Novel Proteomic Biomarker Panel for Prediction of Aggressive

Article pubs.acs.org/jpr

Novel Proteomic Biomarker Panel for Prediction of Aggressive Metastatic Hepatocellular Carcinoma Relapse in Surgically Resectable Patients Gek San Tan,†,‡ Kiat Hon Lim,‡ Hwee Tong Tan,† May Lee Khoo,‡ Sze Huey Tan,§ Han Chong Toh,∥ and Maxey Ching Ming Chung*,†,⊥ †

Department of Biochemistry, National University of Singapore, 8 Medical Drive, Singapore 117597 Department of Pathology, Singapore General Hospital, Outram Road, Singapore 169608 § National Cancer Centre Singapore, Clinical Trials and Epidemiological Sciences, 11 Hospital Drive, Singapore 169610 ∥ National Cancer Centre, 11 Hospital Drive, Singapore 169610 ⊥ Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543 ‡

S Supporting Information *

ABSTRACT: The natural course of early HCC is unknown, and its progression to intermediate and advanced HCC can be diverse. Some early stage HCC patients enjoy prolonged disease-free survival, whereas others suffer aggressive relapse to stage IV metastatic cancer within a year. Comparative proteomics of HCC tumor tissues was carried out using 2DDIGE and MALDI-TOF/TOF MS to identify proteins that can distinguish these two groups of stage I HCC patients. Twelve out of 148 differentially regulated protein spots were found to differ by approximately 2-fold for the relapse versus nonrelapse patient tissues. Four proteins, namely, heat shock 70 kDa protein 1, argininosuccinate synthase, isoform 2 of UTP-glucose-1phosphate uridylyltransferase, and transketolase, were shown to have the potential to differentiate metastatic relapse (MR) from nonrelapse (NR) HCC patients after validation by western blotting and immunohistochemical assays. Subsequent TMA analysis revealed a three marker panel of HSP70, ASS1, and UGP2 to be statistically significant in stratifying the two groups of HCC patients. This combination panel achieved high levels of sensitivity and specificity, which has potential for clinical use in identifying HCC tumors prone to MR. This stratification will allow development of clinical management, including close follow-up and possibly treatment options, in the near future. KEYWORDS: Hepatocellular carcinoma, relapse, recurrence, prognostic biomarkers, 2D-DIGE

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INTRODUCTION Hepatocellular carcinoma (HCC) represents the fourth most common cancer in the world and the third leading cause of cancer death in Singapore,1 and it is responsible for almost one million deaths each year. HCC often develops against a background of liver cirrhosis, commonly caused by viral infection, alcohol abuse, or aflatoxins.1,2 HCC is a highly malignant tumor with poor prognosis and high mortality. Improved prognosis determination has the potential to better guide early treatment decision making in the clinical management of HCC. Several staging systems have been developed to guide prognostic assessment and direct therapeutic interventions of HCC. These include the four major HCC staging systems: the tumor-node-metastasis (TNM) model, the Okuda classification model, the Cancer of the Liver Italian Program (CLIP) score, and the Barcelona Clinic Liver Cancer (BCLC) staging system.3 With the incorporation of information such as tumor size, tumor morphology, liver function, alpha-fetoprotein (AFP) level, presence of portal vein thrombosis, and cancer-related symptoms, these classification systems are able to demonstrate some consistency in the prognostic determination of HCC and © XXXX American Chemical Society

provide estimates of survival outcomes. However, these systems are reliant on pathological findings to classify patients with HCC into distinct groups. Hence, the complementary use of biomarkers that can help to stratify these patients into distinct HCC groups and predict their risk of recurrence will be advantageous in the clinical management of HCC. This is important because the natural course of early HCC is unknown and the disease progression is quite heterogeneous. Some early stage HCC patients enjoy a prolonged disease-free survival, whereas others relapse within a year and progress rapidly to stage IV metastatic cancer. Biomarker(s) with the ability to distinguish and predict early stage HCC patients who are at a higher risk of recurrence may have a strategic impact on postsurgical therapy decisions. Serum AFP level together with serial ultrasound imaging is currently used in the surveillance of HCC, but its Special Issue: Proteomics of Human Diseases: Pathogenesis, Diagnosis, Prognosis, and Treatment Received: March 6, 2014

A

dx.doi.org/10.1021/pr500229n | J. Proteome Res. XXXX, XXX, XXX−XXX

Journal of Proteome Research

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upward trend is often detected only after HCC progression and is shown only in 65% of recurrent patients, as reported by Chang et al.4 Hence, it may not be used reliably as a predictive marker to aid physicians in distinguishing early stage HCC patients with differing tumor aggressiveness. With advances in knowledge of HCC biology, various genes and proteins have been identified that are dysregulated during the carcinogenesis process. Some of these candidates have been investigated for their prognostic roles in HCC in various singlecandidate studies. Recently, proteomics analysis of four early HCCs and four non-HCC tissues derived from two cases of liver transplant surgery using LC−MS/MS identified a cyoskeletal protein, Talin-1, as being upregulated in HCC.5 The increased expression of Talin-1 in HCC was shown (a) to be associated with the dedifferentiation of HCC and (b) to result in a shorter time to recurrence after resection. This may be related to the higher rate of portal vein invasion in HCC. In another report, proteomics profiling of HCC tissues with different metastatic capabilities showed that N-myc downstream-regulated gene 1 (NDRG1) was correlated with metastasis and recurrence in HCC patients after liver transplantation. Immunohistochemical analyses of 143 HCC patients showed that NDRG1-positive expression had poor prognosis, either for shorter disease-free survival or overall survival. NDRG1 was thus suggested to be a novel biomarker for predicting HCC recurrence after liver transplantation.6 However, as compared to a single candidate, a panel of biomarkers would be more suitable to accurately predict HCC prognosis. Lee et al. carried out a genome-scale profiling of gene expression in HCC to identify a panel of genes that can classify HCC into subclasses for prognosis.7 They identified a panel of 406 survival genes that could be used to segregate patients with poor survival versus those with longer survival. They found that high expression of genes in the cell proliferation and ubiquitination pathways were the best predictors for an unfavorable outcome of the disease. However, this study could identify only a panel of genes that was able to subtype patients for their length of survival but not prediction of recurrence. Thus, these markers do not add much value to the currently used staging systems for guiding clinicians in stratifying patients for treatment decisions. Yokoo et al., on the other hand, used a 2DDIGE approach to identify prognostic markers for intrahepatic recurrence within 6 months of surgery.8 The HCC patients in the nonrecurrence group consisted of mainly stage II patients, whereas the early recurrence group consisted of mostly late stage HCC patients (stages III and IV). Thus, there may be a bias or inequality in the comparison of the two groups used in their study. To overcome this, comparative proteomics was carried out on the tumor (T) and adjacent nontumor (NT) tissues of stage I HCC patients who had disease-free survival for more than 2 years after surgery (nonaggressive tumors, n = 20) versus stage I HCC patients who had early relapse and progressed rapidly to stage IV or metastatic disease in less than 1 year postsurgery (aggressive tumors, n = 10) in our study. Using two-dimensional difference gel electrophoresis (2D-DIGE) coupled with MALDI TOF/ TOF MS, a list of differentially regulated proteins obtained, after validation by western blotting and immunohistochemical (IHC) and tissue microarray (TMA) analyses, could potentially serve as prognostic biomarkers to predict early recurrence in patients with aggressive HCC tumors.

