ARTICLE pubs.acs.org/jpr
iTRAQ2DLCESIMS/MS Based Identification of a New Set of Immunohistochemical Biomarkers for Classification of Dysplastic Nodules and Small Hepatocellular Carcinoma Guang-Zhi Jin,†,‡,§,# Yan Li,†,# Wen-Ming Cong,*,^ Hua Yu,^ Hui Dong,^ Hong Shu,† Xiao-Hui Liu,§ Guo-Quan Yan,§ Lei Zhang,§ Yang Zhang,§ Xiao-Nan Kang,§ Kun Guo,† Zhe-Dong Wang,‡ Peng-Yuan Yang,§ and Yin-Kun Liu*,†,§ †
Liver Cancer Institute, Zhong Shan Hospital and §Institutes of Biomedical Sciences, Fudan University, Shanghai, China ‡ Suzhou Etern Hospital, Wujiang City, Jiangsu Province, China ^ Department of Pathology, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
bS Supporting Information ABSTRACT: The study aims to develop novel clinical immunohistochemical biomarkers for distinguishing small hepatocellular carcinoma (sHCC) from dysplastic nodules (DN). iTRAQ2DLCESIMS/MS technique was used to screen immunohistochemical biomarkers between precancerous lesions (liver cirrhosis and DN) and sHCC. A total of 1951 proteins were quantified, including 52 proteins upregulated in sHCC and 95 proteins downregulated in sHCC by at least 1.25- or 0.8-fold at p < 0.05. The selected biomarker candidates were further verified using Western blotting and immunohistochemistry. Furthermore, receiver operation characteristics (ROC) curves and logistic regression model were carried out to evaluate the diagnostic values of the biomarkers. Finally, aminoacylase-1 (ACY1) and sequestosome-1 (SQSTM1) were chosen as novel candidate biomarkers for distinction of sHCC from DN. A constructed logistic regression model included ACY1, SQSTM1, and CD34. The sensitivity and specificity of this model for distinguishing sHCC from DN was 96.1% and 96.7%. In conclusion, ACY1 and SQSTM1 were identified as novel immunohistochemical biomarkers distinguishing sHCC from DN. In conclusion, expression levels of CD34, ACY1, and SQSTM1 can be used to establish an accurate diagnostic model for distinction of sHCC from DN. KEYWORDS: dysplastic nodules, small hepatocellular carcinoma, iTRAQ2DLCESIMS/MS, formalin-fixed and paraffinembedded tissues, immunohistochemical marker, CD34, aminoacylase-1, sequestosome-1
’ INTRODUCTION Hepatocellular carcinoma (HCC) is one of the most prevalent human cancers worldwide; 82% of cases occur in developing countries (with 55% in China).1 HCC occurs mainly in patients with chronic liver diseases such as HBV or HCV infection-based liver cirrhosis. Dysplastic nodules (DN) and small HCC (sHCC) are small lesions usually occurring in a cirrhotic liver. However, distinguishing between sHCC and DN is often difficult on the basis of morphologic features alone. Although current research achievements in imaging techniques have increased the frequency of detection of small lesions, there are still issues to be explored such as low specificity.2,3 Moreover if patients can be treated by resection in the stage of sHCC, the outcome of patients will be improved significantly.4,5 However, detection of DN, especially high-grade DN (HGDN), and correct differentiation from sHCC (e3 cm) are sometimes difficult, especially depending only on imaging techniques or experienced pathologists. It has been reported that glypican 3, HSP70, glutamine synthetase (GS), CD31, and CD34 could serve as molecular r 2011 American Chemical Society
markers for early HCC or HCC.68 However, the diagnostic sensitivity and specificity of immunohistochemistry is still limited. For instance, glypican 3 has a sensitivity of 77% and specificity of 96%,9 and CD34 staining of sinusoidal spaces may be increased in another type of liver nodules, resulting in its low specificity for detection of HCC.10,11 R-Fetoprotein (AFP) is the widely used molecular marker for diagnosis and detection of HCC. However, the sensitivity decreases to about 40% when it is used for detection of sHCC.12,13 In addition, a significant increase in serum AFP level is detected in a considerable number of patients with chronic liver disease.12,14 Moreover, a limited immunohistochemical detection rate (about 1737.5%) of AFP was also demonstrated by several reports.12,1517 Thus, more excellent immunohistological diagnostic markers for diagnosis of sHCC or distinction of sHCC from DN are Received: January 6, 2011 Published: June 01, 2011 3418
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Journal of Proteome Research needed. In this report, we identified aminoacylase-1 (ACY1), phosphoglucomutase-1 (PGM1), sequestosome-1 (SQSTM1), sulfite oxidase (SUOX), and glycogen phosphorylase (PYGL) as candidate immunohistochemical markers using isobaric stable isotope tags and two-dimensional liquid chromatography electronic spray ionizationtandem mass spectrometry (iTRAQ2DLCESIMS/MS) technique, Western blotting, and immunohistochemistry. This study was also designed to evaluate the sensitivity and specificity of the individual markers and the combination of CD34, ACY1, and SQSTM1 significantly improved specificity for detection of sHCC and may provide useful tool for distinction of sHCC from DN.
