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INTRODUCTION
Pancreatic adenocarcinoma is the top ten leading cause
of death from cancer in the world [1]. About 80% of
pancreatic adenocarcinoma patients present with locally
advanced findings or metastasis at the time of diagnosis,
and the median survival time is less than 1 year.
Surgical and the postoperative adjuvant therapies are
only suitable for the remaining 20% patients, and the
extended five-year survival rate is only 10% to 22%.
Several studies suggest that tumors behave differently
with anatomical location, such as the cases of the
left–right colorectal cancer and the greater–lesser
curvature gastric cancer [2–4]. Tumor location implies
a specific organic function, physiological feature, and
histological constitution, and even genomic background.
As for pancreatic cancer, data from the Surveillance,
Epidemiology, and End Results database, Australian
Pancreatic Cancer Genome Initiative cohort, and Dutch
Pancreatic Cancer Group datasets have indicated
higher incidence and better prognosis for head
adenocarcinoma than for body/tail adenocarcinoma, but
post-surgical prognosis remains to be the worse for the
local stage [5–8].
The mechanism for the above disparity has been
speculated based on the manifestation of the obstructive
www.aging-us.com AGING 2021, Vol. 13, No. 6
Research Paper
Integrated analysis of metabolome in a EUS-FNA sample with
transcriptome in the TCGA cohort of pancreatic head and body/tail
adenocarcinoma
Chen Ke1,2, Liu Yuan1,2, Yang Xiujiang1,2, Shen Danjie3
1Department of Endoscopy, Fudan University Shanghai Cancer Center, Shanghai, China
2Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
3Department of Digestive, Minhang Hospital, Fudan University, Shanghai, China
Correspondence to: Yang Xiujiang, Shen Danjie; email: yxj1960@hotmail.com, https://orcid.org/0000-0002-8494-6447;
danjshen@fudan.edu.cn
Keywords: pancreas, cancer, head, metabolome, endoscopic ultrasound
Received: July 29, 2020 Accepted: February 8, 2021 Published: March 10, 2021
Copyright: © 2021 Ke et al. This is an open access article distributed under the terms of the Creative Commons Attribution
License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original
author and source are credited.
ABSTRACT
Metabolome profiles are largely unknown for pancreatic head cancers, in which the predominant anatomical
feature is the exosure of bile, pancreatic juice, and duodenal juice. In this research, 30 head and 30 body/tail
cytological samples acquired by endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) of pancreatic
adenocarcinoma were delivered for liquid chromatography coupled with mass spectrometry (LC-MS).
Transcriptome analysis was performed using the sequencing data from The Cancer Genome Atlas (TCGA) cohort.
LC-MS obtained 4,857 features in EUS-FNA cytological samples, and 586 metabolites were certified. Among them,
30 differential metabolites were identified. In the TCGA cohort, 247 differential metabolism genes were selected
from 1,583 differential genes. The integrated analysis identified the top three enriched metabolic pathways as
follows: branched chain amino acid (BCAA) biosynthesis; glycerophospholipid metabolism; and phenylalanine
metabolism. In cell line, BCAA promoted pancreatic cancer proliferation and inhibited Oxaliplatin-induced
apoptosis. In conclusion, metabolomic analysis with the EUS-FNA sample is feasible for pancreatic cancer. The
integrated analysis can identify key metabolites and enzyme-coded genes between pancreatic head and body/tail
adenocarcinoma. Anti-BCAA metabolism therapy may exert promising effect, especially for the body/tail cancer.
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dilations of the bile and the pancreatic duct (i.e.,
jaundice and acute pancreatitis), and early detection
seems to favor the management of pancreatic head
cancer. However, when both types are resectable, the
biological behavior at the body/tail location may be the
same as that at the head location. Molecular
classification based on large-scale genomic sequencing
has been proposed to guide the precise and individual
management of pancreatic cancer [9]. Dreyer and
Birnbaum et al. demonstrated heterogeneity in the
genomic background of different tumor locations [8, 10].
Pancreas is an organ with complex exocrine and
endocrine functions. The predominate anatomical
feature of pancreatic head cancer is the confluence of
many kinds of metabolic agents, including bile,
pancreatic juice, and duodenal juice. Elevated pressure
due to obstruction by cancer triggers the reflux of juice
and penetration into the microenvironment of cancer
cells. Hence, identifying the metabolic response of
pancreas to pathophysiological stimuli is of great value
in reflecting the status of disease.
