Hindawi Publishing Corporation
Journal of Biomedicine and Biotechnology
Volume 2012, Article ID 985020, 8 pages
SerumPeptidomePatterns of Colorectal Cancer
Basedon MagneticBead Separation andMALDI-TOF Mass
1Institute of Anal-Colorectal Surgery, No. 150 Hospital of PLA, LuoYang 471000, China
2The Clinical Laboratory, No. 150 Hospital of PLA, LuoYang 471000, China
3Department of Radiology, The First Affiliated Hospital of Henan Science and Technology University, LuoYang, China
Correspondence should be addressed to Chun-Fang Gao, email@example.com
Received 2 February 2012; Accepted 14 June 2012
Academic Editor: Saulius Butenas
Copyright © 2012 Nai-Jun Fan et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background. Colorectal cancer (CRC) is one of the most common cancers in the world, identification of biomarkers for early
detection of CRC represents a relevant target. The present study aims to determine serum peptidome patterns for CRC diagnosis.
Methods. The present work focused on serum proteomic analysis of 32 health volunteers and 38 CRC by ClinProt Kit combined
An independent group of serum (including 33 health volunteers, 34 CRC, 16 colorectal adenoma, 36 esophageal carcinoma, and
31 gastric carcinoma samples) was used to verify the diagnostic and differential diagnostic capability of the peptidome patterns
blindly. Animmunoassay methodwas used todetermine serumCEAof CRCand controls. Results.A quick classifier algorithm was
used to construct the peptidome patterns for identification of CRC from controls. Two of the identified peaks at m/z 741 and 7772
were used to construct peptidome patterns, achieving an accuracy close to 100% (>CEA, P < 0.05). Furthermore, the peptidome
patterns could differentiate validation group with high accuracy. Conclusions. These results suggest that the ClinProt Kit combined
with mass spectrometry yields significantly higher accuracy for the diagnosis and differential diagnosis of CRC.
Colorectal cancer (CRC) is the third most commonly
diagnosed cancer in males and the second in females,
with over 1.2 million new cancer cases and 608,700 deaths
estimated to have occurred in 2008 . A majority of CRC
are either locally or distantly invasive at diagnosis, restricting
treatment options and reducing survival rates, whereas the
5-year survival rate is extremely favorable if detected at an
early stage and successfully resected [2, 3]. Therefore, early
diagnosis is of importance for CRC patient prognosis .
Although several screening techniques, such as colonoscopy,
fecal occult blood testing (FOBT), and analysis of various
serial markers are recommended, the early diagnosis rate of
CRC is still comparatively low . So it remains to be an
urgent necessity to explore effective biomarker for diagnosis
Proteomics, concerning comprehensive protein profile
changes caused by multigene alterations, are currently con-
sidered to be the most powerful tool for global evaluation
of protein expression . Human serum contains thousands
of proteolytically derived peptides, called peptidomes, which
may provide a robust correlation of the physiological and
pathological process in the entire body [7, 8]. The panels
of peptidome markers might be more sensitive and specific
than conventionally biomarker approaches . Preliminary
studies have shown that there is a great interest in the low
molecular weight region, particularly peptides smaller than
20kD, which may provide a novel means of diagnosing
cancer and other diseases [8, 10, 11].
Advances in mass spectrometry (MS) now permit the
display of hundreds of small- to medium-sized peptides
using only microliters of serum [12, 13]. Matrix-assisted
2Journal of Biomedicine and Biotechnology
(MALDI-TOF MS) can detect peptides with low molecular
a useful technique for serum peptide profiling. Furthermore,
for accurate MS analysis, the peptidomes fractionation pro-
cedure and preanalytical conditions of peptidomes mapping
must be carefully assessed . Magnetic bead (MB), based
on nanomaterials, has been developed and was considered as
of peptides and proteins in biological samples [15, 16].
Combination of MALDI-TOF MS and MS enables high
throughput and sensitive investigation of peptides and
A well-defined novel technology platform, called Clin-
Prot (Bruker Daltonics, Ettlingen, Germany), comprising
a weak cationic-exchanger magnetic beads- (WCX-MB-)
based sample separation, MALDI-TOF MS for peptide
profiling acquisition, and a bioinformatics package for
inspection and comparison of data sets to create “disease-
specific” peptidome pattern models, which could serve as a
powerful tool for the diagnosis of cancer [17–19].