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Table 1. Clinicopathological Characteristics of 200 Lesionsa group variable
with DN
with sHCC
patients
83
40
69
no. of lesions
83
48
69
male
64
36
58
female
19
4
11
sex
age
’ MATERIALS AND METHODS
mean
47.3
52.5
51
SD cirrhosis
8.6 83
9.5 35
10 54
71
38
60
2
4
3
11
12
67
e20 ng/mL
71
22
31
>20 ng/mL DN grade
12
18
38
etiology
Patients and Specimens
HBsAg positive
Formalin-fixed paraffin-embedded (FFPE) tissue samples of liver cirrhosis (LC), DN, and sHCC specimens were classified by histopathological diagnosis. DN, including low-grade DN (LGDN), HGDN, and HCC were based on the criteria proposed previously.1820 All HCC were selected with the only restriction that it had to be well or moderately differentiated and smaller e3 cm. All of the FFPE tissue blocks of human liver tissues were obtained from patients who had undergone curative resection between 2005 and 2009 at the Eastern Hepatobiliary Surgery Hospital, Second Military Medical University in Shanghai, China. The baseline characteristics of patients are summarized and listed in Table 1. All LC, DN, and sHCC classification was confirmed histologically by 2 pathologists (W-M.C. and H.D.). Institutional review board approval and written informed consent from each patient were obtained.
HCV-Ab positive CD 34 positive AFP
LGDN
26
HGDN
22
DN with HCC
28
ChildPugh class A
32
B
2
C tumor size
FFPE Protein Extraction, iTRAQ Labeling, and 2DLCESI MS/MS
The experimental design of this section is illustrated in Figure 1. LC, DN, and sHCC lesions were marked under pathologists' review (HE sections were referenced) and exactly dissected microscopically after xylene deparaffinization. FFPE proteins were extracted as follows: Dissected tissue sections were placed directly into a LoBind Tube (Eppendorf, Germany) containing buffers of 2% SDS and 20 mM Tris-HCl at pH 9. The samples were heated at 100 C for 20 min and then at 80 C for 2 h with shaking. After incubation, the tube at was kept at 4 C for 5 min. The protease inhibitor mixture (complete EDTA Free, Roche) was added, and the samples were removed into UltrafreeMC (Durapore PVDF 0.45 μm, 0.5 mL, Millipore, USA), centrifuged at 12,000g at 4 C for 15 min, quantified by the BCA method, and stored at 80 C until needed. The extracted proteins from LC, DN, and sHCC were pooled separately (18 samples for each group of LC, DN, and sHCC). For each sample, proteins were precipitated with isopropanol, and pellets were redissolved in the dissolution buffer (0.5 M triethylammonium bicarbonate, 0.1% SDS). Then proteins were quantified by BCA protein assay, and 100 μg of protein was denatured, alkylated, and digested. Proteins were labeled with the iTRAQ tags as follows: LC-115 isobaric tag, DN-116 isobaric tag, sHCC-117 isobaric tag. The labeled samples were combined, desalted with Sep-Pak Vac C18 cartridge 1 cm3/50 mg (Waters, USA), and fractionated by using a Shimazu UFLC system (Shimazu, Japan) connected to a strong cation exchange (SCX) column (polysulfethyl column, 2.1 mm 100 mm, 5 μm, 200 Å, The Nest Group, Inc. USA). SCX separation was
LC
59
1
e2 cm
24
2.13 cm
45
BCLC stage 0 (very early)
24
A (early)
45
tumor differentiation well moderate
32 37
TNM I
57
II
2
a
HBsAg, hepatitis B virus surface antigen; HCV-Ab, hepatitis C virus antibody; SD, standard deviation; BCLC, Barcelona Clinic Liver Cancer; TNM, UICC TNM classification. The histologic grade of tumor differentiation was assigned by the Edmondson grading system.
performed using a linear binary gradient of 045% buffer B (350 mM KCl, 10 mM KH2PO4 in 25% ACN, pH 2.6) in buffer A (10 mM KH2PO4 in 25% ACN, pH 2.6) at a flow rate of 200 μL/min for 90 min, and 30 fractions were collected every 3 min. Each fraction was dried down and redissolved in buffer C (5% (v/v) acetonitrile and 0.1% formic acid solution), and the fractions with high KCl concentration were desalted with PepClean C-18 spin Column (Pierce, USA). All SCX fractions were analyzed 3 times using a QSTAR XL LCMS/MS system (Applied Biosystems, USA) and RPLC column (ZORBAX 300SB-C18 column, 5 μm, 300 Å, 0.1 mm 15 mm, Microm, Auburn, CA). The RPLC gradient was 5% to 35% buffer D (95% acetonitrile, 0.1% formic acid) in buffer C at a flow rate of 0.3 μL/min in 120 min. 3419
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Figure 1. Flowchart for the FFPE protein extraction, iTRAQ labeling, and 2DLCESIMS/MS. LC, DN, and sHCC lesions were exactly dissected microscopically after xylene deparaffinization (A). After retrieval reaction, proteins extracted from LC, DN, and sHCC were quantified and pooled, respectively (B). Then, protein was precipitated, redissolved, and quantified by BCA protein assay. Next, 100 μg of protein was denatured, alkylated, and digested. Protein was labeled with the iTRAQ tags as follows: LC-115 isobaric tag, DN-116 isobaric tag, sHCC-117 isobaric tag. Then three group of samples were mixed and separated by strong cation exchange (SCX) column, and all SCX fractions were analyzed using RPLC column and QSTAR XL LCMS/MS system.