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MATERIALS AND METHODS

HCC Tissue Samples

All tissue samples were derived from HCC patients who have undergone surgery at Singapore General Hospital, Singapore. The tumor and its corresponding nontumoral liver tissues from each patient were obtained for our analysis. The discovery sample set consisted of 30 optimal cutting temperature (OCT) compound-embedded resected liver tissues from HCC stage I patients, which were subsequently stored at −80 °C prior to analysis. All patients had solitary tumors that were confined to the liver, with no evidence of vascular involvement and spread of the tumor to the lymph nodes or other organs at the time of diagnosis and surgery. These patient tissues were separated into two main groups based on their prognostic outcomes: (a) a nonrelapse (NR) group from patients with significant diseasefree survival of longer than 2 years after curative surgery. These patients were considered to possess nonaggressive tumors (n = 20). (b) A metastatic relapse (MR) HCC group from patients that progressed rapidly to stage IV metastatic HCC in less than 1 to 2 years postsurgery. These were patients with aggressive tumors (n = 10). The demographic characteristics and clinicopathological data of this patient group are presented in Table 1A. For validation, a larger sample set consisting of 145 Table 1A. Demographic Characteristics and Clinicopathological Data of the HCC Patientsa parameters Gender male female Race Chinese Malay others Age AFP level (ng/mL) Tumor size (cm) Edmonson Grading grade 1 (welldifferentiated) grade 2 (moderately differentiated) grade 3 (poorly differentiated) grade 4 (undifferentiated) Cirrhosis yes no Hepatitis Virus hepatitis B hepatitis C no Recurrence-free time (months)

nonrelapse HCC (NR) (n = 20)

metastatic relapse HCC (MR) (n = 10)

15 (75%) 5 (25%)

10 (100%) 0 (0%)

17 (85%) 2 (10%) 1 (5%) 64 (54.5, 69.5)b

10 (100%) 0 (0%) 0 (0%) 63.5 (56.25, 72.25)b 145 (99.9, 479)b

9.85 (5.53, 48.38)b 3.5 (2.88, 6.25)b

6.75 (3.88, 14.75)b

3 (15%)

6 (60%)

11 (55%)

4 (40%)

6 (30%)

0 (0%)

0 (0%)

0 (0%)

12 (60%) 8 (40%)

7 (70%) 3 (30%)

12 (60%) 4 (20%) 4 (20%) 69.5 (43.25, 84.25)b

6 (60%) 2 (20%) 2 (20%) 13 (5.5, 16.25)b

p

0.65696c 0.01952c 0.07949c

8.39 × 10−6c

a

Thirty HCC patients whose liver tissues were used for 2D-DIGE analysis. bData are represented as follows: median (1st quartile, 3rd quartile). cAnalyzed by Mann−Whitney test.

B



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Table 1B. Demographic Characteristics and Clinicopathological Data of the HCC Patientsa parameters Gender male female Race Chinese Non-Chinese Age AFP level (ng/mL) Tumor size (cm) Edmonson Grading grade 1 (well-differentiated) grade 2 (moderately differentiated) grade 3 (poorly differentiated) grade 4 (undifferentiated) not specified TNM Staging stage I stage II stage III stage IV Cirrhosis yes no Hepatitis Virus hepatitis B hepatitis C no not tested Recurrence-free time (months)

nonrelapse (NR) (n = 46)

intrahepatic HCC relapsed (HR) (n = 47)

metastatic relapse HCC (MR) (n = 52)

35 (76.09%) 11 (23.91%)

36 (76.6%) 11 (23.4%)

44 (84.62%) 8 (15.38%)

34 (73.91%) 12 (26.09%) 64.11 (50.5, 69.70)b 7.75 (3.78, 77.78)b 4.5 (2.78, 8.00)b

39 (82.98%) 8 (17.02%) 64 (56, 72)b 20.15 (7.28, 98.95)b 3.50 (2.50, 5.25)b

46 (88.46%) 6 (11.54%) 63.05 (53.75, 69)b 31.65 (7.8, 232.50)b 5.35 (3.48, 9.13)b

9 (19.57%) 13 (28.26%)

9 (19.15%) 20 (42.55%)

5 (9.62%) 22 (42.31%)

22 (47.83%) 1 (2.17%) 1 (2.17%)

18 (38.30%) 0 (0%) 0 (0%)

23 (44.23%) 2 (3.85%) 0 (0%)

33 (71.74%) 6 (13.04%) 7 (15.22%) 0 (0%)

27 (57.45%) 10 (21.28%) 10 (21.28%) 0 (0%)

25 (48.08%) 15 (28.85%) 10 (19.23%) 2 (3.85%)

16 (34.78%) 30 (65.22%)

31 (65.96%) 16 (34.04%)

22 (42.31%) 30 (57.69%)

22 (47.83%) 2 (4.35%) 11 (23.91%) 11 (23.91%) 69 (41.5, 78.5)b

22 (46.81%) 2 (4.26%) 15 (31.91%) 8 (17.02%) 14 (6, 23)b

33 (63.46%0 2 (3.85%) 15 (28.85%) 2 (3.85%) 10.5 (4.0, 25.5)b

p

0.36921, 0.84408, 0.41489c 0.08679, 0.03137, 0.49929c 0.10498, 0.24753, 0.00244c

1.85 × 10−8, 6.53 × 10−7, 0.8931c

a One hundred forty five HCC patients whose liver tissues were used for TMA analysis. bData are represented as follows: median (first quartile, third quartile). cAnalyzed by Mann−Whitney test. Data are represented as comparisons of NR vs HR, NR vs MR, and HR vs MR.

