Diagnostic and prognostic role of serum protein peak at 6449 m/z in gastric adenocarcinoma based on mass spectrometry
British Journal of Cancer
Diagnostic and prognostic role of serum protein peak at 6449 m/z in gastric adenocarcinoma based on mass spectrometry
Dongjian Song 0 1
Lifang Yue 2
Hao Li 3
Junjie Zhang 0
Zechen Yan 4
Yingzhong Fan 0
Heying Yang 0
Qiuliang Liu 0
Da Zhang 0
Ziqiang Xia 0
Pan Qin 0
Jia Jia 0
Ming Yue 0
Jiekai Yu 5
Shu Zheng 5
Fuquan Yang 6
Jiaxiang Wang 0
0 Department of Pediatric Surgery, The First Affiliated Hospital of Zhengzhou University , Zhengzhou, Henan 450052 , China
1 Institute of Clinical Medicine, The First Affiliated Hospital of Zhengzhou University , Zhengzhou, Henan 450052 , China
2 Department of Ultrasonography, The Third Affiliated Hospital of Zhengzhou University , Zhengzhou, Henan 450052 , China
3 Department of Pediatric Surgery, Guiyang Maternal and Child Health Hospital , Guiyang, Guizhou 550003 , China
4 Department of Surgery, The First Affiliated Hospital of Zhengzhou University , Zhengzhou, Henan 450052 , China
5 Institute of Cancer, The Second Affiliated Hospital, College of Medicine, Zhejiang University , Hangzhou, Zhejiang 310000 , China
6 Proteomic Platform, Institute of Biophysics, Chinese Academy of Sciences , Beijing 100101 , China
Background: Gastric cancer (GC) is a highly aggressive cancer type associated with significant mortality owing to delayed diagnosis and non-specific symptoms observed in the early stages. Therefore, identification of novel specific GC serum biomarkers for screening purposes is an urgent clinical requirement. Methods: This study recruited a total of 432 serum samples from 296 GC patients split into the mining and testing sets. We aimed to screen for reliable protein biomarkers from matched serum samples based on mass spectrometry, followed by comparison with three representative conventional markers using receiver operating characteristic and survival curve analyses to ascertain their potential values as diagnostic and prognostic biomarkers for GC. Results: We identified an apoC-III fragment with confirmation in an independent test set from a second hospital. We found that the diagnostic ability of this fragment performed better than current standard GC diagnostic biomarkers both individually and in combination in distinguishing patients with GC from healthy individuals. Moreover, we found that this apoC-III protein fragment represents a more robust potential prognostic factor for GC than the three conventional markers. Conclusions: In view of these findings, we suggest that apoC-III protein fragment is a novel diagnostic and prognostic biomarker, a complement to conventional biomarkers in detecting GC.
gastric adenocarcinoma; biomarker; serum
Gastric cancer (GC) is a highly aggressive cancer type associated
with significant mortality, with high incidence worldwide
et al, 2011)
. The high mortality rates are attributable to delayed
diagnosis and non-specific symptoms observed at the early GC
(Roth, 2003; Krejs, 2010)
. The current prognosis for GC is
poor, with o25% survival at 5 years after diagnosis
(Ferlay et al,
2010; Siegel et al, 2013)
. Median survival for patients with
advanced-stage GC remains at 8?10 months
. Early detection has been shown to significantly
improve the efficacy of cancer treatment, but diagnosis is often
only possible after the appearance of the first clinical symptoms,
which occurs too late for successful intervention. This is largely
owing to the absence of specific and sensitive tests that would
facilitate early screening and monitoring of cancerous states.
Although serum pepsinogen (PG) tests, such as low PGI
concentration and/or low PGI/II ratio, are suggestive screening
tests in high-risk countries, such as Japan, they are good indicators
of atrophic gastritis, rather than diagnostic markers of GC
(Miki et al, 2003; Miki, 2006; Nasrollahzadeh et al, 2011)
Proteins secreted from tumour tissues have a greater likelihood
of reaching the systemic circulation, and may therefore serve as a
potential biomarkers for early detection
. Serum-based biomarkers are of considerable
importance in early diagnosis of various diseases, including cancer.
