC analysis for glycopeptides of alpha-1-acid glycopeptide ROC analysis was performed between HCC (n = 42) and controls (n = 80) for fucosylated AGPrelated glycopeptides. Black bars: multifucose with tetraantenary glycans. Gray bars: single fucosylated glycopeptides or bi-, triantennary glycopeptides. Nomenclature of sugar chain, A: number of antennary, G: number of galactoses, S: number of sialic acids, F: number of fucoses. Asn: sugar chain attaching sites in alpha-1-glycoproteins. Figure 7. O-PLS-DA for AGP-related glycopeptides O-PLS-DA (discriminant analysis) was performed for 19 AGP-related glycopeptides. Left: Loading plot, Right: Score plot. HCC, HCV, HBV patients, and healthy individuals are shown as red, yellow, green, and blue dots and ellipses, respectively. Figure 8. The box-and-whisker plot and ROC analysis of “multifucose index.” A) The box-and-whisker plots of multifucose indices (ratio between MF-AGP and the others) for HCC, HBV, HCV, and healthy volunteers. B) ROC curve comparing HCC with HBV group C) ROC curve comparing HCC and HCV group D) ROC curve comparing HCC and healthy volunteers.


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Table 1. Profile of participants


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Table 2. Data processing by Marker Analysis


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Figure 1


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Figure 2


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Figure 3


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Figure 4


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Figure 5


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Figure 6


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Figure 7


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Figure 8


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For TOC only


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Multifucosylated Alpha-1-acid Glycoprotein as a Novel Marker for

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