The Q-TOF instrument was operated in positive ion mode with ion spray voltage typically maintained at 2.0 kV. Mass spectra of iTRAQ labeled samples were acquired in an information-dependent acquisition mode. The analytical cycle consisted of a MS survey scan (4002000 m/z) followed by 5-s MS/MS scans (502000) of the 5 most abundant peaks (i.e., precursor ions), which were selected from the MS survey scan. Precursor ion selection was based upon ion intensity (peptide signal intensity above 25 counts/s) and charge state (2+ to 4+), and once the ions were fragmented in the MS/MS scan, they were allowed one repetition before a dynamic exclusion for a period of 120 s. Because of the iTRAQ tags, the parameters for rolling collision energy (automatically set according to the precursor
m/z and charge state) were manually optimized. Under CID, iTRAQ-labeled peptides fragmented to produce reporter ions at 115.1, 116.1, and 117.1, and fragment ions of the peptides were simultaneously produced, resulting in sequencing of the labeled peptides and identification of the corresponding proteins. The ratios of the peak areas of the three iTRAQ reporter ions reflected the relative abundances of the peptides and the proteins in the samples. Calibration of mass spectrometer was carried out using BSA tryptic peptides. Database Searching and Criteria
Protein identification and quantification for the iTRAQ experiment was performed with the ProteinPilot software 3420
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version 3.0 (Applied Biosystems, USA). The Paragon Algorithm in ProteinPilot software was used for peptide identification and isoform specific quantification. The data search parameters are shown in Supplementary Table S1. To minimize false positive results, a strict cutoff for protein identification was applied with the unused ProtScore g1.3, which corresponds to a confidence limit of 95%, and at least two peptides with the 95% confidence were considered for protein quantification. The resulting data set was auto biascorrected to get rid of any variations imparted due to unequal mixing during combining different labeled samples. For iTRAQ quantitation, the peptide for quantification was automatically selected by Pro Group algorithm (at least one peptide with 99% confidence) to calculate the reporter peak area, error factor (EF), and p-value.
scored as negative, weak, moderate, and strong, and cell staining was classified on the basis of the percentage of positive cells: 0 (0% to15%), 1 (15% to 25%), 2 (25% to 50%), 3 (>50%). The final score of each sample for ACY1 (cytoplasmic staining), PGM1 (nucleo-cytoplasmic staining), SQSTM1 (cytoplasmic staining), SUOX (cytoplasmic staining), and PYGL (cytoplasmic staining) were generated summarizing the results of intensity and percentage of positive cells. The immunostaining was considered negative if the final score was 0 (), and positive if the final score was 1 (+), 2 (++), or 3 (+++). For CD34, negative and positive defined as reported previously24,25 with minor modification and defined as follows: cases showing staining of no or only a few sinusoids were defined as negative, and those showing diffuse staining of sinusoidal endothelium throughout the lesion area were regarded as positive (Supplementary Figure S1).
Western Blot Analysis
Construction of a Diagnostic Model
The pooled samples used in the iTRAQ experiment that contained 20 μg of total proteins were separated by 10% (w/v) SDS-PAGE and transferred onto a PVDF membrane (Millipore, USA). After 1 h of incubation with blocking buffer (5% (w/v) nonfat milk in TBS-T (0.05% (v/v) Tween 20 in Tris-buffered saline)), the membrane was probed with the indicated primary antibodies diluted in blocking buffer overnight at 4 C. After being extensively washed with TBS-T, the membrane was incubated with horseradish peroxidase-conjugated antibody to mouse or rabbit diluted in blocking buffer (1:20,000) for 1 h at room temperature. Bands were visualized by treating the membrane with ECL Western blotting detection reagents (Pierce, USA) and exposing it to X-ray film (Kodak, Japan). Finally, the visualized bands were quantified by QUANTITY ONE software (Bio-Rad, USA). Primary antibodies used in Western blot are shown in Supplementary Table S2.
DN (30 cases) and sHCC (51 cases) scores from immunohistochemistry were used in diagnostic model construction. A forward stepwise method was utilized as an exploratory purpose to determine which markers were to be added or dropped from the logistic regression model. The scores (positive as 1, negative as 0) of ACY1, PGM1, SQSTM1, SUOX, PYGL, and CD34 were subjected to logistic regression to generate a differential diagnostic model for detection of sHCC. The output was the diagnostic score in the range of 01. During model construcion, the diagnostic score of a DN lesion was defined as “0”, whereas that of a sHCC lesion was defined as “1”. The predictive probability of this model was applied to the same data set (30 cases of DN and 51 cases of sHCC), and the ROC analysis was performed.
