American Journal of Anal yt ical Chemistry, 2011, 2, 142-151
doi:10.4236/ajac.2011.22016 Published Online May 2011 (
Copyright © 2011 SciRes. AJAC
Metabolomics Analysis of the Responses to Partial
Hepatectomy in Hepatocellular Carcinoma Patients
Wan Chan1,4, Shuhai Lin1,4, Stella Sun2, Hongde Liu3, John M. Luk2,*, Zongwei Cai1,*
1Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR, China
2Department of Surgery, LKS Faculty of Medicine, Jockey Club Clinical Research Center,
The University of Hong Kong, Hong Kong SAR, China
3State Key Laboratory of Bioelectronics, Southeast University, Nanjing, China
4These authors contributed equally to this work
Received September 16, 2010; revised December 7, 2010; accepted March 3, 2011
In this study, liquid chromatography/quadrupole time of flight mass spectrometry (LC/QTOFMS) was em-
ployed for investigating the metabolome of the sera collected from hepatocellular carcinoma (HCC) patients
before and 3 to 5 months after partial hepatectomy. To investigate the changes in metabolic phenotypes after
the hepatic resection, principal components analysis (PCA) and support vector machine (SVM) were per-
formed for the data grouping and classification. Based on the obtained SVM model, mass spectrometry spec-
tra, database searching as well as the confirmation from authentic standards, several differentiating metabo-
lites were tentatively identified. To improve visualization, z-score plot and heat map display were performed,
which exhibited the changes in concentration of the metabolites. As a result, depletion of circulating car-
nitine, reduced amino acid biosynthesis and increased rate of lipid peroxidation were observed. Meanwhile,
up-regulation of hypoxanthine indicated that purine metabolism might serve as the salvage pathway. Collec-
tively, the results reflected metabolic responses to surgical operation in HCC patients, suggesting perturba-
tion of energy metabolism may occur in 3 to 5 months after the partial hepatectomy.
Keywords: Metabolomics Analysis, Partial Hepatectomy, Hepatocellular Carcinoma, LC-MS
1. Introduction
Metabolite profiling was regarded to reveal metabolic
changes in living systems, tissues or cell lines. The ap-
plications of the relatively new technique have been ex-
ponentially increased in numerous fields, including toxi-
cology, pharmacology, and clinical diagnostics [1]. Stu-
dies of the identities, concentrations and fluxes on these
small molecules in cells, tissues and biological fluids can
enhance understanding of the mechanisms involved in
pharmacology and disease pathophysiology [1-3]. Me-
tabolomics not only involves the “quantitative determi-
nation of targeted metabolite profiling of a complex
metabolic response”, but also the “qualitative determina-
tion of comprehensive or untargeted metabolite profil-
ing” of the pathophysiological process or genetic deter-
minants in living systems [4]. Therefore, metabolic pro-
filing has been extended to the investigation and dis-
cover of new biomarkers for clinical diagnosis and the
evaluation of therapeutic efficacy [5-7].
Metabolomics utilizes techniques that can simultane-
ously quantify thousands of small molecules in a bio-
logical sample. This analytical capability must then be
joined to sophisticated mathematical tools that can iden-
tify a molecular signal among millions of pieces of data
[8]. A large number of metabolites occurring with very
diverse physico-chemical properties and different abun-
dance levels require the powerful analytical capacity [9].
Chromatography coupled with mass spectrometry has
been demonstrated for its vast potential as a tool for this
type of investigation. As a typical LC/MS analysis may
involve hundreds to thousands of peaks, data extraction
and analysis are important for obtaining the relevant
biomarker information. The candidate peaks are then
further analyzed with sophisticated mathematical tools
and structurally characterized for the identification of
biomarkers [10]. Treatment of the acquired mass spec-
trometry data from different tested groups could be con-
ducted by using multivariate data analysis. Principal
components analysis (PCA), an unsupervised projection
method for visualization of the dataset and display the
similarity and difference, is widely applied in me-
tabolomic analysis. Partial least squares (PLS), the re-
gression extention of PCA such as PLS-DA, has been
used as a means of data filtering and feature selection
[11]. Support vector machine (SVM) as the supervised
powerful machine learning can be extended to nonlinear
cases with the help of kernels. SVM has been shown as a
better machine learning to PLS-DA in both cases in
terms of predictive accuracy with the least number of
features [12-14].
