- Open Access
Detection and identification of NAP-2 as a biomarker in hepatitis B-related hepatocellular carcinoma by proteomic approach
© He et al; licensee BioMed Central Ltd. 2008
- Received: 19 August 2007
- Accepted: 10 March 2008
- Published: 10 March 2008
A lack of sensitive and specific biomarkers is a major reason for the high rate of Primary hepatocellular carcinoma (HCC)-related mortality. The aim of this study was to investigate potential proteomic biomarkers specific for HCC.
81 patients with hepatitis B-related HCC and 33 healthy controls were randomly divided into a training set (33 HCC, 33 controls) and a testing set (48 HCC, 33 controls). Serum proteomic profiles were measured using Surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS).) A classification tree was established by Biomarker Pattern Software (BPS). Candidate SELDI peaks were isolated by tricine-SDS-PAGE, identified by HPLC-MS/MS and validated by immunohistochemistry (IHC) in liver tissues.
A total of 6 proteomic peaks (3157.33 m/z, 4177.02 m/z, 4284.79 m/z, 4300.80 m/z, 7789.87 m/z, and 7984.14 m/z) were chosen by BPS to establish a classification tree with the highest discriminatory power in the training set. The sensitivity and specificity of this classification tree were 95.92%, and 100% respectively in the testing set. A candidate marker of about 7984 m/z was isolated and identified as neutrophil-activating peptide 2 (NAP-2). IHC staining showed that NAP-2 signals were positive in HCC tissues but negative in adjacent tissues.
The NAP-2 may be a specific proteomic biomarker of hepatitis B-related HCC.
- Classification Tree
- Protein Peak
- ProteinChip Array
- Adjacent Liver Tissue
- Liver Tumor Tissue
Primary hepatocellular carcinoma (HCC) is one of the most lethal malignancies worldwide. It is the third leading cause of cancer death in China and the sixth most common cancer in the world [1, 2]. Prognosis of HCC remains poor, mainly due to the failure of early diagnosis of the disease in symptom-free patients [3, 4]. In contrast, early detection of HCC before the onset of clinical symptoms can lead to curative treatment, significantly improving prognosis [5, 6].
At present, alpha-fetoprotein (AFP) and des-γ-carboxy prothrombin (DCP) are the two most widely used tests to aid in the diagnosis and monitoring for HCC. However, AFP is found to be normal in around one third of patients with small (<3 cm) HCC , and the specificity of AFP is only about 68.2% in detecting the patients with HCC . Although DCP was believed to be a better marker for diagnosis of HCC, elevated DCP activity is only present in 44–47% of HCCs less than 3 cm in size [9, 10]. Thus, both AFT and DCP are not ideal biomarkers for early diagnosis of HCC. There is a pressing need to find new biomarkers for more effective detection of HCC.
Recent advances in proteomic analysis have offered exciting opportunities for finding novel biomarkers in biological fluids. Today, two major strategies are used to discover clinically useful biomarkers from a proteomic approach. One of them utilizes a SELDI-TOF system [11–14], and the other is a method based on 2-D PAGE coupled with MS [15, 16]. The SELDI-TOF, which generates the protein patterns by MS, has been considered a powerful tool for the discovery of new biomarkers [16, 17]. However, almost all of the proteins' peaks detected by SELDI are not easily identified as protein molecules. Thus, these findings cannot provide any information about the biological roles of the marked proteins in the pathogenesis of a disease. On the other hand, the 2-D PAGE-based method provides a lot of information about proteins, including expression volumes, actual p/s and molecular weights . However, the 2-D method is not sensitive for resolving hydrophobic, low abundant, low molecular weight proteins. LC-MS/MS is a powerful identification tool for proteins, but it is not suitable for analyzing proteins directly . It has been suggested that the combination of SELDI-TOF, 2-D and LC-MS/MS may provide a better solution to identify disease-associated proteomic biomarkers [18, 16, 19].
The aim of this study was to identify serum protein biomarkers in HCC. The analysis was performed using SELDI-TOF-MS technology to screen potential protein patterns specific for HCC. Candidate protein peaks were then separated by Tricine-SDS- PAGE and trypsin digestion, identified by HPLC-MS/MS analysis and database search, and confirmed by immunohistochemistry (IHC) in liver tissues.
