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Promising Diagnostic Biomarkers for Primary Biliary Cirrhosis Identified With Magnetic Beads and MALDI-TOF-MS.

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THE ANATOMICAL RECORD 292:455–460 (2009)
Promising Diagnostic Biomarkers
for Primary Biliary Cirrhosis Identified
With Magnetic Beads and MALDI-TOF-MS
YONG-ZHE LI,1* CHAO-JUN HU,1 XIAO-MEI LENG,1
GUAN-FEI ZHAO,1 NING LI,2 AND YANG XU2*
1
Department of Rheumatology, Peking Union Medical College Hospital, Peking Union
Medical College & Chinese Academy of Medical Sciences, Beijing, China
2
Center for Clinical Laboratory Development, Peking Union Medical College & Chinese
Academy of Medical Sciences, Beijing, China
ABSTRACT
(PBC) is not a rare disease worldwide. Most patients are diagnosed
at the advanced stage, primarily because there are not yet any valid biomarkers available for early diagnosis. Useful biomarkers are absolutely
necessary for early detection of PBC. Fortunately, the use of MALDITOF-MS and pattern recognition software has been successful in finding
specific markers for the early detection of the disease. To screen for
potential protein biomarkers in the serum for diagnosing PBC, MALDITOF-MS combined with magnetic beads and pattern recognition software
was used to investigate 119 serum samples from 44 patients with PBC,
32 controls with other hepatic disease, and 43 healthy controls. A total of
69 discriminant m/z peaks were identified as being associated with PBC.
Of them, the m/z peaks at 3445, 4260, 8133, and 16,290 were used to construct a model for the diagnosis of PBC. This diagnostic model can distinguish PBC from non-PBC controls with a sensitivity of 93.3% and a specificity of 95.1%. In our blind test, it demonstrated good sensitivity and
specificity: 92.9% and 82.4%, respectively. These results indicate that useful serum biomarkers for PBC can be discovered by MALDI-TOF-MS
combined with the use of magnetic beads and pattern recognition software. The pattern of multiple markers provides a powerful and reliable
diagnostic method for PBC with high sensitivity and specificity. Anat
Rec, 292:455–460, 2009. Ó 2009 Wiley-Liss, Inc.
Key words: primary biliary cirrhosis; biomarker; magnetic
beads; MALDI-TOF-MS
Primary biliary cirrhosis (PBC) is a chronic autoimmune cholestatic liver disease characterized by progressive, nonsuppurative inflammation, and the destruction
of small and medium-sized bile ducts (Locke et al., 1996;
Kaplan and Gershwin, 2005). Recently, studies have suggested that PBC is not a rare disease worldwide (Metcalf
and James, 1997), especially asymptomatic PBC, which
is probably more prevalent in the Asian population than
Grant sponsor: National Natural Science Foundation of China
(NSFC); Grant numbers: 30640084, 30471617.
Yong-zhe Li and Chao-jun Hu contributed equally to this
work.
Development, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 102206, China and Center for
Organelle Proteomics of Diseases, Zhejiang University School of
Basic Medical Science, Hangzhou, China. Fax: 186-1067016735. E–mail: yxu1617@126.com
*Correspondence to: Yong-zhe Li, Department of Rheumatology, Peking Union Medical College Hospital, Peking Union
Medical College & Chinese Academy of Medical Sciences, Beijing 100730, China. Fax: 186-10-88068795. E-mail: yongzhelipumch@yahoo.cn or Yang Xu, Center for Clinical Laboratory
DOI 10.1002/ar.20870
Published online in Wiley InterScience (www.interscience.wiley.
com).
Ó 2009 WILEY-LISS, INC.
Received 14 April 2008; Accepted 25 November 2008
456
LI ET AL.
thought previously. Research has shown that PBC is a
significant cause of morbidity and mortality in Chinese
patients (Wong et al., 2005). Currently, the most reliable
procedure for diagnosing PBC is histopathology of a liver
biopsy (Heathcote, 2000). Unfortunately, the characteristic histopathological changes that are associated with
PBC cannot be observed in all tissue samples, and the
patients are generally reluctant to accept this invasive
procedure (Zeniya et al., 2005). Therefore, high concentrations of anti-mitochondrial antibodies (AMA) in the
serum have served as a hallmark for diagnosing PBC
(Leung et al., 1997; Neuberger, 1997). AMA is neither a
characteristic nor a specific marker for PBC, however,
and can be frequently detected in patients with other
medical conditions, such as infectious liver disease (Toda
et al., 1997; Miyakawa et al., 2006). In general, over
10% of patients with PBC have an undetectable level of
serum AMA (Kaplan, 1996; Sakauchi et al., 2006). Frequently, patients with PBC are in the terminal stages of
their disease when they are diagnosed. A new method
for diagnosing the early stage of PBC is still an unmet
need in clinical practice. Proteomics has been shown to
be a promising method for the early detection of cancer,
neuropathic disease, infectious disease, and rheumatic
diseases (Shiwa et al., 2003; Dotzlaw et al., 2004; De
Seny et al., 2005; Kang et al., 2005; Agranoff et al.,
2006). In this study, we use proteomic approaches to
identify relevant biomarkers that could replace invasive
and nonspecific tests for the early diagnosis of PBC.