More and more evidences have demonstrated that me-
tabolomics study is a promising approach for liver dis-
eases diagnosis. Most of the reported metabolomic stud-
ies focused on comparing liver disease with non-malig-
nant or healthy subjects. For instance, liver cancer has
been discriminated with HPLC analysis avoiding false-
positive result from hepatitis and hepatocirrhosis dis-
eases [15]. Biochemical perturbation of liver function
caused by hepatitis B virus was characterized by LC/MS
and GC/MS [16,17]. Low-grade human hepatocellular
carcinoma tumors have been differentiated from high-
grade ones based on high-resolution magic-angle spin-
ning (MAS) 1H nuclear magnetic resonance spectroscopy
[18]. To the best of our knowledge, however, little is
known about the application of metabolomics for the
evaluation of prognostic metabolite profiling in HCC
patients after hepatic resection. In the present study,
LC/QTOFMS was conducted for non-targeted metabolo-
mics to screen metabolic changes in the sera of HCC
patients collected before and 3 to 5 months after hepa-
tectomy. In addition to PCA and SVM introduced for
pattern recognition and data mining, two other appoaches
were also implemented, namely hierarchical clustering
heat map and z-score plot for improving visualization.
The multivariate data analysis provided a powerful tool
for understanding the underlying metabolic responses in
HCC patients through surgical operation.
2. Materials and Methods
2.1. Sample Preparation
The sera were harvested from ten HCC patients pre- and
post-hepatectomy in fasting conditions at Queen Mary
Hospital, The University of Hong Kong. The study was
approved by the Ethics Committee in Hong Kong. All
patients are male with the age of 37 - 59 years old. Sam-
ples were stored at 80˚C and then thawed before analy-
sis. 400 μL acetonitrile was added to 200 μL of each se-
rum sample and vertex vigorously. The sample mixture
was allowed to stand for 5 min and centrifuged at 13,000
rpm for 5 min at 4˚C. The supernatant was lyophilized
with the addition of 400 μL Milli-Q water. 50 μL of ace-
tonitrile/water mixture (1:1) was added to the sera sam-
ples, vortex mixed and centrifuged prior to the LC/MS
analysis. L-carnitine, acetylcarnitine, hypoxanthine and
amino acids (proline, valine, leucine and isoleucine)
were obtained from Sigma (St. Louis, MO, USA).
2.2. LC/MS and LC/MS/MS Analyses
Chromatographic separation was performed on an HP
1100 HPLC system (Agilent Technologies, Palo Alto,
CA, USA) equipped with a 150 mm × 2.1 mm Symmetry
C18 reversed-phase column with particle size 3.5 μm
(Milford, MA, USA). Aliquots of 3 μL each sample were
injected onto the HPLC column for analysis. A linear
gradient at a flow rate of 300 μLmin1 was used con-
taining solvents A (0.1% formic acid in water) and B
(acetonitrile). Initial conditions were 5% B, held for 5
min, increased to 95% B from 5 to 20 min, and held for 4
min, then decreased to 5% B in 1 min and re-condition-
ing for 10 min to the initial conditions. The serum sam-
ple pairs were run in random order and in duplicate. To
minimize the cross contamination, a blank run was in-
serted between the consecutive samples.
A QTOFMS (API Q-STAR Pulsar i, MDS Sciex, To-
ronto, Canada) was employed for the analysis of metabo-
lite ions in positive mode. TurboIonspray parameters for
ESI-MS were optimized as follows: ionspray voltage (IS)
4500 V, declustering potential (DP) 30 V, declus-
tering potential (DP) 15 V, focusing potential (FP)
80 V. The mass range was from 100 to 1,000 m/z. The
ion source gas (GS), gas (GS), curtain gas
(CUR) and collision gas (CAD) were set at 30, 15, 30
and 3, respectively. The temperature of GS was set at
350˚C. Renin substrate tetradecapeptide at 10 pmolμL1
was used for the calibration of the mass spectrometer.