were first washed with 1.5 mL of 1× phosphate buffered saline (PBS) before being ground using a sample grinding kit (GE Healthcare Bio-Sciences, Uppsala, Sweden). First, 30 μL of lysis solution consisting of 7 M urea, 2 M thiourea, 4% 3-(3cholamidopropyl)dimethyammonio-1-propanesulfonate (CHAPS), 10 mM Tris, 50 μg/mL DNase, 50 μg/mL RNase, and 1× HALT protease inhibitor cocktail (Pierce, Rockford, IL, USA) was added to 0.02−0.05 mg of liver tissue that was placed in the grinding tube containing grinding resin pellet. The tube and contents were snapped-frozen with liquid nitrogen. The liver tissue was then ground with a pestle as the sample was being thawed. This process of adding lysis solution followed by freezing and grinding was repeated twice. The total volume of lysis solution added to the ground samples ranged from 100 to 150 μL. The samples were then ultracentrifuged at 303 475g at 15 °C for 1 h to remove the resin and insoluble cellular debris. The protein concentrations of the tissue lysates were determined using the Pierce Coomassie Plus protein assay reagent kit. The liver tissue lysates were finally aliquoted into 20 μL fractions and stored at −80 °C until further use.

paraffin-embedded liver tissues from patients with stages I−IV HCC were used for TMA analysis. These specimens were segregated into three groups: (a) a NR group from HCC patients with disease-free survival after curative surgery (n = 46), (b) an intrahepatic relapse (HR) HCC group from HCC patients with intrahepatic HCC recurrence (single nodular or multiple nodules) after surgery (n = 47), and (c) a MR HCC group from HCC patients with stage IV metastatic HCC recurrence (with or without intrahepatic HCC recurrence) after surgery that was inclusive of hepatic metastasis and metastasis to other organs (n = 52). The demographic characteristics and clinicopathological data of these patients groups are shown in Table 1B. All of these postoperative patients were followed closely for up to 6−9 years postsurgery as outpatients. They were monitored for tumor recurrence by ultrasonography or contrast computerized tomography (CT) scan. Suspected recurrence(s) was further investigated via magnetic resonance imaging (MRI) scans, and hepatic angiography and/or postlipiodol CT scans were used to confirm intrahepatic recurrence. All samples were approved by the NUS Institutional Review Board (IRB) and SGH Institutional Review Board (CIRB) for this study.

Two-Dimensional Difference Gel Electrophoresis (2D-DIGE)

Protein Extraction

The pH of each tissue lysate was adjusted to 8.0−8.5 for optimal labeling of the protein samples with the CyDye fluors. Fifty micrograms of the tumor and its adjacent nontumor tissue lysates were labeled with 400 pmoles of Cy5 and Cy3 DIGE fluors,

Protein was extracted from 0.02 to 0.05 mg of the HCC liver tissues for labeling with the CyDye fluors and subsequently separated using 2D gel electrophoresis. The HCC liver tissues C



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respectively. In parallel, 50 μg of an internal standard containing equal amounts of all samples was labeled with 400 pmoles of Cy2. An experimental duplicate of each patient specimen was also performed with a dye swap in which Cy5 and Cy3 were used to label the nontumor and tumor lysates, respectively (Supporting Information Table S1). The labeling reaction was carried out in the dark for 30 min on ice and terminated by adding 1 μL of 10 mM L-lysine per reaction with a further incubation on ice for 10 min. Isoelectric focusing (IEF) of the CyDye-labeled protein lysates was performed on rehydrated precast 18 cm pH 3−10 NL IPG strips using the Ettan IPGphor/IPGphor II IEF unit (GE Healthcare Bio-Sciences) for a total of forty five 450 V h with the following parameters: (i) 200 V, 200 V h; (ii) 500 V, 250 V h; (iii) 1000 V, 500 V h; (iv) 1000−8000 V (gradient), 4500 V h; and (v) 8000 V, 40 000 V h. SDS-PAGE was then conducted on 13% polyacrylamide gels at 30 mA per gel at 10 °C in the dark. The 2D-DIGE gels were imaged on the Molecular Dynamics Typhoon 9410 variable mode imager (GE Healthcare BioSciences, Sunnyvale, CA, USA) using the fluorescence scan acquisition mode at the optimal excitation/emission wavelengths for each CyDye fluor (Cy2, 488/520 nm; Cy3, 532/580 nm; Cy5, 633/670 nm). DeCyder 2D differential analysis software version 6.5 (GE Healthcare Biosciences) was used to analyze the DIGE images as described in the Ettan DIGE user manual. Differentially expressed protein spots were selected on the basis of (1) an average ratio cutoff ≥ 2-fold difference between the tumor and its adjacent nontumor specimen, (2) Student’s t test and one-way ANOVA statistical values < 0.05, and (3) consistently present and shown to be differentially expressed in at least 80% of the gels for the relapsed patient group and/or the nonrelapse patient group.

Mass Spectrometry Analysis

Gels after DIGE image acquisition were silver-stained based on Vorum’s protocol9 with minor modifications. Protein spots selected for excision were manually excised from the silverstained gels and processed for MS analysis as described previously10 with modifications. MS and MS/MS spectra of peptides were obtained using the Applied Biosystems (Framingham, MA, USA) 4700 Proteomics Analyzer with TOF/TOF optics, operating in a result-dependent acquisition mode. For MS analysis, 1000 laser shots were accumulated for each sample. Using a mass calibration mixture containing six external standards (4700 Proteomics Analyzer mass standards kit, part no. 4333504, Applied Biosystems), each spectrum was calibrated to a mass accuracy of within 50 ppm. Up to 10 of the most intense ions from each sample were selected as precursors for MS/MS acquisition, with trypsin autolysis peaks and keratin tryptic peptides being excluded. The MS/MS analyses were performed using atmospheric air as the collision gas at collision energy of 2 kV and a collision gas pressure of ∼1 × 10−6 Torr. Stop conditions were implemented to obtain 2000−3000 shots, depending on the quality of the spectra. The MS together with MS/MS spectra were searched against the International Protein Index (IPI) human protein database version 3.43 (72 346 sequences; 30 410 250 residues) using GPS Explorer software version 3.6 (Applied Biosystems) with MASCOT search engine version 2.1 (Matrix Science). The following parameter settings were used: trypsin cleavage, maximum of one missed cleavage allowed, N-terminal acetylation, cysteine carbamidomethylation and methionine oxidation were selected as variable modifications, peptide mass tolerance and fragment mass tolerance set to 150 ppm and 0.4 Da, respectively, maximum peptide rank set to 2, and minimum ion score confidence interval for MS/MS data set to 50%. Protein identification was determined on the basis of the criteria of protein and total ion score: confidence interval (C.I.) > 90%, expectation value < 0.01, and with ≥2 peptides (and at least 1 peptide for proteins of low abundance) successfully sequenced. Protein with the best score was chosen from those that satisfied the above criteria, and a possible protein mixture with two or more proteins identified per protein spot were also assessed.