The goal of this study was to screen for potential protein biomarkers in serum for the early diagnosis of PBC
using MALDI-TOF-MS combined with magnetic beads
and pattern recognition software.
PATIENTS AND METHODS
Patients
One hundred nineteen serum samples were collected
from 44 patients with PBC, 32 from patients with liver
diseases other than PBC (10 were autoimmune hepatitis
patients; 10 were hepatitis B patients; nine were hepatocirrhosis patients; one was an alcoholic hepatitis patient;
one was a drug-induced liver disorder patient; and one
was a primary sclerotic cholangitis patient), and 43 were
healthy volunteers. Blood samples were obtained with
informed consent from all subjects. The protocol was
approved by the review board of Peking Union Medical
College Hospital. The 44 patients with PBC were clinically diagnosed according to generally accepted criteria
(Heathcote, 2000). The demographic data of the PBC
patient group are shown in Table 1. The blood samples
were collected in 4 mL BD Vacutainers without anticoagulation and allowed to clot at room temperature for up
to 1 hr; the samples were then centrifuged at 48C for 5
min at 1000 rpm (1000g). The sera were frozen and
stored at 2808C for future analysis.
Serum Pretreatment and Binding to
Magnetic Beads
Serum samples were pretreated with WCX magnetic
beads (SEDTM) (Beijing SED Science and Technology,
Inc.). Briefly, 10 mL of each serum sample was mixed
with 20 mL of U9 solution (9 mol/L urea, 2% CHAPS) in
a 0.5 mL centrifuge-tube. After incubating for 30 min at
TABLE 1. Demographics of the PBC patients and
control groups
Sample type
Normal
PBC
Hepatic disease
Number of
samples
Male/
female
Age
range
Mean age
43
44
32
27/16
3/41
17/15
25–78
16–74
18–75
38.6 610.9
54.1 6 11.3
50.6 613.1
48C, the samples were diluted 40-fold with the addition
of 370 mL of binding buffer (50 mmol/L sodium acetate,
pH 4.0). Then, 50 mL of WCX magnetic beads were
added to a PCR tube, and the tubes were placed in a
magnet separator for 1 min. The supernatant was
removed carefully using a pipette. The magnetic beads
were then washed twice with 100 mL binding buffer.
Diluted serum sample in a volume of 100 mL was added
and mixed with the activated magnetic beads carefully
by pipetting up and down several times; the samples
were incubated for 1 hr at 48C and then washed twice
with 100 mL binding buffer. Following binding and washing, the bound proteins were eluted from the magnetic
beads using 10 mL of 0.5% (vol/vol) trifluoroacetic acid
(TFA). Then, 5 mL of the eluted sample was diluted in
the ratio of 1:2 with 5 mL of SPA (50% CAN 1 0.5%
TFA). One microliter of the resulting mixture was aspirated and spotted onto an 8-spot pre-structured sample
chip (Au-chip, Ciphergen). After air-drying for 5 min
at room temperature, protein crystals on the chip were
detected by MALDI-TOF-MS (Ciphergen, PBS IIc).
Statistical Analysis
Data were collected by averaging 80 laser shots per
spot with an intensity setting of 205 and a detector sensitivity setting of 8. The spectra from all samples were initially analyzed using Biomarker Wizard Version 3.1 with
the following processes: the 4091 mass peak in serum
was used to normalize dimensions; the baseline was subtracted; the peaks were automatically detected and clustered (First 5, Min Peak 10%, Cluster Mass 0.3%, Second
Pass 2); statistics on the sample group was performed.
Model Construction
The data were analyzed and a model was constructed
using Biomarker Patterns Software (BPS) Version 5.0.
The final model was setup as: Relative cost: 0.362; Method
5 0; Advanced 5 10, 1; Testing 5 21; and Costs 5 1:1.