In the current study, we used ClinProt to determine
serum peptidome patterns for CRC diagnosis. The resulting
spectra between groups were analysed using postprocess-
ing software ClinProt 2.2 and patterns recognition Quick
Classifier (QC) Algorithm. Diagnostic model, comprised
by two differentially expressed peptides, were established
and validated by the QC Algorithm, by which different
groups were discriminated effectively. The diagnostic model
obtained in this manner was further verified in blinded CRC,
colorectal adenoma and health volunteer samples. Further-
more, to understand its differential diagnosis potential, the
carcinoma (EC) and gastric cancer (GC) samples. Thus, the
preliminary work was completed for an early diagnosis and
differential diagnosis of CRC from an integrated perspective
of peptide mass patterns.
2.1. Reagents and Instruments. The AutoFlex III MALDI-
TOF MS, MTP 384 target plate polished steel, α-cyano-
hydroxycinnamic (CHCA), MB-WCX kit and peptide cal-
ibration standard were purchased from Bruker Daltonics
(Leipzig, Germany). Trifluoroacetic acid (TFA) was pur-
chased from Alfa Aesar (Ward Hill, MA, USA). Acetonitrile
(ACN) was acquired from Sigma (St. Louis, MO, USA).
Diagnostic Kit of Carcinoembryonic antigen (CEA) (ELISA)
was acquired from Roche Diagnostics GmbH (Sandhofer
2.2. Patients and Sample Collection. Sixty-five health subjects
(blood donor volunteers), 72CRC patients, 16 colorectal
adenoma patients, 36EC, and 31GC patients were enrolled
with the permission of the Local Ethical Commission,
and blood was collected after informed consent from the
patients. Enteroscopy were performed in all health subjects
to exclude the presence of incidental colon and rectum
mass. Colorectal adenoma were diagnosed by enteroscopy
and pathologic diagnosis. CRC, EC, and GC patients were
underwent clinical, staging after surgical excision of the
lesion according to the 2004 tumor-node-metastasis (TNM)
stage classification .
Serum samples were prepared by collecting blood in
a vacuum tube and allowing it to clot for 30 minutes at
room temperature. About 1mL of serum was obtained after
centrifugation at 1100g for 10 minutes and stored in small
aliquots at −80◦C until analysis.
2.3. Study Design. The data set including 65 controls and
characteristics of CRC patients were shown in Table 1. The
and 38CRC patients) was used for the identification of
signals related to peptides expressed differentially in CRC
patients compared with controls and patterns recognition.
The second group (external evaluation data set: 33 health
volunteers, 34CRC patients, 16 colorectal adenoma patients,
dent patterns validation of the cluster blindly. The accuracy
of the peptide model was compared with that of CEA.
The gender ratio (male/female) of health volunteers,
colorectal adenoma patients, EC and GC patients was 1.24,
1.67, 1.25, and 2.44, respectively. The mean age (years) of
health volunteers, colorectal adenoma patients, EC, and GC
patients was 54.63±1.37, 59.75±20.62, 61.14±6.82, 58.48±
10.60. The difference of age and gender of health volunteers
in model construction group and external evaluation data
were not significant. No significant differences were either
observed for age and gender between CRC and health
volunteers, nor for TNM stage of CRC between model
construction group and external evaluation group.
2.4. Sample Purification. We used MB-WCX for peptidome
separation of samples following the standard protocol by
the manufacturer . Step 1, 10μL of WCX-MB-binding
microfuge tube after thoroughly vortexing both reagents.
Step 2, 5μL of serum sample was added to the microfuge
tube containing 10μL of WCX-MB-binding solution and
10μL of WCX-beads, and mixed by pipetting up and down.
Step 3, microfuge tubes were then placed in a magnetic
bead separator (MBS) and agitated back and forth 10 times.
The beads were collected on the wall of the tubes in the
MBS 1 minute later. Step 4, the supernatant was removed
carefully by using a pipette. Step 5: 100μL of WCX-MB wash
buffer was added to tubes, which were agitated back and
forth in the MBS 10 times. The beads were collected on the
wall of the tubes, and supernatant was removed carefully by
using a pipette. After three washes, 5μL of WCX-MB elution
buffer was added to disperse beads in tubes by pipetting up
and down. The beads were collected on wall of tubes for 2
minutes and the clear supernatant was transferred into fresh
to the collected supernatant, mixing intensively by pipetting
up and down, the mixture was then ready for spotting onto
MALDI-TOF MS targets and measurement. Finally, prior
Journal of Biomedicine and Biotechnology3
Table 1: Clinical characteristics of colorectal cancer patients
recruited in model construction group and external evaluation
group (n = 38)
62.00 ± 10.95
group (n = 34)
61.74 ± 11.61
Age (years, X ±SD)
to the MALDI-TOF MS analysis, we prepared targets by
spotting 1μL of the proteome fraction on the polished steel
target (Bruker Daltonics ). After air drying, 1μL of 3mg/mL
CHCA in 50% ACN and 50% Milli-Q with 2% TFA was
crystallization).Thepeptide calibrationstandard (1pmol/μL
peptide mixture) was applied for calibrating the machine.