Image Acquisition and Analysis

The 2D-DIGE gels were imaged on the Molecular Dynamics Typhoon 9410 variable mode imager (GE Healthcare BioSciences, Sunnyvale, CA, USA) using the fluorescence scan acquisition mode at the optimal excitation/emission wavelengths for each CyDye fluor (Cy2, 488/520 nm; Cy3, 532/580 nm; Cy5, 633/670 nm). DeCyder 2D differential analysis software version 6.5 (GE Healthcare Biosciences) was used to analyze the DIGE images as described in the Ettan DIGE user manual. The images were acquired at a high resolution of 100 μm for image analysis. A total of 180 gel images (three images/gel) were viewed and processed using the ImageQuantTL software (GE Healthcare Biosciences). DeCyder 2D differential analysis software version 6.5 (GE Healthcare Biosciences) was used to analyze the DIGE images as described in the Ettan DIGE user manual. The DIGE images underwent spot detection and quantitation in the differential in-gel analysis (DIA) module, with an average of 3500 spots per gel being detected. The matched images were then analyzed using the biological variation analysis (BVA) and extended data analysis (EDA) modules. With the EDA module (a multivariate statistical module), hierarchical cluster analysis (HC) and principal component analysis (PCA) were performed at the same time based on criteria of ≥80% spot maps where protein is present, with Student’s t test ≤ 0.05 and false discovery rate (FDR) applied in the multiple test correction. A default setting of five principle components was selected for PCA calculation. PCA and HC allow determination of any spot map outliers and provide a comparison of the relationship between the spot maps and proteins.

Western Blot Analysis

Western blot analysis was carried out in the discovery set of 30 patients comprising nontumor (NT) and tumor (T) tissue lysates of 10 relapse and 20 nonrelapse patients. Twenty micrograms of HCC tissue lysates was resolved by 1D SDSPAGE on 13% polyacrylamide gels and electroblotted onto 0.2 μm PVDF membranes (BioRad, Hercules, CA). After electroblotting, the membranes were incubated in blocking solution containing 5% (w/v) nonfat dry milk in TBS-T (20 mM Tris, 137 mM NaCl, 0.1% Tween-20, pH7.5) with gentle shaking for 2 h at room temperature. Membranes were immunoprobed with primary antibodies diluted in TBS-T with 1% (w/v) BSA for 1 h. Primary antibodies used were as follows: mouse anti-heat shock protein 70, HSP70 (1:1000; ab48515; Abcam, Cambridge, UK), mouse anti-UDP-glucose pyrophosphorylase 2, UGP2 (1:1000; WH0007360M1; Sigma-Aldrich, St. Louis, MO, USA), rabbit anti-argininosuccinate synthase 1, ASS1 (1:1000; 16210-1AP; ProteinTech Group, Chicago, IL, USA), mouse antitransketolase, TKT (1:1000; ab112997; Abcam) (1:1000; H00007086-B01; Abnova, Taiwan), rabbit anti-enoyl-coenzyme A hydratase 1, ECHS1 (1:1200; 11305-1-AP; ProteinTech Group), rabbit anti-60S acidic ribosomal protein P0, RPLP0 D



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Figure 1. Analysis of 2D-DIGE gels for 30 HCC patient samples using the extended data analysis (EDA) module of DeCyder image analysis software. (A) Hierarchical clustering of the protein expression. Three main clusters were observed in the HCC specimens: (I) relapsed (R) tumor subgroup, (II) nontumor of both NR and R subgroups, and (III) nonrelapse tumor subgroup. (B) Principal component analysis (PCA) of the spot maps generated by 2D-DIGE gels after analysis by EDA module of DeCyder software. Numbers 1−10 represent the spot maps generated for the 10 stage IV metastatic relapse HCC patients, and 11−30 represent the spot maps generated for the 20 nonrelapse HCC patients. Filter criteria was set as ≥80% of the spot maps and p ≤ 0.05 for the Student’s t test.

Immunohistochemistry and Tissue Microarray

(1:2000; 11290-2-AP; ProteinTech Group), and rabbit antigalactokinase 1, GALK1 (1:1200; 15284-1-AP; ProteinTech Group). HRP-conjugated sheep anti-mouse IgG (1:5000; NXA931; GE Healthcare Bio-Sciences) and HRP-conjugated goat anti-rabbit IgG (1:2500; SC-2004; Santa Cruz Biotechnology Inc., Santa Cruz, CA, USA) in TBS-T with 1% (w/v) BSA were used as secondary antibodies. Three 10 min washes were carried out after each antibody incubation step. Subsequent visualization was performed using ECL western blotting detection reagents (GE Healthcare Bio-Sciences) and/or Super Signal West Dura substrate (Pierce). The intensities of the immunostained bands were quantitated using Bio-Rad Quantity One 1D analysis software.

Paraffin-embedded HCC tissue sample blocks from the discovery set of 30 patients were sectioned at 4 μm thickness for immunohistochemical (IHC) analysis. These tissue sections consisted of the tumor regions and their adjacent nontumor regions. For tissue microarray (TMA), the validation set consisting of 145 paraffin-embedded tissue specimens was first sectioned for hematoxylin and eosin staining. Nontumor and tumor regions were then determined, and tissue cylinders of ∼1 mm diameter from the selected regions were punched from the paraffin-embedded tissue blocks onto a recipient paraffin block with a manual tissue arrayer (MTA-1, Beecher Instruments, Sun Prairie, WI, USA). A total of 16 TMA blocks (8 nontumor and 8 tumor) were constructed and subsequently sectioned at 4 μm E


a

1074 2114 2575 2346

2581

8 9 10 11

12

1.00 × 10−5 1.60 × 10−5 0.0099 0.000 58 9.70 × 10−6 0.006 7.50 × 10−5 4.20 × 10−11 1.10 × 10−5 2.90 × 10−8 0.000 21

1.00 × 10−5 1.60 × 10−5 0.0099 0.000 58 9.70 × 10−6 0.006 7.50 × 10−5

4.20 × 10−11 1.10 × 10−5 2.90 × 10−8 0.000 21

0.000 33

2.33 2.77 1.51 1.69 1.71 1.68 1.89

−2.63 −1.88 −1.99 −1.49

−1.72 0.000 33

1-ANOVA

t test

average fold ratio (T/ NT ratio)

−3.06

−4.53 −3.07 −3.94 −3.03

4.48 4.68 2.99 3.22 3.05 4.44 3.62

t test 1-ANOVA

0.0019

0.000 84

Proteins Upregulated in Tumor 0.000 21 6.50 × 10−5 −5 2.10 × 10 1.20 × 10−6 0.000 54 0.000 63 0.000 34 0.0042 9.40 × 10−5 8.40 × 10−5 2.00 × 10−5 2.50 × 10−5 0.0011 0.000 99 Proteins Downregulated in Tumor 1.10 × 10−8 7.00 × 10−8 4.20 × 10−6 4.90 × 10−6 2.80 × 10−5 2.60 × 10−5 0.0019 0.002

Average fold ratio (T/ NT ratio)

metastatic relapse HCC (MR)

Cutoff average ratios of ≥2 with Student’s t test and one-way ANOVA statistical values of p < 0.05.