Tissue Microarrays and Immunohistochemistry and Scoring
For the construction of tissue microarrays, 146 specimens (65 LC, 30 DN, and 51 sHCC) were used, and tissue microarrays were constructed as reported previously.21,22 The hematoxylin and eosin (HE) stained slides from all patients were reviewed and identified by two experienced pathologists (W.-M.C., H.D.). Next, the representative LC, DN, and sHCC lesions were premarked in the paraffin blocks. Tissue cylinders with a diameter of 2 mm were punched from the marked areas of each block and incorporated into a recipient paraffin block. Thus tissue microarray blocks were constructed and each contained LC, DN, and sHCC. Sections 4 μm thick were placed on slides coated with 3-aminopropyltriethoxysilane. Paraffin sections were deparaffinized in xylene and rehydrated by reducing the concentration of ethanol (100%, 95%, and 85%, 5 min each). Antigens were unmasked with microwave irradiation for 3 min in pH 6.0 citric buffer and cooled at room temperature for 60 min. Endogenous peroxidase activity was blocked by incubation of the slides in 3% H2O2/PBS, and nonspecific binding sites were blocked with goat serum. Primary antibodies used in immunohistochemistry are shown in Supplementary Table S2. Next, an EnVision Detection kit (GK500705,: Gene Tech, China) was used to visualize tissue antigens. Tissue sections were counterstained with hematoxylin for 5 min. Negative control slides omitting the primary antibodies were performed in all assays. The integrated optical density (IOD) as density of positive staining was measured as reported previously.23 In addition, cases were semiquantitatively evaluated by two pathologists (W.-M.C., H.D.). Intensity of staining was
Statistical Analysis
Statistical analyses were carried out with SPSS 13.0 software (SPSS, Chicago, IL). Pearson X2 test or X2 test with continuity correction and Fisher’s exact test were used to compare qualitative variables. Quantitative variables were analyzed by Student's t test or MannWhitney test. Experimental data were presented as the mean of each condition ( SD, and p < 0.05 was considered statistically significant. Receiver operating characteristic (ROC) curves were used to determine the diagnostic values of the markers.
’ RESULTS iTRAQ2DLCESIMS/MS Based Quantitative Proteome Analysis and Screening of Potential Candidate Proteins by Western Blot
For enhancement of protein coverage, three replicates were performed on RPLCESIMS/MS. According to the criteria for protein quantification mentioned in the Database Searching and Criteria section, 940, 1330, and 1285 proteins were quantified by each technical replicate, and finally, a total of 1951 proteins were quantified (union of run 1 + run 2 + run 3) (Figure 2). Detailed information of peptides and proteins identified and quantified for the three individual RPLCMS/ MS runs are provided in Supplementary Tables S3S5 (peptides) and Supplementary Tables S7S9 (proteins). In addition, the labeling efficiency of run 1, run 2, and run 3 was 0.99, 0.98, and 0.98, respectively (Supplementary Table S6). In the present quantitative proteomics study, 81.5% (1366/ 1677), 87.4% (1782/2038), and 86.7% (1681/1939) of identified proteins were quantified (Supplementary Table S10). To evaluate technical reproducibility, linear regression analyses were performed on Log10-transformed 116/117 ratios of three 3421
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Table 2. Selected Potential Biomarker Candidates and Their Expression Levels of Each Run Quantified by iTRAQ Analysis LC/sHCC(115/117)
DN/sHCC (116/117)
run 1 run 2 run 3 mean run 1 run 2 run 3 mean Decreased in sHCC PGM1
7.4
2.9
12.1
7.5
4.1
18.4
12.0
GPD1 UGP2 Isoform 1
3.5
4.1 8.4
3.7
4.1 5.2
9.6 5.8 14.1
7.4
9.6 9.1
BHMT
2.9
4.7
5.3
2.1
3.4
2.8
3.6
7.4
PYGL
4.5
2.3
3.4
6.7
3.8
GLYAT
3.0
3.0
4.5
ACO1
3.4
2.9
4.4
SUOX
3.1
3.1
4.3
2.7
3.9
MTHFD1
2.7
ACY1
2.2
2.4
2.5
2.0
2.2
3.0
3.5
5.2 4.5
3.4
3.9 4.3 3.9
2.7
3.1
0.03
0.03
Increased in sHCC
Figure 2. Venn diagram depicting the overlap of proteins quantified in three replicates. The number in parentheses indicates the number of quantified proteins in each run, and a total of 1951 proteins were quantified (union of run 1 + run 2 + run 3).
SQSTM1
0.07 0.07
DPP4
0.21
0.17 0.19
0.23
UBA1
0.60
0.60
0.59
CAPRIN1
analyses of run 1, run 2, and run 3 (Supplementary Figure S3). Pearson correlation coefficients between runs 1 and 2, runs 1 and 3, and runs 2 and 3 were 0.784, 0.751, and 0.864, respectively (p < 0.0001). Thus the ratio of the three replicates analyses were significantly positively correlated, indicating the good technical reproducibility of the RPLCMS/MS analyses. For normalization of iTRAQ ratios derived from three replicates, the 115/117 and 116/117 ratios of a set of the most widely used internal controls detected in the present iTRAQ experiments, including β-tubulin, β-actin, and GAPDH, were particularly assessed. As shown in Supplementary Table S11, the 115/ 117 and 116/117 ratios of these proteins were mainly around 1.0; for example, the average 116/117 ratio of GAPDH was 1.05, and the corresponding SD was 19%. So we chose autobias in the present study, and the ratios of each analysis were automatically normalized by the ProteinPilot software. Finally, 1951 of quantified proteins were subsequently filtered with manually selected filter exclusion parameters (p < 0.05 and expression level differed at least 1.25- or 0.8-fold in both LC and DN compared to sHCC). Thus 147 proteins were screened out (Supplementary Table S12), including 52 proteins upregulated in sHCC and 95 proteins downregulated in sHCC. As shown in Table 2, we selected 14 proteins as potential biomarker candidates, i.e., PGM1, GPD1 (glycerol-3-phosphate dehydrogenase), UGP2 (UTPglucose-1-phosphate 2), BHMT (betainehomocysteine S-methyltransferase 1), PYGL, GLYAT (isoform 1 of glycine N-acyltransferase), ACO1 (aconitate hydratase), SUOX, MTHFD1 (cDNA FLJ56016, highly similar to C-1-tetrahydrofolate synthase), ACY1, SQSTM1, DPP4 (dipeptidyl peptidase 4), UBA1 (ubiquitin-like modifier-activating enzyme 1), and CAPRIN1 (CAPRIN1 isoform 1 of Caprin-1), which were never reported in sHCC or the protein expression levels in sHCC have not been studied in detail. The representative peptide MS/MS spectrum of ACY1 is shown in Figure 3. Among 14 proteins, only 7 protein expressions were confirmed by Western blot and showed differential expression patterns (Figure 4). Another 7 protein expression levels could not be confirmed by Western blot, probably due to antigenantibody reactivity.