A proteome is the complete set of proteins found in a given cell
type in any particular state. Proteomics is the systematic study
of proteomes focusing on the large-scale identification and
characterisation of proteins through measurement of protein
expression and modifications in samples. Although serum is
generated by coagulation, its proteome is vulnerable to the
proteases involved in this cascade, as well as to those involved in
the complement cascade, activated on blood clotting. Various
preanalytical parameters, such as the sampling device used, the
clotting temperature and time, the storage duration and
temperature, and the incubation temperature and handling, have proven
the importance of uniform handling to exclude systematic
preanalytical inconsistency. Thus, false discovery can exert a distinct
influence on the serum proteome, potentially leading to differences
between results even when comparable patient populations and
sample types are studied
(Hsieh et al, 2006; Timms et al, 2007;
Engwegen et al, 2008; Gast et al, 2009; Zeidan et al, 2009)
proteomic technologies are developing rapidly, which promote
large-scale sample screening and facilitate identification of proteins
associated with disease and treatment
(Diamond et al, 2006; Smith
et al, 2006)
. Mass spectrometry (MS), the most powerful
proteomics tool, has evolved to a high-throughput level and
improved to an extent that currently allows rapid and accurate
analysis of several thousand proteins in a single study
(Hirsch et al,
2004; Nilsson et al, 2010)
. Gel-free MS-based ?shotgun? quantitative
proteomics, a commonly used approach, is more sensitive and
accurate relative to two-dimensional gel electrophoresis-based
(Tan et al, 2012)
. SDS?PAGE, followed by in-gel
digestion, is a protein separation technique based on molecular
weight (George et al, 2015). Surface-enhanced laser desorption/
ionisation time-of-flight MS (SELDI-TOF-MS) based on the
selective binding of proteins with different physicochemical
properties on protein chip arrays has been successfully applied to
uncover crucial molecular events in many tumour types, such as
(Li et al, 2002; Rui et al, 2003; Laronga et al, 2003?2004;
Clarke et al, 2005; Hu et al, 2005)
(Kim et al, 2002; Feng et al,
2007; Zinkin et al, 2008)
(Li et al, 2003; Xiao et al, 2003?2004;
Au et al, 2008)
(Adam et al, 2002; Qu et al, 2002; Lin et al,
and ovarian cancers
(Gagnon et al, 2008)
. More recently,
a new high-throughput workflow with matrix-assisted laser
(MALDITOF/TOF MS) based on magnetic beads with different chemical
chromatographic surfaces was established for the effective
discovery and identification of serum peptides
(Villanueva et al,
, whereby proteins selectively bound to magnetic beads are
eluted and analysed with MALDI-TOF MS. Using this system, we
identified novel biomarkers for papillary thyroid carcinoma, Wilms
tumour and breast cancer
(Fan et al, 2009, 2010; Wang et al, 2012)
of which the utility has not been assessed in relation to the
commonly used clinical markers.