The research on the metabolome profile of pancreatic
head and body/tail cancer with local samples is rare.
Hence, in the current study, we first perform
metabolome analysis with endoscopic ultrasound-
guided fine needle aspiration (EUS-FNA) samples for
the two groups. Then, the transcriptome profile is
depicted based on The Cancer Genome Atlas (TCGA)
cohort, an online and open genomic database of
RNA sequences. Finally, the key metabolites and
enzyme-coded genes between pancreatic head and
body/tail adenocarcinoma are identified by integrated
metabolome and transcriptome analysis.
RESULTS
Sixty patients (mean age, 64.6±9.1 years; M/F, 38/22)
undergoing EUS-FNA procedures were included in
the study. The mean BMI was 22.2±2.9 mm. The
dataset covered 30 pancreatic lesions in the head
location and 30 lesions in the body/tail location. The
mean diameter of lesions was 36.0±8.7 mm and
ranged from 21.0 to 60.5 mm. In total, 40% cases
were presented with dilated distal bile duct based on
EUS or CT imaging. In the FNA process, the 22G-
needle was used in 22 cases, whereas 25G-needle was
used in 38 cases. All cases were eventually diagnosed
as pancreatic adenocarcinoma. Table 1 shows that
age, gender, lesion size, BMI, serum metabolism
markers (Alb, BUN, LDH, FDG, and UA), and
CA199 level were similar between the head group and
the body/tail group without significant difference.
However, the 22G-needle was more preferred for
body/tail lesions than head lesions (p<0.001). The
obstructive dilation of the bile duct was more
common in the head group than in the body/tail group
(p=0.001). The uniform epidemiological features and
serum metabolism markers of the two groups indicate
the similarity in their systemic metabolism levels.
All 60 samples were delivered for LC-MS analysis. In
total, 2,550 features and 2,307 features were obtained
at the electrospray ionization negative and positive
(ESI- and ESI+) ionic modes by LC-MS (Figure 1). In
the metabolome analysis, we initially checked the
experimental system by including the quality control
samples into PCA. As shown in Supplementary Figure
1, all quality controls are clustered at the center of the
coordinate axis, indicating the stability of the current
experimental condition. Then, 586 metabolites were
identified and annotated according to compound
molecular weight and peak intensity. As shown in
Supplementary Figure 2, the total PCA plot for the cum
R2X is 0.583, and six outliers are apparent. After
removing the outliers, seven components were derived
from the updated PCA plot (cum R2X=0.513,
Q2=0.130, Figure 2A). Deep mining by OPLS-DA
identified an optimized classification (cum R2Y=0.818,
Q2=0.031, Figure 2B) with three components. The
results of the permutation analysis in Figure 2C prove
that the model has good predictive ability, and no
overfitting exists.
Thirty DEM are finally selected, with the filter of VIP set
to greater than 1 and the p-value set to less than 0.05, as
shown in the volcano plot in Figure 3A and
Supplementary Table 1. The top five changed metabolites
in descending order of VIP, were β-hydroxyisovaleric
acid, L-isoleucine, methyl jasmonate, 3-carboxy-4-
methyl-5-pentyl-2-furanpropanoic acid, and ketoleucine.
The metabolite levels of the two groups are shown in the
heat map and the bar chart Figure 3B, 3C, respectively. In
the TCGA cohort, 1,583 differential genes can be
identified, as shown in the volcano plot in Figure 3D.
After matching the metabolism-annotated gene list, 247
differential metabolism genes were finally selected
(Figure 3E).
The joint enrichment analysis of metabolome and
transcriptome data indicates that the top three
differentially affected metabolism pathways between the
two types of pancreatic cancer are BCAA biosynthesis
(Leu, Ile, and Val) (p=0.002, impact=0.429),
glycerophospholipid metabolism (p=0.003, impact=0.5),
and phenylalanine metabolism (p=0.004, impact=0.6), as
shown in Figure 4 and Table 2. The enriched metabolism
maps are shown in Supplementary Figure 3. BCAA
biosynthesis covers L-isoleucine and 4-methyl-2-
oxopentanoate. Glycerophospholipid metabolism covers
lysoPC (18:1(9Z)), PC (16:0/16:0), and PLA2G-, DGK-,
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Table 1. The clinical features of pancreatic cancer in head and body/tail.