2.5. Mass Spectrometry Analysis. For proteome analysis, we
used a linear Autoflex III MALDI-TOF-MS with the follow-
ing setting: ion source 1, 20.00kV; ion source 2, 18.60kV;
lens, 6.60kV; pulsed ion extraction, 120ns; Ionization was
achieved by irradiation with a crystal laser operating at
200.0Hz. For matrix suppression, we used a high-gating
factor with signal suppression up to 600Da. Mass spectra
were detected using linear positive mode. Mass calibration
was performed with the calibration mixture of peptides and
proteins in the mass range of 1000–12000Da. We measured
three MALDI preparations (MALDI spots) for each MB
fraction. For each MALDI spot, 1600 spectra were acquired
(200 laser shots at 8 different spot positions). Spectra were
collected automatically using the autoflex Analysis software
(Bruker Daltonik) for fuzzy controlled adjustment of critical
2.6. Bioinformatics and Statistical Analysis. The ClinProt
Tools software 2.2 (Bruker Daltonik) was used for analysis of
all serum sample data derived from either patients or normal
controls. Data analysis began with raw data pretreatment,
including baseline subtraction of spectra, normalization of
a set of spectra, internal peak alignment using prominent
peaks, and a peak picking procedure. The pretreated data
were then used for visualization and statistical analysis in
ClinProt Tools. Statistically significant different quantity
of peptides was determined by means of Welch’s t-tests.
The significance was set at P < 0.05. Class prediction
model was set up by QC Algorithm. A classify peptidome
patterns was constructed. To determine the accuracy of the
class prediction, firstly, a cross-validation was implemented.
Twenty percent of model construction group were randomly
selected sample as a test set, and the rest samples were
taken as a training set in the class predictor algorithm.
Secondly, designed as double blind test, the samples of
external evaluation group were classified by the classify
peptidome patterns constructed by QC Algorithm.
2.7. Detection of CEA. The serum CEA of 38CRC and 32
health volunteers included in model construction group was
detected using an electrochemiluminescent immunoassay
(The methods were omitted). The sample was diagnosed
as CRC (CEA ? 5ng/mL), otherwise diagnosed as health
volunteers (CEA < 5ng/mL).
2.8. Statistical Methods, Evaluation of Assay Precision. We
analyzed each spectrum obtained from MALDI-TOF MS
with Autoflex analysis and ClinProt TM software (Bruker
Daltonics), the former to detect the peak intensities of
interest and the latter to compile the peaks across the spectra
obtained from all samples. This allowed differentiation
between the cancer and control samples. To evaluate the pre-
cision of the assay, we determined within- and between-run
variations by use of multiple analyses of bead fractionation
and MS for 2 plasma samples. For within- and between-run
variation, we examined 3 peaks with various intensities. We
determined within-run imprecision by evaluating the CVs
between-run imprecision by performing 8 different assays
over a period of 7 days. SPSS 16.0 was used for analysis of
The significance wasset at P < 0.05. Also, SPSS 16.0 wasused
to compare the accuracy of the peptidome models and CEA.
For the reproducibility of the protein profiling, Within- and
between-run reproducibility of 2 samples were determined
with the WCX-MB fractionation and MALDI-TOF MS
analysis. In each profile, 3 peaks with different molecular
masses were selected to evaluate the precision of the assay.
Despite varying peptide masses and spectrum intensities, the
peak CVs were all <3% in the within-run and <9% in the
between-run assays. These values were consistent with the
reproducibility data for the Protein Biology System reported
by the manufacturer (Bruker Daltonik).
In the pilot study we evaluated the differences of the
serum proteome profiles of CRC in comparison to health
subjects. The mass spectra from 1 to 18kDa were obtained
by MALDI-TOF MS in linear mode. The representative
group are reported in Figure 1. On average about 156
signals common to the two groups have been detected
in this mass range and about 61 were identified by the
ClinProt software with a statistically different area (P < 0.05
by Wilcoxon analysis) in model construction population,
including 49 upregulated and 12 downregulated peptides.