13.7
0.20 0.20
0.21
0.22 0.59
0.48
0.48
Verification of the Candidate Biomarkers for Classification of DN and sHCC
Immunohistochemical verifications of 5 proteins in LC, DN, and sHCC lesions are shown in Figure 5. The upper panel shows the HE staining (AC), the next are immunostaining of ACY1 (DF), PGM1 (GI), SQSTM1 (JL), SUOX (MO), and PYGL (PR), respectively. Expression levels of MTHFD1 and DPP4 were not changed significantly in sHCC compared to DN (Supplementary Figure S2). As shown in Figure 6, the expression levels of ACY1 (A), PGM1 (B), SUOX (D), and PYGL (E) in LC and DN were significantly higher than those in sHCC, and SQSTM1 (C) was significantly higher in sHCC than LC and DN. Correlations between ACY1, PGM1, SQSTM1, SUOX, and PYGL Expressions and Clinicopathological Features
To elucidate the biologic significance, we correlated the PGM1, ACY1, SQSTM1, SUOX, and PYGL expression levels with the clinicopathologic features of DN and sHCC. As shown in Table 3, ACY1, SQSTM1, SUOX, and PYGL expression levels did not correlate with age, sex, cirrhosis, HBsAg, HCV-Ab, CD34, tumor size, BCLC,26 and TNM staging.27 PGM1 expression was associated with serum AFP (p = 0.02) but did not correlate with any other clinicopathologic features. Diagnostic Value of the Diagnostic Model
Forward stepwise multivariate regression analysis of the immunohistochemisty data (30 DN and 51 sHCC cases) indicated that PGM1, SUOX, and PYGL were dropped from the logistic regression model and the CD34, ACY1, and SQSTM1 were included in the resulted diagnostic model as below (e is the mathematical constant and base value of natural logarithms): P ¼
e18:580 + 20:607 3 CD34 3:902 3 ACY1 + 21:180 3 SQSTM1 1 + e18:580 + 20:607 3 CD34 3:902 3 ACY1 + 21:180 3 SQSTM1
Odds ratios for CD34, ACY1, and SQSTM1 in the model were 9E+008, 0.020 and 2E+009, respectively. The area under the 3422
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Figure 3. Representative MS/MS spectrum showing the peptide of ACY1 protein. The ion assignments are as follows: 115-LC, 116-DN, 117-sHCC. The intensity of repots ions from precursor peptides indicates protein expression levels (A). The MS/MS spectra show identified peptide sequences with GPEEEHPSVTLFR leading to the identification of aminoacylase-1 (ACY1) (B).
Figure 4. Western blot analyses. Seven candidate proteins were confirmed and compared expression levels between LC, DN, and sHCC by Western blot analysis. The protein expression levels were normalized with GAPDH.
curve (AUC) of model of the ROC curve of the diagnostic score was 0.991 (95% CI, 0.9761.005, p < 0.000, cutoff value was 0.5487). It was much higher than the AUC of ACY1 (0.869, 95% Cl, 0.7850.952, p < 0.000), PGM1 (0.782, 95% Cl, 0.6750.890, p < 0.000), SUOX (0.637, 95% Cl, 0.5120.763, p = 0.040), PYGL (0.659, 95% Cl, 0.5390.778, p = 0.017), SQSTM1 (0.855, 95% Cl, 0.7630.946, p < 0.000), and CD34 (0.864, 95% Cl, 0.7670.960, p < 0.000) alone (Figure 7). Sensitivity, Specificity, and Positive and Negative Predictive Values for sHCC Detection Using Individual Markers and CD34, ACY1, and SQSTM1 Combination
The sensitivity, specificity, and positive and negative predictive values for sHCC detection of the individual markers and CD34,
ACY1, SQSTM1 combination are summarized in Table 4. A high sensitivity (96.1%) coupled to lower specificity (76.7%) for sHCC detection was seen for CD34. The sensitivity and specificity of ACY1 (), PGM1(), SQSTM1 (+), SUOX (), and PYGL () for detection of sHCC were 80.4% and 93.3%, 76.5% and 80.0%, 84.3% and 86.7%, 60.8% and 66.7%, 45.1% and 86.7%, respectively. However, sensitivity and specificity of the model (CD34, ACY1, SQSTM1 combination) was 96.1% and 96.7%. In addition, positive and negative predictive values for detection sHCC were also improved significantly by the combination of CD34, ACY1, SQSTM1. Afterward, we combined ACY1 and SQSTM1 by logistic regression analysis to distinguished sHCC and DN. However, as shown in Table 4, a lower specificity (80.0%) for distinguishing sHCC from DN was seen using ACY1 and SQSTM1 combination.