Head (n=30)
Body/tail (n=30)
P-value
Age (ys, mean±SD)
66.0 ± 7.6
63.2 ± 10.3
0.235
Gender (n, %)
1.000
Female
11 (36.7%)
11 (36.7%)
Male
19 (63.3%)
19 (63.3%)
BMI (mean±SD)
21.8 ± 3.0
22.6 ± 2.7
0.282
Lesion size (mm, mean±SD)
36.3 ± 6.6
35.8 ± 10.4
0.822
Needle Size (n, %)
<0.001
22G
1 (3.3%)
21 (70.0%)
25G
29 (96.7%)
9 (30.0%)
Serum Liver test
Alb (g/L, mean±SD)
41.6 ± 4.4
43.1 ± 4.1
0.159
BUN (umol/L, mean±SD)
4.9 ± 1.5
4.7 ± 1.1
0.700
LDH (U/L, mean±SD)
169.4 ± 51.8
175.1 ± 64.4
0.707
FDG (mmol/L, mean±SD)
6.4 ± 2.6
6.8 ± 2.9
0.578
UA (umol/L, mean±SD)
252.0 ± 80.8
283.3 ± 62.4
0.098
Serum CA199 (n, %)
0.718
Normal
4 (13.3%)
5 (16.7%)
Elevation
26 (86.7%)
25 (83.3%)
Dilation of Bile duct (n, %)
0.001
Negative
19 (63.3%)
29 (96.7%)
Positive
11 (36.7%)
1 (3.3%)
GPD1L-, and PEMT-coded enzymes. Phenylalanine
metabolism covers L-phenylalanine, 2-phenylacetamide,
and DDC-, HPD-, and TAT-coded enzymes. The detailed
metabolism level for BCAA was shown in Figure 5A.
To further vilify role of BCAA on pancreatic cancer,
proliferation and Oxaliplatin-induced apoptosis were
performed. As shown in Figure 5B, supplementary of
Val and Leu significant promoted pancreatic cell line
Figure 1. Flow chart of the study. The SHCC cohort and TCGA cohort were applied for metabolome and transcriptome analysis,
respectively, and then joint analysis was performed. The pancreatic cancer was divided into head and body/tail groups, based on the
anatomic location.
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Figure 2. The PCA and OPLS-DA model construction of metabolome signature. (A) The plot depicted the distribution of the
pancreatic head and body/neck cancers (n=30 for each group) into seven components with the PCA analysis (cum R2X=0.513, Q2=0.130), after
removing the six outliers. (B) Deep mining by OPLS-DA identified an optimized classification (cum R2Y=0.818, Q2=0.031) into three
components. (C) Results of the 200 times permutation test of the OPLS-DA model. The blue regression line of the Q2-points intersects the
vertical axis below zero indicated the validity of the model.
Figure 3. The DEM and DMG identified in the metabolome and transcriptome data. (A) The volcano plot of DEM denoted the
selection of DEM (n=30), with VIP values >1 and p-value <0.05 provided from the t-test. The blue block in upper-right represented the
selected DEM. (B) The heatmap (30◊60) depicted the Z-score of 30 DEM in metabolome analysis with log transformation, by setting
body/tail cancers as referenced (n=30 for each group). (C) The bar chart depicted the direct comparison for each metabolite between
pancreatic head and body/neck cancer. The Z-score with log transformation was presented. (D) Genes in the transcriptome analysis were
presented in the volcano plot. By setting the fold-change >1 and p-value <0.05 as threshold, differential genes were depicted. The green
point represented the downregulated genes (n=579) and the red points represented the upregulated genes (n=1,004). ( E) The heatmap
(247◊167) depicted the Z-score of DMG levels with log transformation, by setting body/tail cancers as referenced (n=138 for head group,
and n=29 for body/tail group).