2.1 Quality control of SELDI analysis
The reproducibility of SELDI spectra, i.e., mass location and intensity from array to array on a single chip (intra-assay) and between chips (inter-assay), was determined using the pooled normal serum quality control samples. Seven protein peaks in the range of 3,000–10,000 m/z on the observed spectra were randomly selected to calculate the coefficient of variance. The intra-assay and inter-assay coefficient of variance for peak location was 0.05%, and the intra-assay and inter-assay coefficients of variance for normalized intensity (peak height or relative concentration) were 6% and 14% respectively (data not shown). Masses that were within 0.08% mass accuracy between spectra were considered to be the same. Most importantly, it was observed that randomly selected samples, blinded to the person performing SELDI and rerun months or even a year later, were correctly classified by the decision tree classification algorithm.
2.2 Protein peak detection and data preprocessing
Initially, we analyzed serum samples from the training set, using the Ciphergen Biomarker pattern software. Among 126 qualified mass peaks (signal-to-noise ratio>5; ranging from 2 to 50 km/z), 21 protein peaks were over-expressed, whereas 44 protein peaks were significantly downregulated in sera from HCC patients compared with those from controls (all p values<0.05; Table 1). Fig. 1 shows an example of the 7984 m/z peak that was up-regulated in HCC samples vs. control samples.
Intensities of protein peaks between patients with HCC and controls
Expression of biomarkers
The intensities of six protein peaks of the classification tree in patients and controls
Expression of biomarkers
Important peaks selected by BPS
M/z of candidate peaks
Performance of the classification tree in the training and testing sets
2.3 Validation of the serum proteomic profiles in testing set
To evaluate the accuracy and validity of the classification tree generated from the training set, we determined the performance of the classification model in our testing dataset, consisting of 48 HCC and 33 of control samples. Consistent with the results in training set, this classification tree separated HCC samples from control samples with a sensitivity of 100%, a specificity of 96.97%, and a positive predictive value of 95.92%, respectively (Table 4). The area under the receiver operating characteristics (ROC) curve of this model was 0.986, indicating possible diagnostic utility.
2.4 Biomarker purification and identification
2.5 Immunohistochemistry of NAP-2
In this study, we generated serum protein mass spectra from hepatitis B-related HCC patients and controls. Based on the serum proteomic profiles, we constructed a classifier that accurately distinguishes HCC from controls. Validation of this proteomic classifier in a separate independent testing set showed high accuracy for discriminating HCC cases from controls. One of the proteins (7984 m/z) in the proteomic signature was identified as neutrophil-activating peptide 2 (NAP-2), which was further confirmed by IHC as a specific biomarker of hepatitis B-related HCC.
A major strength of this study is the use of a two-step workflow for proteomic biomarker screening. Several reports have determined that class prediction results should be corroborated in independent sample sets [20–22]. To translate from proteomic peaks to proteomic signatures for discriminating HCC patients from controls, we used BPS to select the best proteomic peaks for construction of classification tree . The classifier separated patients with HCC from healthy controls with a sensitivity of 100% and a specificity 93.94%. To confirm our findings, we subsequently applied our trained classifier to a second, independent test dataset, which was obtained by a rigorous standardized protocol similar to that of training dataset but included a greater proportion of HCC patients. In this new dataset, a classifier trained on the spectra obtained in the first phase of the study discriminated HCC cases from controls with a sensitivity of 100% and specificity of 96.97%. This percentage of correct classification was much higher than the currently accepted biomarkers, such as AFP (46% sensitivity and 89% specificity) . Only one in 48 total HCC samples was misclassified. The fact that the diagnostic performance of the training classifiers survived rigorous testing in testing dataset strengthens the conclusions of the study in the training dataset. Another strength of this study is the utilization of comprehensive proteomic techniques including protein profiling, Tricine-SDS-PAGE and HPLC-MS/MS for detection, identification, and IHC for characterization of NAP-2 as a biomarker in HCC.