’ DISCUSSION Distinguishing sHCC from other types of small focal lesions (especially DN) that occur in human chronic liver disease is sometimes difficult on the basis of morphologic features alone. DNs are macroscopically recognizable precursor lesions of HCC and HGDN with a high risk of malignant transformation.28,29 Molecular studies on LC, DN, and HCC may provide much information on the genetic mechanisms involved in the transition from severe dysplasia to malignancy. Studies on the proteomic alterations of these precancerous lesions may eventually lead to new therapeutic strategies. Clinicopathological studies are also important in order to determine optimal management of patients with a precancerous lesion. Although various imaging techniques (ultrasonography, computed tomography scanning, magneticresonance imaging) are 3423
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Figure 5. Representative immunohistochemical staining for five candidate proteins (200). Typical hematoxylin and eosin (HE) stained sections in LC (A), DN (B), and sHCC (C). Typical immunostaining for ACY1 in LC (D), DN (E), and sHCC (F). Typical immunostaining for PGM1 in LC (G), DN (H), and sHCC (I). Typical immunostaining for SQSTM1 in LC (J), DN (K), and sHCC (L). Typical immunostaining for SUOX in LC (M), DN (N), and sHCC (O). Typical immunostaining for SUOX in LC (P), DN (Q), and sHCC (R). ACY1, PGM1, SUOX, and PYGL showed low expression in sHCC, and SQSTM1 showed high expression in sHCC.
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used in the detection of HCC, diagnosis of small lesions or precancerous lesions is relatively inaccurate.30,31 Moreover, detection of DN and correct differentiation from sHCC are sometimes difficult. Especially, focusing on the size of the lesions, these studies show that most DN and sHCC cannot be differentiated by the three above-mentioned imaging techniques; HCC is sometimes diagnosed as DN and vice versa.3235 It has been reported that CD31, R-SMA, HSP70, glypican 3, GS, and CD34 could serve as molecular markers for HCC.7,25,3638 However, their sensitivity and specificity are still far from satisfactory. Thus, suitable biochemical markers to differentiate sHCC from precancerous lesions are intensively needed especially in confusing cases. In this report, a iTRAQ2DLCESIMS/MS technique was used to compare differential protein profiles from LC, DN, and sHCC specimens. As results, a total of 1951 proteins were quantified, including 147 proteins differentially expressed by at least 1.25-fold. Ninety-five of them were downregulated, and 52 were upregulated in sHCC. To the best of our knowledge, the number of quantified proteins in our study is one of the highest in published iTRAQ2DLCESIMS/MS experiments with FFPE tissues. It has been reported that pooling individual samples together was a good strategy to offset individual variations. When more than 7 individual samples were pooled together, the individual variations were not obvious to the whole pool.39 Liu et al. also using pooled samples (by pooling 10 samples for each group) when performing Western blotting analysis.40 On the other hand, because of limited sample source and the large number of potential candidate proteins (14 proteins), we applied Western blot validation using pooled samples (18 individual samples for each group) to screen out these candidate proteins. For further validation, we constructed tissue microarrays consisting of 146 specimens (65 LC, 30 DN, and 51 sHCC) and validated individual expression of candidate proteins by immunohistochemistry. Finally, we narrowed down our range of interesting candidates to ACY1, PGM1, SQSTM1, SUOX, and PYGL for distinguishing a diagnosis of sHCC from DN by immunohistochemical assay. Furthermore, expression levels of ACY1, PGM1, SQSTM1,
Figure 6. Immunohistochemical expression of ACY1 (A), PGM1 (B), SQSTM1 (C), SUOX (D), and PYGL (E). A box and whisker plot (whiskers: 1090%) of integrated optical density (IOD) for the each marker from the tissue microarrays. MannWhitney test showed a significant statistical difference LC (65 lesions) and DN (30 lesions) compared to sHCC (51 lesions). ** p = 0.0011, LC vs sHCC; *** p < 0.0001, LC vs sHCC; ### p < 0.0001, DN vs sHCC. 3424
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3425
0
2
2
0.688 (0.0696.862)
2
4
0.579 (0.1222.753)
1
0.308 (0.0214.557)
a
0
II (T2N0M0)
0
1
1.000d
0.136b
0.136b
0.508b
0.020b
0.960b
1.000d
0.873b
1.000b
0.593b
0.371
c
OR
SQSTM1
0
-
0
1 1.025 (0.1377.642)
0
1
0
35 7 1.029 (0.9731.087)
27 4 0.796 (0.3841.653)
16 4 1.344 (0.6072.974)
27 4 0.796 (0.3841.653)
16 4 1.344 (0.6072.974)
16 3 1.008 (0.3802.672)
27 5 0.995 (0.5551.785)
17 2 0.618 (0.1762.167)
25 6 1.260 (0.7862.019)
2
41 8 1.049 (0.9821.120)
37 8 1.054 (0.9801.134)
2
5
36 7 0.997 (0.7491.326)
3
33 7 1.091 (0.9891.204)
35 7 1.075 (0.7981.449) 8 1 0.672 (0.0974.662)
23 4 0.935 (0.4431.973)
20 4 1.075 (0.5012.307)
p value Pa Na
P: positive,N: negative. b Continuity Correction. c Pearson X2 test. d Fisher’s exact test.