Data acquisition and processing was performed based on
Analyst QS software (service pack 7).
To obtain structural information of the metabolites,
information-dependent acquisition (IDA) mode was used
to acquire the MS/MS spectra of the metabolites in se-
rum samples. Using the IDA in Analyst QS (Applied
Biosystems/MDS Sciex), MS/MS analysis at different
collision energies (CEs) were performed. For the product
ion scans, the resolution of the mass resolving quadru-
pole (Q1) was set low (4 amu window). The ion peaks
with peak intensity exceeding 50 counts in the survey
scan triggered MS/MS analyses. Former target ions that
had been selected for MS/MS analyses were excluded
from MS/MS analyses for 30 s.
Copyright © 2011 SciRes. AJAC
Copyright © 2011 SciRes. AJAC
2.3. Multivariate and Univariate Statistical
The LC/MS chromatograms were imported into “Me-
tabolomics ExportScript” (Applied Biosystems/MDS
Sciex) for peak finding, alignment and filtering. LC/MS
data were processed by using the criteria as described
previously [19]. In the current work, nonlinear PCA as
well as SVM with radial-basis-function (RBF) kernel
were presented to map the classification. Here, T-test
score and separation score were used in SVM model to
rank each feature (metabolite ions). Moreover, the peak
areas of the metabolite ions were integrated from ex-
trated ion chromatograms prior to heat map display and
z-score plot. All the scripts were performed in MATLAB
R2008a software (The MathWorks, Inc., Natick, MA,
The one-way analysis of variance (One-Way ANOVA)
was utilized to compare the serum samples of post-
hepatectomy to pre-hepatectomy for up- or down-regu-
lated metabolites. The P values with Bonferroni correc-
tion were obtained by OriginPro 8 software (OriginLab,
Co., MA). Human metabolome database (http://www. (briefly HMDB) [20] the metlin metabolite
database ( [21] and KEGG da-
tabase ( [22] were
used for metabolite identification and mapping.
3. Results
The schematic flowchart of the metabolomic approach
performed in this study was illustrated in the Figure 1.
Briefly, the metabolites from sera were extracted and
applied to HPLC coupled with electrospray ion source in
positive mode, with the QTOFMS data acquired from
100 to 1,000 m/z. One typical total ion chromatogram
(TIC) is also inserted. The feature tables from the TICs
were yielded consisting of m/z value, retention time, and
integrated intensity using “Metabolomics ExportSript”
software. Feature selection was generated from SVM
model during data training for metabolite discovery. The
metabolite ions were imported into PCA and SVM using
MATLAB programme. For the identification of metabo-
lites of interest, accurate mass data were searched against
databases of known metabolites, such as Metlin, HMDB.
A match was tested by fragmentation data from
QTOFMS in comparison to authentic standards. Finally,
the interpretation of metabolic signature and visualize-
tion in z-score plot and cluster heat map were performed.
As a result, the metabolite ions which were statistically
significant were selected as differentiating metabolites
listed in Table 1.
Figure 1. Schematic flowchart of the reported metabolomic study.
Table 1. Statistical differentiating metabolites.