Plasma-based tumour markers, including CEA
(carcinoembryonic antigen), CA (carbohydrate antigen) 19-9 and CA125, are
clinically applied to monitor GC
(Sakamoto et al, 1987)
markers have shown utility in detecting disease recurrence after
(Pectasides et al, 1991)
, but display low sensitivity,
lack specificity and often are not reproducible
(Haglund et al, 1991;
Kochi et al, 2000; Ychou et al, 2000; Oue et al, 2009)
. Although the
combined use of these biomarkers has led to improved results to
some extent, their value is still far from ideal
(Aloe et al, 2003; Ucar
et al, 2008)
. Several groups have employed quantitative proteomic
approaches to identify novel secreted biomarkers of GC in the
secretome and plasma
(Chong et al, 2010; Yang et al, 2010; Loei
et al, 2012; Marimuthu et al, 2013)
. Identification of novel and
specific GC serum biomarkers for screening purposes is an urgent
clinical requirement. The current study, with an independent test
set from a second hospital, aimed to screen for reliable protein
biomarkers from matched serum samples (preoperative,
postoperative and relapsed) with SELDI-TOF-MS, followed by protein
identification using MALDI-TOF/TOF MS, immunoassay-based
protein confirmation and comparison with three representative
conventional markers (CEA, CA19-9 and CA125) using receiver
operating characteristic (ROC) and survival curve analyses to
ascertain their potential value as diagnostic and prognostic
biomarkers for GC.
MATERIALS AND METHODS
Patients and serum samples. Human serum samples were used
based on Institutional Review Board-approved protocols, and
written patient consent obtained where appropriate. A total of 432
serum samples from 296 individuals were included from the
Division of General Surgery, the First Affiliated Hospital of
Zhengzhou University, China, from June 2010 to March 2015
(mining set) and the Third Affiliated Hospital of Zhengzhou
University, China, from May 2010 to January 2015 (testing set).
The mining set consisted of 120 preoperative serum samples from
120 patients with GC, 106 corresponding postoperative serum
samples (surgical treatment was either not conducted or
abandoned due to financial constraints for 14 patients), 30
corresponding relapsed serum samples and 64 individuals,
including 17 with gastritis and 47 healthy donors, as control.
The testing set consisted of 60 preoperative serum samples from 60
patients with GC and 52 individuals, including 19 with gastritis
and 33 healthy donors, as the control for the second study step.
Meanwhile, fresh-frozen GC epithelium tissue samples from 75
patients were randomly obtained from the First and Third
Affiliated Hospital of Zhengzhou University, whereas samples
from 15 gastritis and 60 healthy donor fresh-frozen tissue were
used as control. Patients were staged according to the World
Health Organization, Lauren?s classification and International
Union against Cancer (UICC) TNM system. Details of
clinicopathological data are summarised in Table 1. Patients subjected to
curative intent resection were classified according to the following
criteria: complete removal of primary gastric tumour, D2
dissection of regional lymph nodes, absence of macroscopic
tumours remaining at the site of resection and absence of
metastases in the liver, lungs or distant organs at the time of
surgery. Postoperative follow-up visits were performed every 3
months for the first 2 years, followed by every 6 months up to 63
months or death. As of April 2015, complete periodic follow-up of
all patients was recorded (Figure 2A).
In addition, patients should have been clear of concomitant
primary cancers and not received chemotherapy or radiotherapy
before sample collection. All participants were pre- or
postoperatively histologically verified with adenocarcinoma or gastritis
via gastroscopy biopsy or histopathological examination by more
than two senior pathologists. The benign lesion and healthy donor
groups were age- and gender-matched with the GC group. In
addition, serum levels of CEA, CA19-9 and CA125 were measured
using commercial enzyme immunoassay kits with cut-off values set
No. of patients
at 5 ng ml 1, 37 U ml 1 and 35 U ml 1, respectively. All
overnight fasting serum samples from cancer and cancer-free
individuals were collected in vacutainer tubes. Nutritional therapy
of GC and control individuals at 72 h before blood sampling was
based on lower insulin-secreting low-calorie, low-fat and high-fiber
diet preparations. The duration of preoperative total parenteral
nutrition preparatory phase ranged between 5 and 10 days (8 days
on average). Enteral nutrition with elementary commercially
available diet was begun 20 h after surgery, which was continued
for 6 days; it was started at an 8 ml h 1 flow rate and increased
gradually, with the volume doubled every 24 h, up to 100 ml h 1.