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proliferation in Ki67 flow cytometry analysis, which
also was proved by CCK-8 test (Figure 5C). While, no
significant value was observed for Ile for both two
experiments. In the Figure 5D, the rate of apoptosis
as shown by the percentage of Annexin V+ positive
cells was significant decreased after the Oxaliplatin
treatment for 48 h. Those data indicated BCAA
promoted pancreatic cancer proliferation and inhibited
Oxaliplatin-induced apoptosis.
The obstructive dilation of the bile duct is common
in head and thus may induce bile siltation in
the microenvironment of the pancreatic cancer.
Hence, bile acid metabolism was further checked in
this study. As shown in Figure 6, no significant
differences exist between the two groups for the nine
metabolites (glycocholic acid, deoxycholic acid,
cholic acid glucuronide, taurocholic acid, cholic acid,
taurocholic acid 3-sulfate, glycoursodeoxycholic
acid, glycochenodeoxycholic acid 3-glucuronide, and
3-sulfodeoxycholic acid) involved in bile acid
metabolism.
DISCUSSION
The current study initially investigated the metabolomic
profiles of pancreatic head and body/tail cancer by
using EUS-FNA-acquired cytological materials. Then,
the transcriptome signature obtained by the TCGA cohort
was integrated into the metabolome. Our findings
Figure 4. Enriched pathway by integrated analysis. Both the DEM and DEG were submitted to MetaboAnalyst, an online joint analysis
module for integrated analysis. The count number is matched number of metabolites from the DEM with the pathway library. The more
count number, the more reliable of the enriched pathway. The p-value is the statistical value from the over-representation analysis. The
dashed line indicated the significant threshold p-value of 0.05.
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Table 2. Integrated enrichment analysis of the metabolome and transcriptome data.
Pathway
Total
Count
P-value
Impact
Valine, leucine and isoleucine biosynthesis
8
2
0.002
0.429
Glycerophospholipid metabolism
34
3
0.003
0.500
Phenylalanine metabolism
11
2
0.004
0.600
Glycosylphosphatidylinositol-anchor biosynthesis
3
1
0.028
0.143
Phenylalanine, tyrosine and tryptophan biosynthesis
4
1
0.037
1.000
Arachidonic acid metabolism
37
2
0.045
0.472
Linoleic acid metabolism
5
1
0.046
0.250
Valine, leucine and isoleucine degradation
38
2
0.048
0.093
Aminoacyl-tRNA biosynthesis
46
2
0.067
0.044
alpha-Linolenic acid metabolism
13
1
0.117
0.083
Sphingolipid metabolism
21
1
0.182
0.550
Biosynthesis of unsaturated fatty acids
22
1
0.190
0.048
Tryptophan metabolism
40
1
0.320
0.051
Pyrimidine metabolism
41
1
0.327
0.083
Purine metabolism
68
1
0.485
0.014
indicate that the differential metabolites between head
cancer and body/tail cancer are mainly enriched by the
following: valine, leucine and isoleucine biosynthesis;
glycerophospholipid metabolism; and phenylalanine
metabolism. Bile acid metabolism was not involved with
the pancreatic cancer of the head group.
Previous studies have reported the promising value of
metabolome in identifying pancreatic cancer from non-
malignant diseases, such as chronic pancreatitis [11–
13]. Due to the specimen acquisition was easy and
falsifiable, serum samples were collected for the
metabolomics studies by different techniques, including
gas chromatography coupled with mass spectrometry
(GC-MS), LC-MS, or NMR spectroscopy. LC-MS is
one of the most used techniques for untargeted
metabolomic studies that have set higher sensitivities in
finding the metabolite. However, a critical question is
on whether the systemic perfused serum sample can
specifically reflect the local cancer metabolomic profile.