1
9 33 0.971 (0.9151.029)
I (T1N0M0)
11 31 0.969 (09101.031)
10 21 0.646 (0.4400.950)
7 24 0.836 (0.5171.352)
TNM (n = 43)
1.000d
2 18 2.769 (0.74710.261)
3 17 1.382 (0.5013.811)
A (early)
10 21 0.646 (0.4400.950)
7 24 0.836 (0.5171.352)
2.13 cm
BCLC (n = 51) 0 (very early)
3 16 1.641 (0.5744.688)
9 23 0.786 (0.5171.195)
8 11 0.388 (0.2090.719)
3 28 2.632 (0.9837.048)
1
11 38 1.063 (0.8901.270)
10 35 0.946 (0.8761.022)
0
2
9 34 1.094 (0.8111.4175)
1
10 30 1.031 (0.8381.269)
11 31 0.867 (0.6871.095) 1 8 2.462 (0.34117.750)
5 22 1.354 (0.6562.793)
7 17 0.747 (0.4111.357)
OR
2 18 2.769 (0.74710.261) 0.761b
0.761b
0.371b
0.382c
0.845b
1.000d
1.000b
1.000b
1.000b
0.884
b
p value Pa Na
PGM1
3 17 1.382 (0.5013.811)
e2 cm
tumor size (n = 51)
8 24 0.732 (0.4891.095)
2 17 2.073 (0.5707.545)
well
moderate
differentiation (n = 51)
5 26 1.300 (0.6722.516)
5 14 0.700 (0.3311.482)
>20
e20 tumor
AFP (ng/mL) (n = 50)
1 0.244 (0.0173.572)
9 40 1.084 (0.8771.340)
1
positive
10 35 0.946 (0.8761.022)
negative
CD34 (n = 51)
negative
HCV-Ab (n = 47) positive
5 1.282 (0.1689.777)
9 34 0.969 (0.7631.230)
1
positive
negative
HBsAg (n = 49)
2 0.529 (0.0545.203)
8 32 1.059 (0.8281.354)
1
yes
8 34 1.037 (0.7381.456) 2 7 0.854 (0.2083.501)
no
cirrhosis (n = 43)
male female
sex (n = 51)
4 20 1.220 (0.5362.773)
6 21 0.854 (0.4741.536)
OR
e52
Pa Na
>52
age, mean years (n = 51)
variable
ACY1
sHCC
1.000d
0.775b
0.775b
1.000b
0.668b
1.000d
1.000d
1.000b
1.000d
1.000b
1.000
b
OR
SUOX
0
-
1.379 (0.2796.824)
1 0.741 (0.04911.142)
4
1
0.645 (0.0439.737)
11
0.279c
0.146c
0.405c
1.000b
1.000b
1.000b
0.066d
1.000b
0.166
c
0
1
18 24 0.960 (0.8861.040)
14 17 0.783 (0.5101.204) 1.000d
OR
PYGL
1 0.632 (0.0626.451)
0
2 0.614 (0.1243.043)
0
0
1
24 18 0.947 (0.8521.053)
17 14 1.003 (0.6441.561)
9 0.996 (0.5011.980)
17 14 1.003 (0.6441.561)
9 0.996 (0.5011.980)
8 11 1.674 (0.8113.455)
20 12 0.730 (0.4631.152)
11 18 0.854 (0.4151.756)
16 15 1.101 (0.7141.696)
2
26 23 1.077 (0.9721.193)
24 21 1.083 (0.9701.210)
2
4
23 20 1.067 (0.8691.311)
2
22 18 1.033 (0.8801.214)
23 19 1.006 (0.7791.298) 5 4 0.974 (0.2953.213)
15 12 0.974 (0.5781.641)
13 11 1.030 (0.5751.846)
p value Pa Na
0.279c 6 14 1.505 (0.6943.263) 11
14 17 0.783 (0.5101.204)
6 14 1.505 (0.6943.263)
5 14 1.806 (0.7704.237)
15 17 0.731 (0.4861.099)
9 10 0.741 (0.3681.493)
11 20 1.212 (0.7571.940)
1
19 30 1.019 (0.9041.148)
19 26 1.014 (0.8951.148)
1
2
18 25 0.958 (0.7791.177)
3
15 25 1.200 (0.9761.475)
16 26 1.408 (0.8621.371) 4 5 0.866 (0.2462.647)
13 14 0.695 (0.4201.050)
7 17 1.567 (0.7963.084)
p value Pa Na
Table 3. Relationship between ACY1, PGM1, SQSTM1, SUOX, and PYGL and Clinicopathologic Features in sHCC
0.442d
0.991c
0.991c
0.157c
0.665c
0.495d
0.495d
0.865b
1.000b
1.000b
0.921c
p value
Journal of Proteome Research ARTICLE
dx.doi.org/10.1021/pr200482t |J. Proteome Res. 2011, 10, 3418–3428
Journal of Proteome Research
ARTICLE
Figure 7. Receiver operation characteristics (ROC) curve analysis of aminoacylase-1 (ACY1), phosphoglucomutase-1 (PGM1), CD34, sequestosome-1 (SQSTM1), sulfite oxidase (SUOX), glycogen phosphorylase (PYGL) and CD34, ACY1, SQSTM1 combination to discriminate sHCC form DN lesions. The areas under the curve (AUC) were 0.869 for ACY1, 0.782 for PGM1, 0.637 for SUOX, 0.659 for PYGL, 0.855 for SQSTM1, 0.864 for CD34, and 0.991 for CD34, ACY1 and SQSTM1 combination.