Among the proteins identified by MS, one of the top three up-regulated proteins was characterized to be the neutrophil-activating peptide 2 (NAP-2). NAP-2 is a major form of CXCL7, a member of chemokine family involving in regulating immunity, angiogenesis, stem cell trafficking, and mediating organ-specific metastases of cancers . Recent studies using SELDI-based serum proteome profiling, in combination with immunoassays and Western blot analysis, have identified CXCL7 as a marker of advanced myelodysplastic syndromes (MDS), a hematologic stem cell malignancies in elderly patients [13, 25]. CXCL7 is translated as a propeptide, then cleaved to several smaller forms each reported to have specific functions. The shortest form, called NAP-2, is structurally related to NAP-1/IL-8, which was shown to be highly expressed in human hepatocellular carcinoma  and its serum levels were correlated with clinicopathological features and prognosis of HCC . NAP-2 is a potent activator and attractant for human neutrophils in vitro and in vivo . The expression of NAP-2 mRNA was very low in normal liver tissues but extremely high in liver tumor tissues . Interestingly, no expression of NAP-2 was observed in tissues of other type of tumors, suggesting that NAP-2 probably to be a specific biomarker of liver cancer.
Previous studies using either 2D-gel electrophresis or SELDI-MS in HCC have provided evidence that proteomic biomarkers can be used to discriminate HCC from healthy controls. Reported protein biomarkers included ferritin light chain , vitronectin , apolipoprotein E, chloride intracellular channel 1 , liver aldolase, tropomyosin β-chain, ketohexokinase, enoyl-CoA hydratase, albumin, amooothelin, arginase-1 , complement C3a , and brian derived neurotrophic factor (BDNF) . Differences between previous studies and this present study might result from the applied methods (comprehensive strategy vs single approach), available samples (e.g. serum vs tissue), sample preparation (e.g. fractionation), different protein chip (CM10, IMAC-Zn, IMAC-Cu, WCX2), patient or control characteristics, and data analysis with different algorithms.
In summary, we have identified a set of protein peaks that could discriminate HCC from healthy controls. From the protein peaks specific to HCC disease, we identified and characterized neutrophil-activating peptide-2 as a potential proteomic biomarker of HCC. Further studies with larger sample sizes will be needed to verify this specific protein marker and to address its efficacy, especially with regard to discriminating histologic types of HCC and disease stages. Nevertheless, our study demonstrates a rational approach for identifying HCC biomarkers that could be used for detection and monitoring HCC by proteomic techniques.
5.1 Patients and samples
Characteristics of the subjects
HCC patients (N = 48)
Controls (N = 33)
46.0 ± 11.8
41.8 ± 10.8
Hepatitis B infection (%)
Hepatitis C infection (%)
5.2 SELDI-TOF-MS analysis of serum protein profiles
Protein profiling of serum samples was performed using the eight-spot format WCX2 (weak cationic exchange) ProteinChip Arrays (Ciphergen Biosystems, Fremont, CA, USA). Frozen serum samples were thawed and spun at 10,000 rpm for 5 min at 4°C. Twenty μl of U9 buffer was added to 10 μl aliquots of each serum sample and placed on ice for 30 min before adding 360 μl WCX-2 buffer. Arrays were prepared as follows: each array was pre-equilibrated 2 × 5 min in 200 μl WCX-2 buffer on a horizontal shaker (MSI Minishaker) before sample addition. The sample supernatant was added and incubated for 1 hr on the shaker. After incubation, the sample was removed, and each spot was washed with 200 μl WCX-2 buffer for 2 × 5 min with agitation. After washing, the array was carefully separated from the bioprocessor and washed briefly with deionized water. 0.5 μl sinapinic acid (SPA) was deposited on the array spots and allowed to air dry.
The ProteinChip Arrays were read by surface-enhanced laser desorption/ionization time-of flight (SELDI-TOF-MS) mass spectrometry (ProteinChip PBS II reader, Ciphergen). This was calibrated using NP20 chips that had been bound with all-in-one standard proteins to set up the parameters. The optimal detection parameter of mass/charge size range was set between 2000 and 10000 M/Z with a maximum of 50000 M/Z. The laser intensity was set at 175 and detector sensitivity was set at 5. An average value of 130 spots was presented for each sample. All samples were detected with the same parameters. All the raw data was normalized with the ProteinChip Software version 3.1 (homogenization of the total ion strength and M/Z). The M/Z sample peaks with more than 2000 M/Z were normalized with biomarker wizard of ProteinChip Software version 3.1 for noise filtering. The first threshold for noise filtering was set at 5, and the second was set at 2. The minimum threshold for clustering was set at10%. Spectrum analysis was performed using the Biomarker Patterns Software.