No. m/za (RT, min) productions, m/z metabolites Fold-change P value
1 203.0503 (1.25) Un-known 2.82b 7.19E-5
2 162.1146 (1.32) 103, 85, 60 carnitine 3.03 1.76E-5
3 204.1170 (1.35) 85, 60 acetylcarnitine 4.06 0.0071
4 116.0680 (1.28) 70 proline 1.78 0.033
5 118.0821 (1.28) 72 valine 1.83 0.0089
6 132.0738 (1.28) 86 leucine/isoleucine 2.37 0.0012
7 137.0409 (1.35) 119, 110 hypoxanthine 2.35 0.015
8 494.3157 (19.83) 104,184 LysoPCc (16:1) 2.11 0.011
9 496.3260 (21.45) 104,184 LysoPC (16:0) 1.26 0.031
10 468.3114 (19.4) 104, 184, 450 LysoPC (14:0) 2.23 0.0062
11 520.3364 (20.5) 104, 184, 502 LysoPC (18:2) 2.25 3.97E-4
12 522.3429 (21.88) 104, 184 LysoPC (18:1) 1.55 0.0072
13 546.3537 (22.20) 104, 184 LysoPC (20:3) 2.26 0.0026
14 782.5476 (25.05) 184 PEd(22:2/18:1)/ isomers 1.49 0.0139
15 806.5580 (25.40) 184 PCe(18:2/20:4)/ isomers 1.29 0.014
aPositive ion mode; bpost-hepatectomy versus pre-hepatectomy up-regulation () down-regulation (); cLysoPC: lysophosphatidylcholine; dPE: phophati-
dylethanolamine; ePC: phosphatidylcholine.
3.1. Multivariate Statistics
The separation from kernel PCA was obtained from the
first two components. In Figure 2, PC1 accounts for
89.61% of the total variation and PC2 for 6.65%. The
result showed that the partial hepatectomy had a small
effect in such a short time and four post-hepatectomy
samples (namely post-1, -3, -4 and -9) had not been se-
parated from the pre-hepatectomy group. Therefore, we
introduced the supervised learning method further. SVM
with RBF kernel was performed for data training in Fig-
ure 3, then feature selection was also finished based on
the model. It was well-known that the evaluation for the
classification accuracy was of great importance. Two
parameters (C and γ) were used for determination by
using the grid search strategy [24]. Because there are
only two independent parameters for parallel searching
rather than iterative processes, the computational time
required would be not much more than that by other ad-
vanced methods (e.g. walking along a path). Conse-
quently, the grid search on C and γ using cross-validation
was also performed shown in Figure S1. The parameters
(C and γ) in SVM construction were chosen with the
cross-validation rate 85%. It should be noted that when
the cross-validation rate is higher, the prediction accu-
racy is higher. Considering metabolomic variations, such
personalized responses to a particular therapy-partial
hepatectomy, was displayed in z-score plot (Figure S2)
and cluser heat map (Figure S3). Carnitine as the most
significantly decreased metabolite in post-hepatectomy
data set was highlighted in red box in the figures.
4. Discussion
By using LC/QTOFMS, the sera from ten HCC patients
pre- and post-surgery were investigated for highlighting
the differentiating metabolites via multivariate data
analysis. Multivariate data analysis in PCA and SVM
models was discussed. Upon finishing the capture of
systems-level examination of features, identification of
metabolites was perfomed for indication of the underly-
ing metabolic mechnisms with the improved visualize-
tion using z-score plot and cluster heat map.
Figure 2. The analysis of PCA for pre-hepatectomy and
post-hepatectomy groups. Four samples in post-hepatec-
tomy group, namely post-1, post-3, post-4 and post-9, were
Copyright © 2011 SciRes. AJAC
Figure 3. The clustering result of support vector machine
with RBF kernel for pre-hepatectomy and post-hepatec-
tomy groups.
4.1. Identification of Differentiating Metabolites
Identification of differentiating metabolites listed in Ta-
ble 1 was mostly based on the comparison of the ob-
tained chromatography retention time and MS/MS frag-
mentation pathway with those from database record or
reference standard. Ions at m/z 162 and m/z 204 were
detected with the relatively high scores from the SVM
data training in these metabolite ions. Their identifica-
tions were carried out by using reference standard
through retention time in liquid chromatography and
MS/MS fragmentation. It should be noted that the frag-
ment ions at m/z 60 and m/z 85 were the characteristic
ions from acylcarnitine [23]. Thus, the ion at m/z 162
yielded its fragment ions at m/z 60 and m/z 85, corre-
sponding to carnitine (Figure 4(a)). Similarly, the ion at
m/z 204 was identified to be acetylcarnitine. Figure 4b
shows the MS/MS spectrum of the ion at m/z 520 in pos-
itive ion mode. Because the fragment ion at m/z 184 as a
characteristic choline moiety from lysophosphati-dylch-
oline (LysoPC) [24,25] was detected, the ion at m/z 520
was identified to be LysoPC (18:2). Similarly, other Ly-
soPCs as well as phosphatidylcholine (PC) and pho- pha-
tidylethanolamine (PE) were tentatively identified in
positive ion mode. Additionally, identification of hypo-
xanthine and amino acids was carried out by comparison
to the obtained MS/MS spectra with those of authentic
Figure S1. Cross-validation for performance in SVM model.