Trabado and Darst et al. reported that gender and age
are the principal factors for plasma metabolome
variability in healthy humans [14, 15]. The obesity
measured by BMI has also been regarded a profound
factor [16]. In addition, the uncontrolled bias from other
unknown physiological and pathological conditions
cannot be ignored in plasma metabolome. Nowadays,
EUS-FNA almost is the routine examination in
advanced pancreatic cancer for sample biopsy. EUS-
FNA is the only minimally invasive technique for the
"live" specimen acquisition. With the guidance of EUS,
the specific tumor tissue can be targeted not only for
routine cytohistological examination but also for next-
generation sequencing [17–19]. Currently, no studies
have been reported about the use of EUS-FNA samples
for metabolomic analysis. However, Rezig et al.
established a feasible metabolomic approach with the
cytological material of thyroid tissues acquired by FNA
via the 21-gauge needle [20]. In overcoming the flaw of
serum sample, we initially performed EUS-FNA to
acquire local cytological samples for metabolomic
analysis. The PCA plot in our study has achieved
stability in the current experimental condition for all
quality controls clustered at the center of the coordinate
axis. Hence, the EUS-FNA-based metabolome analysis
is feasible for pancreatic cancer.
As we previously described, pancreatic head cancer
suffers from more exposure to chemical agents,
including bile, pancreatic juice, and duodenal juice, than
body/tail cancer. Our findings reveal that BCAA
biosynthesis, glycerophospholipid metabolism, and
phenylalanine metabolism are the most enriched
pathways. Amino acid metabolism is the most
predominant pathway in pancreatic cancer. The BCAA,
are linked with obesity, insulin resistance, and
development of diabetes. Two prospective cohorts can
robustly elucidate the predictive value of BCAA during
pancreatic cancer development [21, 22]. Phenylalanine
is also a member of aromatic amino acid (AAA)
metabolism. No potential explanations have been
reported for elevated BCAA biosynthesis and reduced
phenylalanine metabolism pathways in pancreatic head
cancer unlike in body/tail cancer. The concept of tissue
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Figure 5. BCAA pathway in pancreatic cancer. (A) metabolites level of BCAA between pancreatic head and body/neck cancer (n=30 for
each group). Val and Leu significantly promoted pancreatic cell line proliferation in Ki67 flow cytometry analysis (B) and CCK-8 test (C). Flow
cytometry indicated Oxaliplatin treatment for 48 h deceased the rate of apoptosis (D). * p<0.05, ** p<0.005, *** p<0.001.
Figure 6. The nine metabolites level involved with bile acid metabolism. The bar chart denoted the direct comparison for the nine
bile acid metabolites between pancreatic head and body/neck cancer (n=30 for each group). The Z-score with log transformation was
presented. GUDCA, glycoursodeoxycholic acid, GCDC, glycochenodeoxycholic acid.
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context-directed activity of the amino acid metabolic
pathway in cancer reported by Mayers et al. may offer
insights for further investigation [23]. Our data shown
the Leu, Ile, and Val were enriched in the body/tail
adenocarcinoma, which shown a worse prognosis than
head adenocarcinoma, reported on the previous clinical
cohorts [5–8]. Consistently, in cell line, both three
amino acid promoted pancreatic cancer proliferation
and inhibited Oxaliplatin-induced apoptosis. Hence,
those data indicated anti-BCAA metabolism therapy
might exert promising effect, especially for the body/tail
cancer.
Obstructive jaundice is the most common symptom of
pancreatic head cancer due to the dilation of bile duct,
and bile acid is the most widely investigated metabolic
agent [24]. By contrast, for pancreatic body/tail cancer,
the reflux of bile and duodenal juice is weak. In vitro
experiments have proven the effect of bile acid on
pancreatic acinar cell and ductal epithelium [25, 26].
However, direct evidence from exposure of pancreatic
cancer to bile acid and metabolic profiling in vivo is
lacking. Although the obstructive dilation of bile duct is
more common in the head than in the body/tail, no
difference for bile acid metabolites in the two tumor
locations has been established in our study, which
indicate that siltation of the bile does not always lead to
the alteration of bile acid metabolism in the local
region. We may also speculate that bile indirectly
operates in the entire pancreas instead of directly
stimulating the microenvironment. However, the
enriched glycerophospholipid metabolism pathway in
pancreatic head cancer may be associated with the
siltation of the bile.
Studies have shown the value of metabolome in
chemotherapy and chemoresistance [27, 28]. As for
the chemotherapy of pancreatic cancer, no specific
regimes are proposed for the head or body/tail group.
And the chemoresistance is the major cause of the
failure of therapy. The current study inspires us
whether specific targets (such as the amino acid
metabolism) exist for the drug discovery for different
location of pancreatic cancer, which further research
are warranted.