547 550 559 560 555 1400 1462

1 2 3 4 5 6 7

spot no.

nonrelapse HCC (NR)

31387.39

56940.25

Isoform 2 of UTP-glucose-1-phosphate uridylyltransferase (UGP2) Enoyl-CoA hydratase, mitochondrial precursor (ECHS1)

42272.23 34273.51

Galactokinase (GALK1) 60S acidic ribosomal protein P0 (RPLP0)

46530.37 43009.92

67877.63

Transketolase (TKT)

Argininosuccinate synthase (ASS1) Serine-pyruvate aminotransferase (AGXT)

77495.65

theor. MW

Heat shock 70 kDa protein 1 (HSPA1A; HSPA1B)

protein

Table 2. List of Differentially Expressed Proteins between the Metastatic Relapse (MR) and Nonrelapse (NR) HCC Patient Tissuesa

8.34

8.15

8.08 8.61

6.04 5.72

7.58

5.97

theor. pI

17292.59

47409.84 29401.87 18607.41 23808.41

78076.92 78076.92 78076.92 78882.05 79687.18 41170.94 40692.31

exp. MW

5.35

7.3 6.16 6.5 6.88

5.11 5.19 7.52 7.7 7.87 5.8 5.8

exp. pI

Journal of Proteome Research Article

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Figure 2. Western blot and immunohistochemical (IHC) analysis of four differentially expressed proteins: (A) HSP70, (B) TKT, (C) UGP2, and (D) ASS1. (I) Box plots based on the comparison of tumor/nontumor (T/NT) ratio of the immuno-positive band intensities (western blots) obtained from the NR (n = 20) and MR (n = 9) HCC groups. (II) Box plots based on the T/NT ratio of IHC staining index obtained from NR (n = 19) and MR (n = 8) HCC groups. (III) Representative IHC tissue sections of the HCC patients from the NR and MR groups. Statistical analysis (p) was performed using the Mann−Whitney test.

thickness for immunohistochemical staining. HSP70 staining was carried out using the Bond Max Automated Immunohistochemistry Vision Biosystem (Leica Microsystems GmbH, Wetzlar, Germany), whereas ASS1, UGP2, and TKT staining were performed manually using the Dako REAL Envision Detection System (DAKO Corp., Glostrup, Denmark). Tissue sections on coated slides were deparaffinized in xylene and hydrated in ethanol prior to antigen retrieval. Antigen retrieval conditions of the four stains were as follows: Leica BOND-MAX epitope retrieval solution 1 at 100 °C, 20 min for HSP70; 1 mM TrisEDTA (pH 9) at 100 °C, 20 min for ASS1 and TKT; 10 mM citrate (pH 6) at 100 °C with pressure, 20 min for UGP2. Endogeneous peroxidase activities were blocked using hydrogen peroxide solution for 10 min before being incubated with primary antibodies for 1 h at room temperature. The following antibody dilutions were used: HSP70 at 1:6400 (ab48515; Abcam), UGP2 at 1:400 (WH0007360M1; Sigma-Aldrich), ASS1 at 1:1000 (16210-1-AP; ProteinTech Group), TKT at 1:1000 (ab112997; Abcam), and 1:1500 (H00007086-B01; Abnova). Slides were washed and incubated with horseradish peroxidase-conjugated goat anti-rabbit/mouse immunoglobulins for 20 to 30 min at room temperature, and this was followed by detection with a

ready-to-use 3,3′-diaminobenzidine chromogen solution for 10 to 15 min. The slides were then counterstained with hematoxylin, dehydrated, mounted with a coverslip, and viewed under a light microscope. Scoring of stained tissue slides was evaluated by a skilled pathologist without prior knowledge of the clinicopathological information on the specimens. Staining intensity was graded on a scale of 0 to 3 (0 = no staining; 1 = weak staining; 2 = moderate staining; 3 = strong staining) and percentage of positively stained cells was scored from 0 to 100%. Staining index of each specimen was calculated as the sum of these two parameters. Differences in protein expression between tumor (T) and nontumor (NT) HCC liver sections were calculated by subtracting the staining index of NT from the that of the T tissues. Statistical Tests of Western Blotting and Immunohistochemical Data

The XLSTAT statistical software for Microsoft Excel version 2012.3.03 (Addinsoft SARL, Paris, France) and OriginPro data analysis and graphing software version 8.5 (OriginLab, Northampton, MA, USA) were used to perform statistical analysis on the western blotting and immunohistochemical stain data. G



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Figure 3. TMA analyses of HSP70, ASS1, and UGP2 in HCC tissues (a) Representative TMA images of the 145 HCC patient tissue sections. Immunostaining of HSP70, ASS1, and UGP2 in the nontumor and tumor tissue sections of representative NR, HR, and MR HCC patients. (b) Box plots based on TMA analysis of (I) HSP70, (II) HSP70 (stage I HCC), (III) ASS1, and (IV) UGP2 in the 145 HCC patients as well as (V) serum AFP levels in these patients. Statistical analysis was performed using the Mann−Whitney test.

Correlation between protein expression of the potential biomarkers derived from immunohistochemical staining and the clinicopathological data was performed by nonparametric

correlation analysis, such as the Mann−Whitney test. To facilitate the validation of the biomarkers for distinguishing the different clinicopathological stages of HCC patients, receiver H



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Validation by Western Blotting and Immunohistochemistry

operating characteristic (ROC) curve and area under the curve (AUC) were constructed. The predictive performance of the potential prognostic biomarkers as a panel was assessed using principle component analysis (PCA), and ROC curves were constructed using the PCA factor scores.