Two peptides selected for model construction were shown
4Journal of Biomedicine and Biotechnology
Figure 1: View of the aligned mass spectra of the serum protein
profile of model construction group (red: 10 health subjects,
blue: 10 colorectal cancer patients) obtained by MALDI-TOF after
purification with WCX magnetic beads.
in Table 2 and the others are shown in Supplementary
Table S1 (see Supplementary Material available online at
Classification models were developed to classify samples
between CRC and health volunteers. The use of individual
peaks as diagnostic biomarker for CRC was addressed
using QC algorithm analysis. First, we conducted com-
parison between CRC and health volunteers. Second, all
detected peaks were analysed by ClinProt 2.2 to generate
cross-validated classification models. The optimized model
resulted in the following correct classification of samples.
as a class prediction for a cross-validation set to discriminate
CRC from health volunteers, which achieved a recognition
capacity of 97.3% and a cross-validation of 97.3%. Regions
of the mass spectra obtained at 800 resolving are reported in
Preliminary statistical analysis was carried out for each
single marker and for the cluster of signals by the receiver
operating characteristic curve analysis. Area under curve
(AUC) of peak A at m/z 741 (P < 0.000001) and of peak B at
m/z 7442 (P < 0.000001) was 0.988 and 0.991, respectively,
which corresponds to a highly accurate test, according to the
criteria suggested by Swets  (Figure 3). Moreover areas of
these peaks in the spectra of CRC were statistically different
from those of the health volunteers (Figure 4). Combination
of the two peaks allowed to yielding a specificity of 100%,
and a sensitivity of 94.74% for CRC (Table 3, Figure 5).
To verify the accuracy of the established QC classification
model with the adopted peptides, we introduced another
group of samples (not used in model construction), which
consisted of 34CRC, 16 colorectal adenoma, and 33 health
volunteers. As a result, the model correctly classified 94.12%
(32/34) of CRC (sensitivity), 100% (16/16) of colorectal ade-
noma (specificity), and 100% (33/33) of health volunteers
(specificity), which surpassed that of CEA (a specificity of
51.02% (25/49), and a sensitivity of 41.18% (14/34)). To
model, we introduced a group of other common cancers
samples, which consisted of 36EC and 31GC. As a result, the
model correctly classified 100% (36/36) of EC (specificity)
and 100% (31/31) GC (specificity) as controls (Table 3).
The usefulness of multiple markers for diagnosis, prognosis,
and for predicting the risk of developing diseases or their
complications is now widely recognized [7, 23]. Various
proteomic approaches have been applied to biomarker
discovery using biological fluids. It is being interestingly
recognized that low mass weight peptides, such as S100A8
and fibrinogen, play an important role in physiological
and pathological process and could be used as relevant
biomarker candidates [24, 25]. Recently mass spectrum that
directly detects and differentiates short peptides has offered
a promising approach for peptidomic biomarker discovery
[8, 10, 26–28].
Compared with genomic approaches, proteomic analysis
has the advantage of visualizing co- and posttranslational
modifications of proteins, possibly of relevance for biologic
are time-consuming for routine use, such as the classic
method of comparing data from two-dimensional elec-
trophoresis, subsequent isolation of the proteins from the
gel, and analysis by MS . Another method, the surface-
enhanced laser desorption and ionization time-of-flight MS,
recently reported by several groups, have been applied in
common cancers screening using serum peptidome patterns
[30–33]. These reports emphasized the potential diagnosis
value of low molecular mass peptide or protein.
MALDI is a soft ionization technique used in MS,
allowing the analysis of bio-molecules such as proteins,
peptides sugars, and large organic molecules. The time-of-
flight (TOF) mass spectrometer is ideally suited type to the
MALDI, which can reach a resolving power m/Δm of the
well above 20,000FWHM (full-width half-maximum; Δm
defined as the peak width at 50% of peak height). As a
powerful tool for surveying complex patterns of biologically
informative molecules, MALDI-TOF MS protein profiling
has been applied in proteomics biomarker research and
has become a promising tool in cancer biomarker research
[26, 34, 35].