Limitations also exist in the current work. First,
pancreatic head cancer and pancreatic body/tail cancer
were investigated based on unpaired tumor samples and
without the adjacent normal samples as reference.
Considering a normal reference may benefit the
identification of tumor specific metabolites. Second, the
model classification and the selected DEM need to be
validated by further external cohorts. Third, the
metabolomic analysis has been restricted to advanced
pancreatic cancer only. Extending our conclusions to
the entire process of pancreatic carcinogenesis should
be implemented carefully.
In conclusion, our data demonstrate that the EUS-
FNA-acquired cytological sample is suitable for the
metabolomic analysis of pancreatic cancer. Tumor
location indeed plays a role in metabolic profiling. The
integrated analysis indicates that amino acid pathways,
including elevated BCAA biosynthesis and reduced
phenylalanine metabolism, are enriched in head cancer.
Anti-BCAA metabolism therapy might exert promising
effect, especially for the body/tail cancer. The obstructive
siltation of bile does not always lead to the enrichment of
bile acid metabolites in pancreatic head, but it may be
related to glycerophospholipid metabolism.
MATERIALS AND METHODS
Patients and sample collection
A total of 60 patients with suspected advanced pancreatic
cancer underwent EUS-FNA from October 2018 to March
2019 at Fudan University Shanghai Cancer Center
(SHCC) were consecutively included. The detailed
clinical characteristics were retrieved from the database.
All patients gave informed consent after approval of the
current study by the institutional review board.
The detail of EUS-FNA procedure was described as
before [29]. Two experienced endoscopists performed
all the procedures. Linear longitudinal ultrasound
endoscopes (EG3270UK and EG-3870UTK, Pentax,
Japan) were applied under HI VISION ultrasound
platform (Preirus HITACHI, Japan), with conscious
sedation via intravenous midazolam and fentanyl. After
inserting the needle into the echoendoscope, endo-
scopists repositioned the echoendoscope to set lesion at
desired position and advanced needle. A 5-20 ml
syringe was set to activate the suction. All the materials
were finally delivered to the cytological and histological
examination. Endoscopist directed rapid on-site
evolution (ROSE) was performed in all cases during
sample preparation. The remain cytological samples
were flushed into tubes and stored at -80° C until use.
Final diagnosis was defined by the histologic evidence
in surgical, biopsy pathology, or definite cytology.
Sample preparation
The aspirated sample (100 μL per case) was mixed with
300 μL of methanol, then vortexed for 30 s, and
ultrasound for 20 min at ice bath. The sample was
centrifuged at 12000 rpm for 15 min. The supernatant
(200 μL) was transferred to vial for LC-MS analysis.
Equal volumes (20 μL) of each sample were mixed and
pooled into the quality control sample.
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LC-MS analysis
The workflow was shown in Figure 1. All experiments
were performed on an Ultimate 3000LC platform
(Thermo Scientific, USA) and equipped with Hyper gold
C18 (100x2.1mm 1.9 μm) column. Chromatographic
separation conditions included column temperature at
40° C, flow rate at 0.35 mL/min, automatic injector
temperature at 4° C, injection volume at 10 μL. The
mobile phase A was water plus 5 % acetonitrile and 0.1%
formic acid. The mobile phase B was acetonitrile plus
0.1% formic acid. Metabolomics feature extraction and
preprocessed were performed with Compound Discoverer
software (Thermo Scientific, USA). The raw data was
normalized and edited into two-dimensional data matrix
by excel 2010 software, including retention time,
compound molecular weight, samples, and peak intensity.
Metabolome joint transcriptome pathway
The metabolome data mining was carried out by SIMCA
(version 14.1). Unsupervised principal components
analysis (PCA) and orthogonal partial least squares
discriminant analysis (OPLS-DA) were performed to
construct discriminated models for head and body/tail
cancers. R2Y and Q2 were the evaluated parameters,
which represented the fitness and prediction ability of
OPLS-DA model. A permutation test with 200 times was
examined to certify the model’s validity. The unpaired
student’s t-test was used to compare the difference
between two groups. The differential metabolites (DEM)
were selected and ranked by variable importance in the
projection (VIP) values >1 and p-value <0.05 provided
from the t-test.