Copyright © 2011 SciRes. AJAC
Figure S2. Z-score plot for all the potential biomarkers. Red dots indicated that pre-hepatectomy whereas green dots indi-
cated that post-hepatectomy sample sets. (LysoPC: lysophosphatidyl-choline; LysoPE: lysophosphatidylethanolamine; PC:
lysophosphatidylcholine ; PE: phosphatidy lethanolamine). (z-score range: 10 to 10).
Figure S3. Cluster heat map. Six post-hepatectomy samples are clearly separated from the pre-hepatectomy group, but dis-
tribution of other four samples in post-hepatectomy group (namely post-1, post-3, post-4 and post-9) could not be recognized,
consistent with kernel PCA result. (LysoPC: lysophosphatidyl-choline; LysoPE: lysophosphatidylethanolamine; PC: lyso-
phosphatidylcholine; PE: phosphatidylethanolamine). (Fold-change range: 3 to 3).
Copyright © 2011 SciRes. AJAC
Copyright © 2011 SciRes. AJAC
Figure 4. MS/MS spectra of carnitine (a) and LysoPC (18:2) with the collision energy of 20 eV.
4.2. Multivariate Analysis and Annotation of
Metabolic Shifts
In Figure 2, PCA was applied for grouping pre- and
post-hepatectomy in HCC patients. The obtained data
showed that discrimination of the two groups was not
clear. Furthermore, SVM with RBF kernel as the super-
vised machine learning was applied to examine the ef-
fectiveness of the classification function and feature se-
lection (Figure 3). Cross-validation was also performed
to evaluate the performance of SVM model (Figure S1).
The parameter pairs (C and γ) were tested and the one
with the best cross-validation accuracy was picked. A
wide range from 1010 to 1010 for the parameters C and γ
was selected in construction, and the best region on the
grid was obtained. The cross-validation rate was much
less than 100%, probably resulted from the small number
of cases and variation of the personalized status. Never-
theless, SVM model with RBF kernel could classify two
groups for feature selection.
After the metabolites have been identified, the possi-
ble metabolic regulation could be used for the interpreta-
tion of biochemical changes induced by partial hepatic-
tomy. Carnitine, a trimethylated amino acid, was re-
ported to exhibit deficiency in some individuals follow-
ing a strict vegetarian diet [26]. On one hand, carnitine
deficiency observed in our study might indicate that en-
ergy metabolism was disturbed by partial hepatectomy in
the HCC patients becasue carnitine has functioned to
reduce the availability of lipid peroxidation by trans-
porting fatty acids into the mitochondria for beta-oxida-
tion to generate adenosine triphosphate (ATP) energy.
On the other hand, the increase of serum carnitine could
be considered a new index of improved liver function
[27], suggesting that L-carnitine could inhibit hepatocar-
cinogenesis via protection of mitochondia in another
investigation [28]. Taken together, sharp down-regula-
tion of carnitine implied that reduced energy production
may occur in 3 to 5 months after partial hepatectomy.
Notably, decreased amino acid biosynthesis after the
surgical operation might play a key role in the HCC pa-
tients, because carnitine is synthesized endogenously
from the essential amino acids. The pronounced decrease
of proline, valine and leucine/isoleucine may contribute
to carnitine deficiency [29]. Therefore, we speculated
that partial hepatectomy could cause reduced energy
production through decreased amino acid synthesis and
carnitine deficiency.
Interestingly, LysoPCs in post-hepatectomy were up-
regulated significantly compared to pre-hepatectomy.