During the initial 5 days after surgery, the patients were
additionally supplemented parenterally via peripheral veins, similar
to the preoperative period. As a routine pre-treatment examination
at admission by our hospital laboratory, no significant differences
were evident pertaining to the plasma lipid profile including
highdensity lipoprotein-cholesterol, low-density
lipoprotein-cholesterol, cholesterol (CHO) and triglycerides (TAG) with cut-off
values set at 0.91 mmol l 1, 3.61 mmol l 1, 5.20 mmol l 1 and
1.70 mmol l 1, respectively in GC vs control from both the mining
and testing sets (P40.05; Supplementary Table 1). Pre-analytical
parameters, such as sampling device, clotting temperature and
time, storage temperature and duration and, incubation
temperature and handling, were controlled by following a standard
protocol that was the same for cases and controls. Normal, benign
gastric disease and GC serum samples were depleted of
highmolecular weight proteins by acetonitrile precipitation. Sera were left
at room temperature for 1 h, centrifuged at 3000 r.p.m. for 10 min,
and stored at 80 1C. For immunological confirmation of
apolipoprotein C-III (apoC-III) and b-actin, a similar procedure
using rabbit anti-apoC-III and anti-b-actin antibodies (Cat. #
sc50377, Santa Cruz Biotechnology, Santa Cruz, CA, USA), one of the
most authoritative antibody-producing suppliers at a dilution of
1 : 400 overnight at 4 1C, respectively, was employed. Details of
SELDI-TOF-MS analysis, bioinformatic analysis, fractionation,
identification and immunoassay-based confirmation of the
candidate protein biomarkers are presented in online Supplementary File.
Statistical analysis. Data analysis was performed using the
Zhejiang University Cancer Institute-ProteinChip Data Analysis
System (ZUCIPDAS). The undecimated discrete wavelet transform
method was applied to denoise the signals using Rice Wavelet
Toolbox v. 2.4. Baseline correction was achieved by aligning
spectra with a monotone local minimum curve, and mass
calibration carried out by adjusting the intensity scale to the three
peaks present in all the spectra. The peaks were filtered to maintain
a signal-to-noise ratio (SNR) 43, whereby SNR was defined as the
ratio of the height of the peak above baseline to the wavelet-defined
noise. To distinguish between data from different groups, we used
a non-linear support vector machine (SVM) classifier, originally
developed by Vladimir Vapnik, with a radial-based function kernel,
parameter Gamma of 0.6 and cost of constraint violation of 19. The
leave-one-out crossing validation approach was applied to estimate
the accuracy of this classifier. The capability of each peak in
distinguishing data of different groups was estimated based on the
P-value of Wilcoxon t-test.
Quantitative variables, presented as mean?s.d., were analysed
with unpaired Student?s t-test. Categorical variables were assessed
using Pearson w2 test. ROC curves were utilised to assess the
diagnostic value of CEA, CA19-9, CA125 and the candidate protein
biomarker. Survival curve analysis was performed using the Kaplan?
Meier method, and significant levels assessed with the log-rank test.
P-values were two-sided, and statistical significance set at Pp0.05.
Serum protein profiles and data processing. Serum samples
from the mining set were analysed and compared using
SELDITOF-MS with the WCX2 chip. All MS data were
baselinesubtracted and normalised using total ion current, and peak
clusters generated with Biomarker Wizard software. Wilcoxon
rank sum tests to determine relative signal strength disclosed 15
differentially expressed proteins, including 9 upregulated and 6
downregulated protein peak intensities from samples of
preoperative GC patients, compared with controls (Supplementary Table 2).