Table 4. Sensitivity, Specificity, and Positive and Negative Predictive Values for sHCC Detection Using Individual Markers; CD34 and SQSTM1 Combination; and CD34, ACY1, and SQSTM1 Combination sHCC DN sensitivity specificity PPVa NPVb (%) (%) (n = 51) (n = 30) (%) (%)
and acyl groups.42 ACY1 has been assigned to chromosome 3p21.1, a region reduced to homozygosity in small cell lung cancer (SCLC), and its expression has been reported to be reduced or undetectable in SCLC cell lines and tumors.43 SQSTM1 is an adapter protein that binds ubiquitin and may regulate signaling cascades through ubiquitination. It may regulate the activation of NFKB1 by TNF-R, nerve growth factor (NGF), and interleukin1.4446 Recently, it has been reported that SQSTM1 is overexpressed in breast tumors and regulated by prostate-derived Ets factor in breast cancer cells.47 In our study, it was indicated that ACY1 and SQSTM1 could be adopted as good immunohistochemical biomarkers for distinguishing sHCC from DN and these two markers in combination with CD34 may significantly improve the lower specificity of CD34 for detection of sHCC. In summary, we conclude that ACY1 and SQSTM1 are novel immunohistochemical biomarkers for classification of DN and sHCC. The diagnostic model composed by CD34, ACY1 and SQSTM1 is very useful in distinguishing sHCC from DN.
’ ASSOCIATED CONTENT
bS
Supporting Information This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*(Y.-K.L.) Tel: +86-21-54237962. Fax: +86-21-54237959. E-mail:
[email protected]. (W.-M.C.) Tel: +86-2181875191. Fax: +86-21-81875191. E-mail:
[email protected].
Indivdual Marker CD34 positive
49
7
96.1
76.7
87.5
92.0
ACY1 negative
41
2
80.4
93.3
95.3
73.7
PGM1 negative
39
6
76.5
80.0
86.7
66.7
SQSTM1 positive
43
4
84.3
86.7
91.5
76.5
SUOX negative
31
10
60.8
66.7
75.6
50.0
PYGL negative
23
4
45.1
86.7
85.2
48.1
80.0
89.3
96.0
98.0
93.5
ACY1, SQSTM1 Combination ACY1 + SQSTM1
50
6
98.0
CD34, ACY1, SQSTM1 Combination predicted by model a
49
1
96.1
96.7
Positive predictive values. b Negative predictive values.
SUOX, and PYGL were not correlated with age, sex, cirrhosis, HBsAg, HCV-Ab, CD34, tumor size, BCLC, and TNM staging, with the exception of PGM1 expression level, which was associated with serum AFP. Moreover, from the results of our iTRAQ2DLC-MS/MS analyses, we also identified that SULT1A1 might be a useful biomarker for the detection of HCC, and similar results about SULT1A1 have been reported.41 Receiver operation characteristics (ROC) curves and the area under the curve (AUC) were used to indicate the predictive values of biomarkers. In our results, the AUC of CD34, ACY1, PGM1, SQSTM1, SUOX, and PYGL were 0.854, 0.869, 0.782, 0.855, 0.637, and 0.659, respectively. However, the constructed logistic regression model containing CD34, ACY1, and SQSTM1 resulted in a significantly improved AUC of 0.991. ACY1 is a cytosolic, homodimeric, zinc-binding enzyme that catalyzes the hydrolysis of acylated L-amino acids to L-amino acids
Author Contributions #
These two authors contributed equally to this work.
’ ACKNOWLEDGMENT We thank our patients for their willingness to take part in this study. This work was financially supported by China National HighTech Research and Development Program (2006AA02A308), China National Key Projects for Infectious Disease (2008ZX 10002-021 and 2008ZX10002-017) and the Major State Basic Research Development Program of China (973 Program) (2011CB910604). ’ REFERENCES (1) Parkin, D. M.; Bray, F.; Ferlay, J.; Pisani, P. Global cancer statistics, 2002. CA Cancer J. Clin. 2005, 55 (2), 74–108. (2) Bolondi, L.; Gaiani, S.; Celli, N.; Golfieri, R.; Grigioni, W. F.; Leoni, S.; Venturi, A. M.; Piscaglia, F. Characterization of small nodules in cirrhosis by assessment of vascularity: The problem of hypovascular hepatocellular carcinoma. Hepatology 2005, 42 (1), 27–34. (3) Krinsky, G. Imaging of dysplastic nodules and small hepatocellular carcinomas: experience with explanted livers. Intervirology 2004, 47 (35), 191–8. (4) Takayama, T.; Makuuchi, M.; Hirohashi, S.; Sakamoto, M.; Yamamoto, J.; Shimada, K.; Kosuge, T.; Okada, S.; Takayasu, K.; Yamasaki, S. Early hepatocellular carcinoma as an entity with a high rate of surgical cure. Hepatology 1998, 28 (5), 1241–6. (5) Lencioni, R.; Cioni, D.; Crocetti, L.; Franchini, C.; Pina, C. D.; Lera, J.; Bartolozzi, C. Early-stage hepatocellular carcinoma in patients with cirrhosis: long-term results of percutaneous image-guided radiofrequency ablation. Radiology 2005, 234 (3), 961–7. 3426
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