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Four out of the eight proteins were successfully validated by western blotting and immunohistochemistry (IHC). The regulation trends for these four proteins corroborated the 2DDIGE results (Supporting Information Figure S2). Figure 2, panels A-I−D-I, showed the box plots for the western blot intensity data for HSP70, TKT, UGP2, and ASS1, respectively (see Supporting Information Figure S3 for the western blot images). By applying the nonparametric Mann−Whitney statistical test, HSP70 and TKT were confirmed to be significantly more upregulated in the tumors of the MR HCC patient group than that of the NR group (with p = 0.005 and 0.031, respectively). Subsequent IHC staining of paraffinembedded tissue sections of the same set of resected HCC liver tissues was performed (Figures 2A-II−D-II and 2A-III−DIII). Mann−Whitney statistical test confirmed a significant difference for HSP70 upregulation between the relapse and nonrelapse groups (p = 0.021) (Figure 2A-II) but not for TKT, ASS1, and UGP2 (Figure 2B-II,C-II,D-II). This result could be attributed to the small sample size and the heterogeneity of the HCC tissue sections. Hence, to reassess and validate the prognostic potential of these proteins as specific biomarkers for aggressive metastatic HCC tumors, we performed a larger-scale TMA screening experiment that included a group of HCC patients with only intrahepatic recurrence. The sample size was also increased to 145 patients with ∼ n = 50 for each of the NR HCC, HR HCC, and MR HCC patient groups. Addition of the HR patient group allowed us to eliminate the possibility of the biomarkers as being merely general HCC recurrence prognostic markers, thus ensuring that they are also more specific to stage IV metastatic relapse HCC. The representative TMA images and box plots for these TMA results are shown in Figure 3, panels a and b, respectively, and they indicated that HSP70, ASS1, and UGP2 all displayed significance in the prediction of MR HCC (also see Supporting Information Table S2 for ROC results).

RESULTS

Two-Dimensional DIGE Analyses of HCC Tissues

Using the EDA module of the DeCyder 2D differential analysis software for the analyses of the protein profiles of the 180 (30 × 6) DIGE images, we were able to segregate the patients into distinct HCC groups using hierarchical clustering analysis and principal component analysis (PCA) (Figure 1). Three main clusters were observed: the nontumor subgroups (II) were segregated from the relapsed tumor (I) and the nonrelapse tumor (III) subgroups (Figure 1A). Although patients A−C were classified under nonrelapse HCC, they eventually relapsed and progressed to metastatic HCC after 2 years. The tumor profiles of these patients were shown to cluster together with the tumor profiles of the relapsed subgroup. As such, our result seemed to demonstrate that we were able to segregate metastatic HCC relapse cases from the nonrelapse cases regardless of the recurrence-free period. As illustrated in Figure 1B, the PCA of the spot maps (for protein spots present in ≥80% spot maps and p ≤ 0.05 Student’s t test) showed distinctive clustering into four different HCC subgroups: nonrelapse (NR) tumor and its nontumor counterpart and metastatic relapse (MR) tumor and its nontumor counterpart HCC subgroups. NR and MR nontumor spots were observed to be separately clustered at the top left quadrant, whereas the NR and MR tumor spots were clustered at the bottom left and right quadrants, respectively. Hence, this result demonstrated that metastatic relapse (MR) HCC cases could be segregated from the nonrelapse (NR) HCC cases (Figure 1).

TMA Analyses of HSP70, ASS1, and UGP2

Differentially Expressed Proteins Found in the Metastatic Relapse (MR) versus Nonrelapse (NR) HCC Tissues

HSP70. In the TMA analysis, HSP70 showed zero basal staining on the nontumor tissues in general but was observed to be upregulated in the tumors of both HR and MR patient groups compared to that of their respective nontumor tissues (Figure 3a,b-I). Furthermore, HSP70 was found to be more upregulated in the MR than HR HCC tissues. To ascertain the potential of HSP70 as a prognostic biomarker for HCC recurrence, a series of receiver operating characteristic (ROC) curves using the subtracted (T minus NT) immunostain indices acquired from the 145 patients was constructed, and the results are summarized in Supporting Information Table S2. The results showed that comparison of HSP70 expression was able to distinguish the R HCC groups from the NR HCC group with an AUC of 0.617 and p = 0.010 as well as MR from NR HCC patients with an AUC of 0.708 and p = 1.277 × 10−4. ASS1 and UGP2. The TMA results showed strong immunostaining of both ASS1 and UGP2 in the nontumor sections of the HCC tissues as illustrated in Figure 3a. Expression of ASS1 was notably more downregulated in the tumor tissues of MR HCC group than in tumors of the other two groups (Figures 3a,b-III). This result was corroborated by the ROC analysis data (Supporting Information Table S2). Similarly, UGP2 was demonstrated to be significantly more downregulated in the tumors of the MR HCC subgroup, as shown in Figure 3a,b-IV. ROC analysis of UGP2 regulation was able to distinguish specifically the MR group from the NR group (AUC = 0.608, p =

From a total of ∼3000 protein spots detected by DeCyder, 148 protein spots were found to be differentially regulated in the HCC tumor tissues when compared to their adjacent nontumor counterparts for both the MR HCC and NR HCC groups. These differentially expressed protein spots have a cutoff average ratio of ≥2-fold, with Student’s t test and one-way ANOVA statistical values of p < 0.05. Among these differentially expressed proteins spots, 12 were found to be regulated by approximately 2-fold or more when the expression levels of the relapse patient tissues were compared to that of the nonrelapse patient tissues (Table 2 and Supporting Information Figure S1). Of these, seven were found to be upregulated and five to be downregulated, and they corresponded to eight unique proteins after identification by MALDI-TOF/TOF MS (Table 2 and Supporting Information Data S1). Six of these proteins, namely, transketolase (TKT), galactokinase, UTP-glucose-1-phosphate uridylyltransferase (UGP2), argininosuccinate synthase (ASS1), serine-pyruvate aminotransferase, and enoyl-coA hydratase, are metabolic enzymes. The other two proteins are 60S acidic ribosomal protein P0 and heat shock 70 kDa protein 1 (HSP70). This may suggest that altered metabolism (also known as Warburg effect) in the liver tumor tissues could have contributed to the development of relapsed and nonrelapsed HCC. I



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Figure 4. continued

J



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Figure 4. Performance of HSP70, ASS1, and UGP2 as potential biomarkers for prognosis of HCC recurrence. ROC curves for (a) the three individual biomarkers and serum AFP levels and (b) HSP70, ASS1, and UGP2 as panel of (I) three or (II−IV) two biomarkers for prognosis of MR HCC. C.I., confidence interval; PPV, positive predictive value; NPV, negative predictive value. Statistical analysis (p) was performed using the Mann−Whitney test. (c) Illustration on the algorithmic use of HSP70, ASS1, and UGP2 as prognostic biomarkers.