Thirteen differentially expressed proteins, including 10 upregulated
and 3 downregulated protein peak intensities, were observed for
postoperative GC patients, compared with preoperative GC
patients (Supplementary Table 3), and 15 differentially expressed
proteins, including 9 upregulated and 6 downregulated protein
peak intensities for relapsed GC patients, compared with
postoperative GC patients (Supplementary Table 4). From the random
combination of protein peaks with remarkable variation, the SVM
screened out the model with maximum Youden index of the
predicted value, leading to the identification of a marker positioned
at 6449 Da with continuous dynamic presence in the control,
preoperative, postoperative and relapsed GC patient sera. The
6449 Da protein was remarkably elevated in preoperative GC
patient sera, compared with the control, but decreased in
postoperative sera and upregulated again in relapsed GC samples
(493.010?163.037 vs 1563.664?1080.217 vs 644.712?342.500 vs
1247.915?157.747; Po0.05; Figure 1A and B). In addition, the
6449 Da protein level progressively increased through clinical
stages I, II, III and IV (649.724?620.964 vs 1506.884?1036.531 vs
2229.2995?2099.2703 vs 3081.431?2063.393; Po0.05; Figure 1C
and D). Using leave-one-out cross-validation (LOOCV), the
6449 Da fragment was used to effectively distinguish GC from
controls with an accuracy of 85.3% (157 out of 184), sensitivity of
85.0% (102 out of 120) and specificity of 85.9% (55 out of 64).
Protein peak validation. The remaining 60 GC and 52 control
serum samples (33 healthy donors and 19 with benign gastritis)
were analysed as a blind testing set to validate the accuracy and
validity of the 6449 Da protein marker identified from the mining
set. The peak intensity of the 6449 Da protein was higher than that
of control (486.494?157.816 vs 1498.369?1075.437; t ? 6.719,
Po0.0001; Figure 2B). The 6449 Da marker distinguished GC
samples from controls with an accuracy of 84.8% (95 out of 112),
sensitivity of 86.7% (52 out of 60) and specificity of 82.7%
(43 out of 52).
6,000 6,200 6,400 6,600 6,800 7,000
6,000 6,200 6,400 6,600 6,800 7,000
P = 0.302
200 300 400 500 600 700 800 900 1,000 1,100
In-gel digestion and MALDI-TOF/TOF-MS identification of the
candidate protein biomarker. Protein spots positioned at
6449 Da were excised from the gel with the Ettan Spot Picker,
followed by digestion with trypsin and MALDI-TOF/TOF analysis
(Figure 2C). The sequence of proteins and peptide segments with
m/z of 6449 Da was
E.MQPRVLALLASADASLLSFMYMKHATKTAKDVQVAQQAR.G (complete sequences are not listed owing to
patent pending). Subsequent analysis using the MASCOT search
programme and NCBI database led to identification of the peptide
segment as apoC-III, with a matching rate of 59.3% and matching
score of 104.9 points (Table 2).
Confirmation of candidate protein biomarker expression using
western blot (WB) and ELISA. As full-length human apoC-III is
10.9 kDa, the m/z 6449 Da biomarker peptide with total sequence
coverage 459.3% represents the large fragment of apoC-III.
Immunological analysis of apoC-III was performed using an
available antibody purchased from authoritative
antibody-producing supplier (Santa Cruz Biotechnology), specific for epitope
corresponding to amino acids 25?99 mapping at the C-terminus of
apoC-III of human origin covering 490% of the complete
identified sequence. To further investigate the expression of
apoC-III deduced from the results of SELDI-TOF-MS, WB was
performed using serum specimens from 30 controls (15 with
gastritis and 15 healthy donors), matched GC-pre, GC-pos and
GC-rel patients (Figure 2D). Data were consistent with proteomic
findings. The protein biomarker with b-actin used as the loading
control was remarkably elevated in GC-pre sera, compared with
control, but decreased in GC-pos and upregulated again in GC-rel
(0.306?0.138 vs 1.075?0.123 vs 0.512?0.148 vs 0.778?0.106;
Po0.05; Figure 2E). ELISA data on apoC-III levels in the same
serum samples were consistent with WB findings (0.516?0.182 vs
2.043?0.940 vs 1.165?0.442 vs 1.518?0.309; Po0.05; Figure 2F),
whereby optical density ratios of apoC-III following normalisation
to corresponding control were calculated and used as the vertical
scale. As apoC-III expression is confined to the liver and the
intestine (and possibly adipose tissue), apoC-III expression in the
gastric epithelium from 75 GC and 75 control individuals (15 with
gastritis and 60 healthy donors) was confirmed via WB and ELISA,
in which no significant differences were detected between GC and
control individuals, respectively (WB: control, 0.175?0.027 vs.