LysoPCs levels previously described in hepatitis patients
were down-regulated compared to the healthy control,
but the mechanisms remain to be explored [16]. Never-
theless, the decreased concentrations of PC and PE were
also observed, indicating that lipid peroxidation occurred
after hepatectomy in HCC patients [30]. One of the pos-
sible correlations was highlighted in Figure 5 (a). Lipid
peroxidation could be controlled by carnitine, thus the
increased rate of lipid peroxidation as the feedback re-
vealed depletion of carnitine could cause metabolic shifts
in relative pathways. Similarly, two kinds of LysoPCs
(16:0 and 18:2) were found with increased concentra-
tions but carnitine with decreased concentration in the
intestinal fistulas patients [31]. Furthermore, It was ele-
vated hypoxanthine in purine metabolism that was ob-
served in post-hepatectomy groups from Table 1, sug-
gesting that the purine biosynthesis could be substituted
by the salvage pathway after the operation [32]. The pos-
sible metabolic pathways were simply delineated in Fig-
ure 5(b).
Collectively, perturbation of the differentiating me-
tabolites might reveal reduced energy production from
our results associated with partial hepatectomy in HCC
patients, which was consistent with the evidence from
altered protein and energy metabolism in chronic liver
disease associated with malnutrition and the report in
primary carcinogenesis and generation of metastasis
[33,34]. Therefore, partial hepatectomy might lead to
liver function deterioration instead of amelioration in
terms of whole body level.
Finally, to improve the visualization, z-score plot
(Figure S2) and cluster heat map (Figure S3) were per-
formed. They were plotted for each of fifteen metabolites
with emphasis on depletion of carnitine. The z-score plot
displayed metabolic alterations between pre-hepatectomy
and post-hepatectomy (z-score range: 10 to 10). The
heat map represented the unsupervised hierarchical clus-
tering of the data grouped by sample type (fold-change
range: 3 to 3). It also visualized the up- or down-regu-
lation of each metabolites and distribution of four sam-
ples (namely post-1, 3, 4 and 9) among them could not
be recognized, revealing the discrimination from the me-
tabolite clustering as well as sample grouping. Carnitine,
as the amino acid derivate, was classified with three
amino acids (proline, valine and leucine/isoleucine) in
the same cluster.
It is important to note firstly that the number of patient
cohort was small but still may be scientifically important,
and bear further investigation in a large population. Sec-
ondly, the sera were collected from pre- and 3 to 5
months post-partial hepatectomy in HCC patients, and
the result indicated the resection might induce liver func-
tion deterioration. Hereby, we would follow up for a
longer time to investigate their liver functions. Thirdly,
lifestyle factors greatly influence metabolism, making it
difficult to disentangle their effects from operation-re-
lated outcomes. Nevertheless, the reported metabolomics
study involving LC/QTOFMS and multivariate data
analysis might provide a promising tool for understand-
ing the underlying metabolic responses because little is
known currently about metabolic perturbation in HCC
patients through the surgical operation.
Figure 5. Lipid peroxidation (a) and proposed metabolic pathways (b).
Copyright © 2011 SciRes. AJAC
Copyright © 2011 SciRes. AJAC
5. Conclusions
Among fifteen statistically significant metabolites, amino
acids and acetylcarnitine decreased significantly and
down-regulation of PC and PE was accompanied with
pronounced increase of LysoPCs. Carnitine, a necessary
factor in the utilization of long chain fatty acids to pro-
duce energy, was down regulated sharply by partial he-
patectomy. The elevated hypoxanthine might play a vital
role in salvage pathway for reduced energy production.
Metabolomic approach with highly efficient classifica-
tion was perfomed to distinguish the two groups of data
sets and delineate the effects of partial hepatectomy in
metabolic shifts, reflecting that hepatic resection might
induce liver function deterioration in 3 to 5 months.
6. Acknowledgements
The authors would like to thank Mr. David T. W. Chik
and Ms. Silvia T. Mo for their technical assistance in
LC/QTOFMS analysis, and Dr. Zhu Yang for his help in
performing SVM model and z-score plot.
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