Table 3. Probability of Recurrent and Nonrecurrent HCC Cases Detected in 145 HCC Patients Using an Algorithmic Combination of the Three Biomarkersa no. of HCC cases biomarkers HSP70+ HSP70+/ASS1+ HSP70+/ASS1+/ UGP2+

NR

HR

MR

% of recurrent HCC in biomarker-positive cases

% of nonrecurrent HCC in biomarker-positive cases

17 (21.5%) 13 (21%) 3 (8.8%)

21 (26.6%) 15 (24.2%) 10 (29.4%)

41 (51.9%) 34 (54.8%) 21 (61.8%)

78.5% (34% HR; 66% MR) 79.0% (30.6% HR; 69.4% MR) 91.2% (32.3% HR; 67.7% MR)

21.5% 21% 8.8%

biomarkers

NR

HR

MR

% of recurrent HCC in biomarker-negative cases

% of nonrecurrent HCC in biomarker-negative cases

negative for all three biomarkers

9 (69.2%)

3 (23.1%)

1 (7.7%)

30.8%

69.2%

a

The percentages of recurrent and nonrecurrent HCC cases were determined on the basis of positivity in HSP70, followed by ASS1 and lastly UGP2. + represent positivity in the prognostic value of biomarker in which HSP70 is upregulated and ASS1 and UGP2 are downregulated. For example, the HSP70+ column represents the number of cases that showed upregulation of HSP70 among each patient group (NR, HR, or MR). The percentage was determined on the basis of the number of positive cases in each patient group relative to the total number of positive cases in all the three groups. The percent of recurrent HCC in biomarker positive cases was obtained from the sum of positive HR and MR cases. Using a panel of the three biomarkers (HSP70+/ASS1+/UGP2+), the percentages of recurrent and nonrecurrent HCC cases were determined on the basis of positivity in HSP70, followed by ASS1 and lastly UGP2 (29.4% + 61.8% = 91.2%). The 69.2% of nonrecurrent HCC in biomarker-negative cases was derived from the nine NR cases relative to a total of 13 cases that showed negativity for all three biomarkers.

the combined biomarkers are shown in Figure 4b. HSP70 when combined with ASS1 showed an increase in sensitivity from 80.4 to 90% for the prediction of MR HCC (Figure 4b-II), whereas ASS1 when combined with UGP2 showed an increase in specificity (from 64.4 to 76.7%) in the prediction of MR HCC (Figure 4b-IV). An algorithmic method of utilizing the three biomarkers to better represent their prediction for MR HCC was evaluated as shown in Figure 4c. The prognostic value of the biomarker(s) was illustrated in Table 3, whereby HSP70 is upregulated and

0.023 by Mann−Whitney test) with 61.5% sensitivity and 64.4% specificity at the threshold immunostain index (T−NT) of UGP2 < 0. HSP70, ASS1, and UGP2 as a Panel of Three Biomarkers. Because all three biomarkers were able to individually distinguish MR HCC patients from both NR and HR HCC patients (Figure 3b and Supporting Information Table S2), we decided to combine them to assess their performance as a panel of two or three biomarkers. The ROC curves of the individual biomarkers are presented in Figure 4a, and those for K



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specificity. Our findings were in agreement with previous studies that associated overexpression of HSP70 with poor prognosis in cancer patients,14 including HCC patients.15−18 However, ours is the first proteomics study to show that HSP70 expression in resected HCC tumors can be used to predict recurrence in HCC patients. When correlated with TNM staging and vascular invasion, HSP70 levels were slightly higher in tumors of advanced TNM stages (p = 0.027) but had no positive correlation with venous infiltration status (p = 0.231). Hence, to ensure that HSP70 prediction of HCC recurrence was not dependent on TNM staging and to accentuate its potential in the prognosis of HCC recurrence in early stage HCC patients, the data for 85 stage I HCC patients were selected from the 145 patients to perform ROC analysis. As shown in Figure 3b-II and Supporting Information Table S2, it was demonstrated that the prognostic performance of HSP70 was not affected but was enhanced with an increase in sensitivity (from 80.4 to 88%) and specificity (from 63 to 76.9%) at the threshold immunostain index (T−NT) of HSP70 > 0 in distinguishing MR HCC patients from NR HCC patients. A similar trend was also observed for the prognosis of HR HCC patients versus MR HCC patients. Hence, HSP70 showed no dependence on TNM staging and was able to predict MR HCC in early stage (stage I) HCC patients with high confidence. Because we showed that increased expression of HSP70 positively predicts MR HCC even in early stage HCC patients, HSP70 could be a potential drug target. Indeed, silencing of HSP70 was reported to result in apoptosis and chemosensitization of tumor cells, including liver cancer cells.19−22 Negative expression of HSP70 was also suggested to be a protective factor that can decrease post-operative recurrence of liver hepatoma.23 Taken together, these observations suggest that HSP70 plays a crucial role in resisting insults during tumorigenesis and anticancer treatment and could be a potential target for cancer therapy.24,25

ASS1 and UGP2 are downregulated in HCC cases among each patient group (NR, HR, or MR). The percentage of recurrent HCC in biomarker positive cases was determined from the sum of positive HR and MR cases. Initial prognosis using HSP70 with a T−NT threshold value of HSP70 > 0 enabled the prediction of HCC recurrence with 78.5% confidence, with twice the probability (66%) for MR HCC as compared to 34% probability for HR HCC prediction (Table 3). Subsequent use of ASS1 (T−NT threshold value of ASS1 < −0.5) showed slight enhancement of the prediction of HCC recurrence to 79% confidence (69.4% MR versus 30.6% HR) and final addition of UGP2 (T−NT threshold value of UGP2 < 0) to the biomarker panel further enhanced the prediction of HCC recurrence to 91.2% confidence, with 67.7% probability for MR HCC versus 32.3% probability for HR HCC. Negativity for all three biomarkers exudes 69.2% (∼70%) confidence for no HCC recurrence, with very low probability of 7.7% for MR and 23.1% probability for HR. Prognostic Value of Alpha-Fetoprotein (AFP) in the HCC Patients

AFP has been routinely used as a cancer marker for clinical screening of HCC patients.4,11−13 To accentuate the value of our prognostic biomarkers, we also assessed the performance of AFP in the prediction of MR HCC on the same group of patients. The AFP levels in the sera/plasma of these HCC patients were obtained prior to HCC tumor resection. The increment in AFP level was observed in both HR and MR HCC patients as illustrated by the box plots depicted in Figure 3b-V. Although ROC (Figure 4a) and Mann−Whitney statistical analysis demonstrated that increase in AFP level can help to distinguish patients in the MR HCC group from the NR HCC group (Supporting Information Table S2), extreme variations in AFP levels with a wide dynamic range even within individual groups were observed (Table 1B and Figure 3b-V). Consequently, this might render it less specific in MR HCC prediction. Indeed, addition of AFP to the three biomarker panel showed no enhancement in sensitivity for the prediction of MR from NR (86%), but it reduced the specificity of the prediction from 51.2% (Figure 4b-I) to 48.7%, with an AUC of 0.646 and reduced significance of p = 0.045.