Data Score
The examiner was blind to the data used to test the
diagnostic model. Forty-eight samples were tested in
total, including 14 patients with PBC, 14 patients with
other hepatic diseases, and 20 healthy controls.
RESULTS
Optimization of the Experimental Conditions
and Evaluation of the Reproducibility
Reproducibility was evaluated with four mixed serum
samples from the healthy controls of blood type O (two
women and two men). The mixed serum samples were
457
PROMISING DIAGNOSTIC BIOMARKERS FOR PBC
Fig. 1. An 8-spot reproducibility test showed good reproducibility. The CV of all the selected mass
peaks was below 10%.
TABLE 2. The 69 discriminating m/z peaks among PBC, hepatic disease controls, and normal controls
m/z
3088
2543
8133a
7628
8600
5477
31842
3935
2759
4281
4300
7967
3955
22798
a
P
m/z
P
m/z
P
m/z
P
m/z
P
2.70E–09
1.3E–08
2.2E–07
2.7E–07
3E–07
1.6E-05
2.1E–05
4.2E–05
4.7E–05
7.1E–05
7.9E–05
0.0001
0.0002
0.0002
15874
11485
4068
8071
15608
5803
11697
6846
16290a
16489
3375
9280
2869
16077
0.0002
0.0002
0.0002
0.0003
0.0003
0.0004
0.0006
0.0007
0.0008
0.0009
0.001
0.001
0.0013
0.0018
5855
3774
8290
4382
28026
4645
9487
23386
5246
14016
5634
9794
14103
7405
0.0023
0.0024
0.0024
0.0027
0.0027
0.0027
0.0027
0.0033
0.0037
0.0042
0.0051
0.0056
0.0056
0.0066
5315
43917
46614
8554
3681
28790
7761
3445a
25099
4528
43219
24015
4676
7049
0.0075
0.0101
0.0111
0.0114
0.0122
0.0124
0.0125
0.0135
0.0151
0.0189
0.0194
0.0235
0.0236
0.0273
3397
3887
9606
32550
5214
5384
7554
4260a
47633
1498
9399
9116
5525
0.03
0.03
0.0333
0.0351
0.0383
0.0414
0.0419
0.0434
0.0441
0.0452
0.463
0.474
0.0487
Selected to construct a model for diagnosis of PBC.
spotted on an eight spot Au-chip. The CV was under
10% for all the selected mass peaks (Fig. 1).
Protein Fingerprint Analysis of Serum Samples
in Patients with PBC, Other Hepatic Disease
Controls, and Healthy Controls
The protein profile of the serum samples from the 44
patients with PBC, the 32 patients with other hepatic
diseases, and the 43 healthy controls were extracted by
magnetic beads and examined by MALDI-TOF-MS. The
data were analyzed by Biomarker Wizard Version 3.1;
69 m/z peaks were found to discriminate the patients
with PBC, patients with other hepatic diseases, and normal controls (Table 2).
Identification of Biomarker Pattern and
Construction of Diagnostic Model
The 69 m/z peaks that were distinct among patients
with PBC, patients with other hepatic diseases, and
healthy controls were analyzed by BPS Version 5.0 to
458
LI ET AL.
Fig. 2. The representative m/z peaks at 3445 (a), 4260 (b), 8133 (c), and 16290 (d) in the PBC, hepatic
disease control (HD), and normal control (Nor) samples.
TABLE 3. Serum sample used in the training and
test groups
Immunology
Normal
PBC
Hepatic disease control
Fig. 3.
TABLE 4. Classification of the data in the diagnostic
model for PBC
Training group
Test group
Total
Group
Cases N
N correct-classed
Pct accurate
23
30
18
20
14
14
43
44
32
PBC
Control
30
41
28
39
93.33
95.12
The decision trees of diagnostic model for PBC.
identify biomarker patterns. The m/z peaks at 3445,
4260, 8133, and 16290 Da of 71 objects (Fig. 2, Table 3)
were selected as the best markers to construct a diagnostic model for PBC. The decision tree is shown in Fig.
3, and the characteristics of the diagnostic model are
shown in Table 4 and Fig. 4.
Fig. 4. ROC curve of the diagnostic model for class PBC. Note:
The data used to construct the diagnostic model included 71 samples:
30 PBC, 18 hepatic disease controls, and 23 healthy controls.
Test of the Diagnostic Model for PBC in a
Blind Test
Forty-eight samples, including 14 from patients with
PBC, 14 from patients with other hepatic diseases, and
459
PROMISING DIAGNOSTIC BIOMARKERS FOR PBC
20 healthy controls (Table 3) were used to test the PBC
diagnostic model. The results are shown in Table 5.