GC, 0.182?0.078; t ? 0.089, P ? 0.934; Supplementary Figure 1A
and B and ELISA: control, 0.267?0.031 vs. GC, 0.368?0.196;
t ? 1.789, P ? 0.076; Supplementary Figure 1C).
Diagnostic and prognostic value of the candidate protein
biomarker, compared with CEA, CA19-9 and CA125. Relative
intensities of the 6449 Da peak, together with CEA, CA19-9 and
CA125 data in the testing set were used to determine diagnostic
and prognostic value. Sensitivities were determined from the
results of 60 patients with GC and specificities from 52 controls (19
with gastritis and 33 healthy donors). Taking the histologically
verified results in patients as the golden standard, the diagnostic
value of CEA was assessed by means of an ROC curve, the area
under the curve value of which was calculated as 0.695 (95% CI:
0.597?0.793; Po0.0001; Figure 3A), compared with the control.
ROC curves of CA19-9, CA125 and the 6449 Da peak were
0.610 (95% CI: 0.505?0.714; P ? 0.046; Figure 3A), 0.617 (95%
CI: 0.513?0.721; P ? 0.033; Figure 3A) and 0.914 (95% CI: 0.870?
0.957; Po0.0001; Figure 3A), respectively. ROC curves of the
CEA, CA19-9 and CA125 combination and the CEA, CA19-9,
CA125 and 6449 Da peak combination were 0.780 (95%
CI: 0.696-0.863; Po0.0001; Figure 3B) and 0.959 (95% CI:
0.921?0.997; Po0.0001; Figure 3B), respectively. The cut-off
level (1498.369 a.u.) for the 6449 Da peak was set at mean of data
obtained in 60 patients with GC. CEA levels were o5 ng ml 1 in
27 cases, CA19-9 levels were o37 U ml 1 in 24 cases and CA125
levels were o35 U ml 1 in 24 cases. In 15 cases, CEA, CA19-9
and CA125 levels were not significantly elevated. Notably,
however, the 6449 Da peak was above the cut-off level in 9 of
the 15 cases. Thus, measurement of the peak may be used as a
complement to CEA, CA19-9 and CA125 assessment in nine
cases where the conventional tumour markers were not
diagnostic. Sensitivity and specificity of combined CEA,
CA19-9 and CA125 were 36.7% (22 out of 60) and 48.1%
(25 out of 52), while a combination of CEA, CA19-9, CA125 and
6449 Da presented 88.3% (53 out of 60) sensitivity and 84.6%
(44 out of 52) specificity, respectively.
No significant differences in survival times were evident
between GC patients with CEA levels greater than or equal to
and those with CEA lower than cut-off values (35.3 months vs 39.8
months; HR 1.731, 95% CI 0.912?3.285; w2 ? 2.815, P ? 0.093;
Figure 3C) in Kaplan?Meier survival analysis. Similar results were
obtained for CA19-9 (35.3 months vs 39.0 months; HR 0.964, 95%
CI 0.505?1.838; w2 ? 0.013, P ? 0. 911; Figure 3D) and CA125
(38.9 months vs 45.8 months; HR 1.169, 95% CI 0.625-2.232;
w2 ? 0.247, P ? 0.619; Figure 3E). In contrast, survival times for GC
patients with 6449 Da peak intensities greater than or equal to
average relative intensity values were shorter than those of GC
patients with peak intensities lower than the average relative
intensity values (30.0 months vs 44.3 months; HR 2.233, 95% CI
1.263?4.679; w2 ? 6.680, P ? 0.009; Figure 3F), clearly indicating that
higher 6449 Da peak intensity is an unfavourable prognostic factor.