Genomic data of RNA sequencing for pancreatic
carcinoma was queried from TCGA database. To
investigate the genomic profile between head and
body/tail group, edgeR Package [30] was applied to
select the differential genes with fold-change >1 and
p-value <0.05 in TCGA cohort. Then, differential
metabolism genes (DMG) were identified and mapped in
a previous 2,752 metabolism-annotated genes list [31].
The integrated analysis was performed by joint analysis
module in MetaboAnalyst [32]. Over-representation
analysis based on hypergeometric analysis was applied
for enrichment. The topology analysis was based on the
degree centrality within a pathway.
Cell lines and culture
Human pancreatic cancer cell line Panc-1 was were
purchased from the Cell Bank of Type Culture
Collection of Chinese Academy of Sciences, and have
been authenticated by Chinese Academy of Sciences
and cultured in RPMI-1640 medium (Gibco, CA, USA).
Cells were incubated in a humidified atmosphere at
37° C with 5% CO2. The RPMI-1640 medium included
leucine (Leu, 50mg/L), isoleucine (Ile, 50mg/L), and
valine (Val, 20mg/L), which was termed as control
medium. The branched chain amino acid (BCAA)
medium was supplemented with purified amino acids
(Sigma) to 10x leucine (500mg/L), 10x Ile (500mg/L),
or 10x Val (200mg/L), respectively.
Cell proliferation and apoptosis detection
Flow cytometry was used to evaluate cell proliferation
and apoptosis. Incubation with BCAA medium for 48h,
Panc-1 cell was harvested and labeled with Ki-67-FITC
(Biolegend) for proliferation detection.
Panc-1 cells (2×103/well) was suspended in 100 μL
RPMI-1640 medium and incubated in 96-well plates.
After cell adherence, different medium was added, and
culture for five days. The cell proliferation rate was
calculated by cell counting kit-8 (CCK-8) regent
(Dojindo Laboratories), according to the protocol.
Oxaliplatin (Selleck) induced cell apoptosis was
detected. Panc-1 cells (2×106/well) was incubated with
Oxaliplatin (10 μmol/L) for 48 h, and harvested for flow
cytometry by labelling Annexin V-FITC (Biolegend),
and 7-AAD (Biolegend). Annexin V +/7-AAD - were
early apoptotic cells and Annexin V +/7-AAD + were
late apoptotic cells. The percentage of both early and
late apoptosis were calculated.
Flow cytometry was performed using a BD FACS
Canto II flow cytometer (BD Biosciences, San Diego,
CA, USA). Data were analyzed using the FlowJo
software (Flowjo, Treestar Inc., Ashland, OR, USA).
Statistical
Categorical variables were compared by using Chi-
square tests, and continuous variables were compared
by using unpaired student’s t-test. A P value < 0.05 was
considered statistically significant. All statistical
analyses were performed using the SAS 8.02 software
package (SAS Institute, USA).
AUTHOR CONTRIBUTIONS
Chen Ke and Yang Xiujiang contributed conception and
design of the study. Yang Xiujiang and Liu Yuan
performed sample collection. Chen Ke performed cell
experiment. Shen Danjie performed data and the
statistical analysis. Chen Ke wrote the manuscript. All
authors contributed to manuscript revision, read, and
approved the submitted version.
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CONFLICTS OF INTEREST
The authors declare that they have no conflicts of
interest.
FUNDING
This work was supported by the grant from the Program
of Shanghai Anti-cancer Association (No. SHCY-JC-
201931).
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SUPPLEMENTARY MATERIALS
Supplementary Figures
Supplementary Figure 1. The quality control testing by including quality control samples into the PCA analysis. (A) the PCA plot
in ESI+ mode. (B) the PCA plot in ESI- mode.
Supplementary Figure 2. The total PCA analysis indicated the six outliers. (A) the PCA plot. (B) Hotelling's T2 plot shows six cases
with values larger than the 99% confidence limit were considered as outlier.
www.aging-us.com 8893 AGING
Supplementary Figure 3. The metabolism pathway of the top three enriched items, labeled with metabolites and enzyme-
coded genes.
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Supplementary Table
Please browse Full Text version to see the data of Supplementary Table 1.
Supplementary Table 1. The detailed DEM and DMG lists.