5.3 Bioinformatics and biostatistics
Patients with HCC were split into a training set and a testing set. The training sample set consisted of 33 HCC patients and 33 healthy controls. The protein profiling spectra obtained from the serum samples were normalized using total ion current normalization from Ciphergen's ProteinChip Software version 3.1. Peak labeling was performed by the Biomarker Wizard feature of this software. The intensities of selected peaks were then transferred to Biomarker Pattern Software (BPS) as a 'root node'. On the basis of peak intensity, a threshold was determined by BPS to classify the root node into two child nodes. If the peak intensity of a blind sample was lower than or equal to the threshold, this peak would be labeled as left-side child node. Peak intensities higher than the threshold would be marked as right-side child node. After multiple rounds of decision-making, BPS pooled all labeled samples into a terminal node, where samples were divided as cancer group or control group using the classification tree. Classification of the training set was made to yield the least classification error.
The testing set consisted of serum samples from 48 patients with HCC and 33 control individuals. Using the classification model generated from training dataset, BPS evaluated all of the protein peak intensities for each sample in the testing dataset. It then discriminated HCC and control samples according to their proteomic profile characteristics. Model sensitivity was defined as the probability of predicting HCC cases, while specificity was defined as the probability of predicting healthy controls. A positive predictivity value was defined as the probability of HCC if a test result was positive.
5.4 Biomarker purification and identification
Pooled serum samples (n = 6) from HCC patients with high SELDI intensities at 7789 and 7984 m/z were selected. These samples were diluted with 9 M urea, 10 mM Tris/HCI (Ph 7.4) and applied to AKTA Purifier T-900 column system . After sample purification, albumin and immunoglobulin were removed from the serum by 3 GA and then by Protein A. The rest of the fractions were loaded onto a Tricine-SDS-PAGE gel according to the methods of Fountoulakis and Schagger [34, 35], using ETTAN II (Amersham Pharmacia) gel electrophoresis system.
Electrophoresis was run at 20 mA for 3 hr. Gels were then stained with Coomassie Brillian Blue. The bands corresponding to the 8000 m/z markers were excised and then destained with two washes of 50 μl deionised water, followed with 50 μl ACN/50 mM/L NH4HCO3 (1:1, v/v), and dried in a SpeedVac concentrator. The dried gel slices were rehydrated with 10 mM DTT followed by 50 mM IAM (45 min at room temperature in the dark). After several washes with 25 mM NH4HCO3 and 100% CAN, 20 mg/L-solution of trypsin was added to the gel slices and digestion was allowed to proceed at 37°C for 12 hr.
The trypsin digested sample was loaded onto a C18 reversed-phase column (5 mm × 250 μm, PepMapC18, LC Packings, Amsterdam, The Netherlands), and the peptides were separated by electros pray ionization (ESI, Bruker Esquire 3000, Bruker Daltonik, Bremen, Germany). Proteins were identified by an automated searching algorithm against the SWISS-Protand NCBI protein database.
5.5 Immunohistochemistry (IHC)
HCC tissues and adjacent liver tissues (control) were processed according to standard approaches . The anti-NAP-2 serum (1:1600, Immunechem Pharmaceuticals INC, Canada) was applied to both HCC and control slides and incubated in a moist chamber at 4°C overnight. 0.01 ml PBS was used as the negative control in all experiments. Slides cut in parallel to the IHC-treated sections were stained by HE for better identification of the different tissue areas. To avoid interindividual bias of IHC staining differentiations, all slides were determined by an experienced pathologist.
This work was supported by Grants from Guangxi Scientific Fund (Grant No. : 0322025-2, 0428005-12, 0511201-4, and 0630006-5E). The authors thank Maureen Convery for her excellent proofreading of this manuscript.
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