Patient blood samples are an ideal source of disease biomarkers
owing to their ease of access. Several studies have identified
potential candidates, but few have overcome validation and
reproducibility issues to achieve clinical application
To our knowledge, this is the first study to characterise an apoC-III
fragment as a potential diagnostic and prognostic biomarker based
on MS across a broad spectrum of GC sera with an independent
test set from a second hospital. We used protein chip MS to
identify a unique serum protein positioned at 6449 Da with
continuous dynamic presence in control, preoperative,
postoperative and relapsed GC sera that effectively discriminates
between sera from GC patients and healthy volunteers with high
sensitivity and specificity in the mining set. The 6449 Da protein
panel screened using a SVM was significantly upregulated in
preoperative GC, decreased in postoperative sera and upregulated
again in relapsed GC, compared with control, as validated in an
independent testing set. In one previous study by
Cohen et al
), the 9.4 kDa protein, identified as apoC-III was decreased in
pre-operation stomach cancer sera as compared with control
groups. The inconsistency with Cohen research team may be
caused by socio-geographical difference and the instability of this
protein, leading to further truncation during prolonged storage.
Gast et al (2009), found that over long storage duration the
intensity of some vulnerable proteins would decrease because of
fragmentation, while the intensities of the fragments of these
proteins would increase. Samples of both cases and controls
analysed in the study by Cohen et al, were collected respectively
from an unknown time on whereas samples of both case and
control groups from the mining and testing sets in our study were
all collected within the same 5-year window. Another, possibly
more likely explanation for the discrepant result is difference in
pre-analytical sample handling to which some proteins are more
vulnerable. Consistent overnight fasting samples of both cases and
controls from both mining and test sets analysed in our study were
pre-analytically handled identically following a standard protocol.
Samples were allowed to clot for 1 h before they were centrifuged
for 10 min at 3000 rpm at room temperature. All blood samples
with significantly distinctive fasting data (overnight fasting vs
unknown fasting) from two commercial sources analysed in the
study by Cohen et al, were allowed to clot for 30 min before they
were centrifuged for a variable duration at inconstant
centrifugation speed. We did not find a convincing explanation for this
discrepant result, but it probably illustrates the above-mentioned
susceptibility of apoC-III to external circumstances. For future
studies, more effort should be put into the collection of blood
samples of cases and controls with the use of more rigorous
standardised and higher quality procedures. Remarkably, a increase
of this protein was also found in two other GC sample-sets analysed
by our group (publication in preparation). Although the specificity
of the candidate protein biomarker using the method of LOOCV
was somewhat lower in the testing than mining set, sensitivity was
slightly higher in the testing set. In addition to the above dynamic
changes in preoperative, postoperative and relapsed GC sera, the
peak intensity of 6449 Da progressively increased with higher clinical
stages, supporting the theory that excessive levels contribute to
tumour progression. Using a combination of MS and immunological
methods, the protein was identified as a fragment of apoC-III, a
small 99 amino acid protein that forms a component of TAG-rich
very-low-density lipoproteins and high-density lipoproteins.