ASS1 and UGP2 as Predictive Biomarkers for Metastatic Relapse HCC

ASS1 was shown to be capable of differentiating MR HCC tumors from NR tumors (AUC of 0.621, p = 0.021) with a high sensitivity of 84.6% at a threshold immunostain index (T−NT) of ASS1 < −0.5 and differentiating MR HCC tumors from HR HCC tumors (AUC of 0.602, p = 0.041) with a high specificity of 80.4% at a threshold immunostain index of ASS1 ≤ −1.8. Reduced expression of ASS1 has been reported to be a predictive biomarker in osteosarcoma for metastasis and poor prognosis.26 Our expression data seem to concur with the notion that HCC tumors are arginine auxotrophic. These tumors downregulate expression of ASS1, leading to an intrinsic dependence on extracellular arginine due to its inability to synthesize arginine for growth, and this is known as arginine auxotrophy. Selective elimination of extracellular arginine would trigger apoptosis in these arginine auxotrophic tumors,27 and this is an effective anticancer therapy that has been demonstrated in HCC in previous studies.28,29 UGP2 was also found to be downregulated in colorectal tumor tissues, and this was proposed to be associated with the altered metabolic pathways in colorectal cancer.30 In the liver, UDPglucose produced by UGP2 is a direct precursor of glycogen synthesis. Reduced expression of UGP2 would promote the progression of glycolysis. This could be linked to the dependence of most cancer cells on aerobic glycolysis (the Warburg effect) to

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DISCUSSION In this study, 2D-DIGE was employed for comparative proteomics analyses of HCC patients’ liver tissues with the aim of discovering novel prognostic biomarkers that are differentially expressed in the HCC tumor for prediction of HCC recurrence. Our results showed that three proteins, heat shock 70 kDa protein 1 (HSP70), argininosuccinate synthase (ASS1), and isoform 2 of UTP-glucose-1-phosphate uridylyltransferase (UGP2), have the potential to predict HCC recurrence and differentiate MR from NR HCC patients. HSP70 as a Prognostic Biomarker for Metastatic Relapse HCC Even in Early Stage HCC Patients

We noted that HSP70 could better distinguish MR HCC tumors from NR HCC tumors with an increase in sensitivity from 63.3% (R) to 80.4% (MR) at the threshold immunostain index (T−NT) of HSP70 > 0. HSP70 could also differentiate tumors of MR HCC group from tumors of the HR HCC group (AUC of 0.731, p = 2.074 × 10−5) at a threshold immunostain index of HSP70 > 0.1. Hence, this showed that HSP70 may not only be a potential prognostic biomarker for HCC relapse but also can predict MR HCC from HR HCC with enhanced sensitivity and L



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generate energy for cellular proliferation.31−33 The preference for glycolysis in solid tumors had also suggested glycolytic enzymes as potential targets for therapeutic interventions.34−38 Overall, both ASS1 and UGP2 showed specificity in predicting MR HCC tumors from NR HCC tumors. The regulation of both biomarkers also did not show any correlation with TNM staging and vascular invasion (results not shown).

I), ASS1, UGP2, and AFP as prognostic biomarkers for HCC; representative silver-stained image of the 2D-DIGE gel and the identities of the differentially expressed proteins between the metastatic relapse and nonrelapse HCC tissue samples; 2DDIGE images of HSP70, TKT, UGP2, and ASS1; western blot analysis of HSP70, TKT, UGP2, and ASS1 in the tumor and nontumor tissue lysates of 10 relapse and 20 nonrelapse patients; MS and MS/MS data of the selected 12 differentially expressed protein spots; and western blot, IHC, and TMA data. This material is available free of charge via the Internet at http://pubs. acs.org.

HSP70, ASS1, and UGP2 Have Higher Specificity than AFP for Metastatic Relapse HCC

We showed that a combination of HSP70 and ASS1 as biomarkers displayed higher sensitivity for predicting MR versus NR HCC from early stage HCC biopsies, and the prediction can be further enhanced with an increase in specificity when ASS1 is used in combination with UGP2. With an algorithmic utilization of the three biomarkers (HSP70 followed by ASS1 and subsequently UGP2), we were able to predict recurrence of HCC in surgically resectable patients with a confidence of 91.2% and a higher specificity to MR than local recurrence (Table 3). In comparison, there is no significant difference in AFP levels when comparing HR versus MR groups (p value =0.249) (Supporting Information Table S2), and AFP also exhibited lower sensitivity and specificity as compared to HSP70 in distinguishing MR patients from NR patients (Figure 4a and Supporting Information Table S2). Hence, AFP, unlike HSP70, ASS1, and UGP2, is less specific for MR HCC. As such, AFP can be employed for prognostic screening of HCC recurrent patients but not specifically for MR in HCC patients. The panel of three biomarkers has shown superior performance to that of AFP and achieved high levels of sensitivity and specificity in identifying HCC patients who are more susceptible to MR. We believe that this panel is a clinically viable biomarker panel that can be used on clinical-grade formalin-fixed paraffinembedded (FFPE) samples and will add value as an additional prognostic marker. Furthermore, these groups of MR patients should also benefit from further therapy. Currently, there is no established local or systemic adjuvant therapy for resected HCC, although clinical trials are ongoing. Still, the biomarker panel that we have identified may potentially enable the selection of patients for early intervention strategies to reduce occurrence of HCC relapse. The markers themselves may represent potential targets for anticancer therapies and compel further investigation.

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Corresponding Author

*Tel.: (65) 6516 3252; Fax: (65) 6779 1453; E-mail: bchcm@ nus.edu.sg. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors wish to acknowledge the financial support of the National Medical Research Council (NMRC), Singapore (NMRC grant no. 1259/2010). Ethics approval was obtained from the NUS Institutional Review Board (IRB) and SGH Institutional Review Board (CIRB) for this work.

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REFERENCES

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CONCLUSIONS Currently, clinical staging systems exist that focus on both patient and tumor factors without directed markers that focus on tumor biology. We believe that this is the first study using comparative proteomics to identify biomarkers for MR in HCC patients. From the list of proteomic markers, further analysis using IHC on FFPE samples has revealed three biomarkers (HSP70, ASS1, and UGP2) that have the potential to predict MR HCC from NR HCC patients. Our results also showed that as a single candidate, HSP70 is the best performing prognostic biomarker and is more specific than AFP in identifying metastatic relapse/recurrence from nonrelapse HCC patients. When used in combination, the three biomarkers achieved high levels of sensitivity and specificity for better prognostication of HCC.

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AUTHOR INFORMATION

ASSOCIATED CONTENT

S Supporting Information *

Experimental design for the 2D-DIGE analysis of the HCC tissues; ROC data for the performance of HSP70, HSP70 (stage M



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