In present study, by integrating the purification of short
peptides with WCX-MB, detection of peak intensity with
MALDI-TOF MS, and profile analysis with ClinProt Tool
software 2.2, we have successfully detected a series of short
peptides that differentially expressed in the serum of patients
with CRC. A case control comparative analysis between
CRC and health volunteer was performed. Peptidomic maps
associated with the disease were drawn. The results show
that, compared to normal controls, CRC sharing 61 signif-
icantly differentiated peptides, including 49 upregulated and
12 downregulated peptides. Current knowledge of cellular
regulation indicates that many networks operate at the
epigenetic, transcriptional, and translational levels. Genomic
and proteomic technologies will help further understand
the intracellular signaling and gene transcription systems
as well as the protein pathways that connect extracellular
microenvironment to the serum or plasma macroenvi-
ronment of cancer . These 61 interesting significantly
differentiated peptides may provide further evidence for
understanding the occurrence and progress of CRC. In
Journal of Biomedicine and Biotechnology5
Table 2: Statistic of the 2 candidate biomarkers signals selected for the diagnostic model for identifying colorectal cancer from health
1P value calculated with the Wilcoxon test; values lower than 0.05 suggest statistical relevance.
2P value calculated with the Anderson-Darling test, values lower than 0.05 suggest statistical relevance.
3Average area of peaks for colorectal cancer subjects.
4Average area of peaks for health subjects.
5Standard deviation of peaks for colorectal cancer subjects.
6Standard deviation of peaks for health subjects.
Figure 2: Zoom of the mass range for the two signals (MALDI-TOF linear mode) used in the cluster to differentiate colorectal cancer (CRC)
from healthy volunteers (H).
6Journal of Biomedicine and Biotechnology
Figure 4: Box-and-whiskers plot calculated from the areas of the two signals used in the cluster for the two studied populations. Red
represents colorectal cancer, green represents healthy volunteers.
Table 3: The predicted results of peptidome pattern distinguishing colorectal cancer patients from controls.
Sensitivity (%) Specificity (%)Youden’s index
38 3294.74% (36/38)100% (32/32)0.95
Figure 5: Two-dimensional peak distribution view of the two peaks
selected for the diagnostic model. The peak area and the m/z
values are indicated on the x- and y-axes. The ellipses represent the
standard deviation of the class average of the peak areas/intensities.
Red represents colorectal cancer patients and green represents
particular, the prominent peptides that have a greater than
be defined as the leading differential peptides associated with
colorectal cancer, worthy of further sequence determination
and function analysis.
By using the QC algorithm analysis, classification model
were developed to classify samples between healthy volun-
teers and CRC. A cluster of two peptides at m/z 741 and
7772 achieved a recognition capacity and a cross-validation
of close to 100% (a specificity of 100%, and a sensitivity
of 94.74%) to discriminate CRC from healthy volunteers.
Blinded verification of the QC classification model proved
to correctly classify 94.12% (32/34) of CRC, 100% (33/33)
health volunteers. Furthermore, to evaluate the differential
diagnosis capacity, 16 colorectal adenoma patients and
36EC patients, and 31GC patients were applied for blinded
verification. Interestingly, 100% of the individuals were
classified as control, which suggest that the classification
model could identify CRC from colorectal adenoma and two
of the most common digestive tract cancers (EC and GC).
This demonstrated that the QC Algorithm would be effective
in facilitating the construction of a sensitive and specific
According to our knowledge, this study is the first to
screen CRC related short peptides in sera by combining
WCX-MB and MALDI-TOF-MS. The classification model
Journal of Biomedicine and Biotechnology7
we have setup have application in providing alternatives for
CRC diagnosis or differential diagnosis, and may provide a
better understanding of the pathogenesis in CRC or help
in tailoring the use of chemotherapy to each patient, finally
resulting in an improvement in patient outcome. Despite of
the high sensitivity and specificity, the number of specimens
analyzed in this study was relatively small, which may limit
the validity of the results. The next step of our study will be
to analyse larger patient cohorts and to run blinded samples
to confirm the usefulness of our currently identified peptides
for CRC diagnosis. After this confirmation, we will then
isolate and identify the biomarkers of the interest and study
their biological role in CRC pathogenesis.
In conclusion, we directly profiled peptidome patterns
from WCX-MB-purified serum samples with MALDI-TOF
MS, and constructed a peptidome model that differentiated
CRC from control samples with high sensitivity and speci-
ficity, which may be applied as an alternative method for the
diagnosis and differential diagnosis of CRC.
Conflict of Interests
The authors stated that there are no conflict of interests
regarding the publication of this paper.
This work was funded by the Medical Science Research
Project of The Chinese People’s Liberation Army (no.
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