Recent reports have indicated that apolipoprotein levels in blood
can be used as potential biomarkers for different cancers. Apo-A1,
a regulator of tumour growth and metastasis, has been shown to be
involved in antiproliferative and proapoptotic activities via
regulation of cancer cell differentiation
et al, 2013; Kim et al, 2014)
. Hsu et al (2013) recently
demonstrated a correlation of dyslipidemia-associated
apoA1 minor allele with unfavourable baseline characteristics in
Taiwanese breast cancer patients, and the 10-year follow-up
revealed poorest survival in patients carrying both minor alleles in
the lymph node-negative group. ApoC-I was identified as a
potential serum biomarker for colorectal cancer,
hormonerefractory prostate cancer and liver fibrosis
(Engwegen et al,
2006; Yamamoto-Ishikawa et al, 2009)
. Emerging roles of apoC-III
synthesised in the liver, and to a minor degree, the small intestine,
include directing the atherogenicity of high-density lipoprotein,
intestinal dietary TAG trafficking and modulating insulin-secreting
pancreatic beta-cell survival and apoptosis via activation of the
mitogen-activated protein kinases p38 and extracellular signal
regulated protein kinases 1 and 2
(Sol et al, 2009; Kohan, 2015)
resulting in ephemeral hyperglycaemia which is difficult to detect
abnormal glucose levels through traditional blood glucose monitoring
means, a positive effect on GC progress via aquaporin 3 and risk
factor for GC posed by Helicobacter pylori infection due to
(Ikeda et al, 2009; Zhou et al, 2015)
. There is still
confusion, however, about the precise role of apoC-III in pancreatic
beta-cell failure. Other reports indicate that apoC-III is a potential
biomarker in pancreatic and breast cancer
(Chen et al, 2007;
McComb et al, 2007)
. However, these earlier reports employed
MALDI-based screening and did not verify results obtained with
immunoassay-based experiments or comparisons with representative
conventional markers. Our WB and ELISA data confirmed that
apoC-III is dynamically regulated in matched GC-pre, GC-pos and
GC-rel patient sera, consistent with proteomic findings, suggesting
that lipoprotein metabolism is dysregulated in GC. In addition,
apoCIII expression in the gastric epithelium in GC vs control was
confirmed via WB and ELISA. Further studies to determine whether
apoC-III is secreted from GC cells and the molecular mechanisms
underlying apoC-III-mediated GC progression are warranted.
Moreover, an earlier study using matrix-assisted laser desorption/ionisation,
time-of-flight and tandem MS reported that apoC-III could be
suppressed by the serine?threonine kinase receptor?associated protein
(STRAP), which promotes growth, and enhances tumourigenicity
(Anumanthan et al, 2006)
Diagnosis based on measurement of a panel of biomarkers is
more reliable than a single marker test owing to the multifactorial
nature of cancer. Comparison of the diagnostic ability of the
apoCIII fragment, determined based on ROC analysis, with the current
standard GC diagnostic biomarkers (CEA, CA19-9 and CA125)
revealed that the candidate protein biomarker is distinctly superior
to all three standards, either individually or in combination, in
distinguishing patients with GC from healthy individuals. Notably,
the 6449 Da peak was elevated in 9 of 15 cases in which CEA,
CA19-9 and CA125 were below cut-off levels, suggesting that the
6449 Da peak may be used as a complement to CEA, CA19-9 and
CA125 in detecting GC, consistent with two previous reports using
(Qian et al, 2005; Su et al, 2007)
. Similar to
diagnosis results, Kaplan?Meier survival analysis disclosed that this
6449 Da protein fragment represents a new stronger potential
prognostic factor for GC, compared with the three representative
conventional markers, indicating that measurement of peak
intensity positioned at 6449 Da in serum should improve
estimation of postoperative survival chances for these GC patients.
However, the identity of only a small proportion of the detected
peaks has been confirmed, and the roles of many of the identified
proteins in GC development are not yet known. Moreover, reliable
validation of the potential GC biomarker in larger sample cohorts
is necessary for translation to clinical application. The issue of
whether a causal link exists between GC and abnormal lipid
metabolism is yet to be established, and complicated by the fact
that the detected peak may be associated with the inflammation
state and not specific for GC.
We are grateful to Professor Lijun Wang from the School of
Foreign Languages of Zhengzhou University for language editing
and polishing of this paper. This work was supported by a grant
(no.81172085) from the National Natural Science Foundation of
CONFLICT OF INTEREST
The authors declare no conflict of interest.
Supplementary Information accompanies this paper on British Journal of Cancer website (http://www.nature.com/bjc).
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