Discrimination of m/z Peaks Between Patients
with PBC with Positive AMA-M2 and Patients
with PBC with Negative AMA-M2
The BPS identified five m/z peaks that were able to
discriminate AMA-M2 positive and AMA-M2 negative
patients with PBC. The results are shown in Fig. 5 and
Table 6.
DISCUSSION
Cholestatic disorders are not uncommon and should be
differentiated from other hepatic diseases (Kim et al.,
2000). There are currently no diagnostic tests available that
can meet this goal, however. At present, liver histological
presentations are used for diagnosis. Biochemical and serological tests can only identify 50%–60% of PBC cases in the
early stage of the disease course. The early diagnosis of
PBC remains a technical challenge (He et al., 2006).
In recent years, as proteomics research has developed,
more and more proteomic technologies have emerged
and have begun to be used in clinical research (Baumeister, 2005; Sinz et al., 2005; Chung et al., 2007). The
search for new biomarkers for complicated diseases has
become more and more successful with the use of new
high throughput proteomic techniques (Choi et al., 2002;
Hayman and Przyborski, 2004). Biomarkers, especially
patterns of multiple biomarkers, are more reliable and
powerful for diagnosis, differential diagnosis, and treatment guidance than the current techniques. Serum pro-
TABLE 5. The predictions for PBC made by the
diagnosis model in a blind test
Group
Cases N
N correct-classed
Pct accurate
PBC
Control
14
34
13
28
92.86
82.35
teome analysis by MALDI-TOF-MS has provided new information about peptides and proteins that have the
potential to be surrogate markers. As magnetic beads
can provide a large absorbable surface, they have much
more potential to adsorb peptides and proteins from serum than the ProteinChip surface alone (Villanueva
et al., 2004; Whiteaker et al., 2007). The combination of
magnetic beads and MALDI-TOF-MS can allow us to
enrich and discover more proteins that have low abundance in sera. In our study, the protein fingerprints in
sera of 44 subjects with PBC and 75 controls were analyzed with WCX magnetic beads and MALDI-TOF-MS.
Sixty-nine potential protein biomarkers were found, and
a diagnostic model for PBC was constructed by pattern
recognition software using the m/z peaks of protein biomarkers 3445, 4260, 8133, and 16290. In a blind test on
48 samples, the diagnostic model had a sensitivity of
92.9% and a specificity of 82.4%. This result shows that
the detection of serum proteins by WCX magnetic beads,
MALDI-TOF-MS, and pattern recognition software can
be a powerful tool to distinguish PBC from other hepatic
diseases and healthy controls.
The identification of novel biomarkers for PBC may
increase our understanding of the progression and
pathophysiology of the disease on a molecular level.
Because PBC is a multi-factorial liver disease, using a
combination of multiple markers should be a more reliable and powerful way to diagnose the disease with
higher specificity and sensitivity than using a single
TABLE 6. The five m/z peaks that discriminate
between PBC patients with positive AMA-M2 and
PBC patients with negative AMA-M2
m/z
P
Mean-M21
Mean-M22
4068
3682
3935
4472
8133
0.008
0.026
0.026
0.026
0.028
0.94
1.18
1.46
6.97
5.86
1.49
1.66
2.04
9.03
7.78
Fig. 5. The representative m/z peaks at 3682, 3935, 4068, 4472, and 8133 in patients with PBC with
positive AMA-M2, and patients with PBC with negative AMA-M2.
460
LI ET AL.
marker alone. In our study, the selection of 3445, 4260,
8133, and 16290 m/z peaks by BPS as a biomarker pattern for PBC could separate patients with PBC from
controls with a high confidence level. The selection of
multi-markers was achieved by the BPS (Wiesner, 2004).
One limitation of our study is that only 30 samples
from patients with PBC were included in the building of
the diagnostic model, and only 14 PBC sera samples
were used for the blind test. Therefore, it is still an open
question whether these potential biomarkers are really
specific to PBC. A larger sample with more differential
diagnosis, such as other rheumatisms, inflammatory diseases, and other organ specific diseases is warranted for
further investigation.
In conclusion, using a small number of PBC samples,
we have shown that MALDI-TOF-MS combined with
magnetic beads and pattern recognition software can
distinguish PBC from other hepatic diseases and healthy
controls. In particular, we have found biomarker patterns at 3445, 4260, 8133, and 16290 m/z peaks and
have constructed a diagnostic model for PBC that has
good sensitivity and specificity.
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