Liang Mo1, Bing Wei1, Renji Liang1, Zhi Yang1, Shouzhi Xie1, Shengrong Wu1, Yong You2. 1. Department of Thoracic Surgery, the First Affiliated Hospital of University of South China, Hengyang, Hunan Province, China. 2. Medical College, University of South China, Hengyang, Hunan Province, China.
Lung cancer is a type of malignant tumor that seriously endangers human health and
life because it has high morbidity and mortality worldwide.[1] Lung adenocarcinoma, a subtype of lung cancer, is the leading cause of
cancer-related deaths in the United States. Lung adenocarcinoma has an average
5-year survival rate of 15% to 17%, which is primarily due to late-stage diagnosis
and no available clinical tests that can provide therapy recommendations.[2] Additionally, the etiology of lung adenocarcinoma is complicated, and it is
difficult to achieve early-stage diagnoses with existing imaging, histopathology,
and bronchoscopy methods. Thus, most patients are diagnosed with advanced-stage lung
adenocarcinoma on admission.[3] Therefore, there is an urgent need to find early diagnostic markers for lung
adenocarcinoma that are conducive to the early detection and treatment of this
malignancy, as these would improve patient survival rates.Proteomics is a cross-discipline that has emerged in the post-genomics era and is
used to identify all proteins in a given sample. The goal of proteomics is to
analyze the interactions and connections between proteins from a holistic
perspective, revealing the rules of protein function and cellular activities.[4] Among these methods, non-targeted metabolomics can quantify metabolites in
biological systems, maximizing the information from metabolites.[5] Because of the large number of small molecule metabolites in biological
samples and the large dynamic range of their concentrations, chromatography-mass
spectrometry is the most important tool for metabolomics research. Liquid
chromatography and tandem mass spectrometry (LC-MS/MS) is a series analysis platform
with high performance liquid chromatography as the separation system and
high-resolution mass spectrometry as the detection system. Compared with other
chromatographic-mass spectrometry techniques, LC-MS/MS is more suitable for the
analysis of metabolites with low volatility or poor thermal stability. Ultra-high
performance liquid chromatography columns packed with 1.7-μm ultrafine particles are
at least 10× faster than conventional HPLC, with several times higher sensitivity
and resolution.[6] Currently, ultra-performance liquid chromatography and
quadrupole-time-of-flight (Q-TOF) mass spectrometry have been widely used in
metabolomics research. Therefore, protein metabolomics technologies have become an
indispensable tool for studying tumor biology, and this field has shown rapid development.[7]In the process of searching for lung cancer biomarkers, blood,[7,8] urine,[9,10] saliva,[11] and lung tissue[12] have been used as research samples. Protein metabolomics techniques are used
to identify differences in metabolite expression between cancerous and normal lung
tissues, thereby screening for biomarkers for the early diagnosis of lung cancer. So
far, many lung cancer biomarkers have been identified. As previously reported,
volatile organic compounds (VOCs)[13] can be used as biomarkers for detecting lung cancer during breathing.
Previous studies have clarified that metabolites such as cyclophilin (CYP-A),
macrophage migration inhibitory factor (MIF),[14] polymeric immunoglobulin receptor (PIGR), 14-3-3η,[15] thymosin β4 (TMSB4), ubiquitin, acyl-CoA-binding protein (ACBP), cysteine
protease inhibitor A (CSTA), Cytochrome C,[16] thioredoxin (TXN), humanS100 calcium binding protein A6 (S100A6),
thymopoietin (TMPO), ribosomal proteins L39 and S30, peroxidase (peroxidase, PRDX) 1
and 3 (PRDX1, PRDX3), enolase-1 (ENO1), histone H2A.2,[17] haptoglobin (HP),[17] and SAA1 and SAA2[18] are overexpressed in cancer tissues, suggesting that they can be used as
specific diagnostic biomarkers for lung cancer. Furthermore, Li et al.[19] showed that leucine-rich alpha-2-glycoprotein (LRG1) is highly expressed in
urine samples from lung cancerpatients compared with healthy subjects. However,
these studies are far from enough with regard to the complex associations between
metabolomics and lung adenocarcinoma. A systemic analysis of metabolites of lung
adenocarcinoma tissues is urgently needed to offer more candidates for the diagnosis
and mechanism of early-stage lung adenocarcinoma.In this study, a comprehensive metabolomics analysis of lung adenocarcinoma tissues
was performed by LC-MS/MS. The selected differentially expressed metabolites could
be used as clinical biomarkers for incipient diagnosis and prognosis of lung
adenocarcinoma.
Materials and methods
Sample collection and preparation
In this study, tissue samples from 10 lung adenocarcinomapatients, including
tumor and non-tumor tissues, and tissue samples from 10 benign lung tumorpatients were collected. The clinical characteristics of these 20 patients are
shown in Table 1.
All patients provided written informed consent, and ethics approval was obtained
from the Ethics Committees of the First Affiliated Hospital of University of
South China.
Table 1.
Clinical characteristics of the lung cancer and benign lung tumor
patients.
Lung cancer patients (Cancer 1–10)
Benign lung tumors patients (Lump 1–10)
N (male/female)
10 (6/4)
10 (8/2)
Age (median/range)
61/50–74
54/48–62
Smoker/non-smoker
4/6
5/5
c or p stages (I–II/III–IV)
8/2
Tumor metastasis (yes/no)
3/7
0/10
c stage (clinical stage) and p stage (pathological stage) were based
on the TNM classification.
Clinical characteristics of the lung cancer and benign lung tumorpatients.c stage (clinical stage) and p stage (pathological stage) were based
on the TNM classification.Each sample weighed 60 mg and was sequentially added to 200 μL of water for
homogenization, and then 800 μL of a pre-cooled methanol/acetonitrile solution
(1:1, v/v). The mixture was vortexed and sonicated twice for 30 minutes,
incubated at −20°C for 1 hour, centrifuged at 14,000 ×g for 4 minutes at 4°C,
and then the supernatant was vacuum dried. The material obtained from vacuum
drying was reconstituted in 100 μL of an aqueous acetonitrile solution
(acetonitrile: water = 1:1, v/v), followed by vortexing and centrifugation at
14,000 ×g for 5 minutes at 4°C. Quality control (QC) samples, a mixture of the
three samples in equal amounts, were used to determine the instrument state
prior to injection, to balance the chromatography-mass spectrometry system, and
to evaluate system stability throughout the experiment. The supernatant of the
above samples were taken for LC-MS/MS analysis.
Chromatography and mass spectrometry
Samples were separated on an Agilent 1290 Infinity LC Ultra Performance Liquid
Chromatography System (Agilent Technologies Inc., Santa Clara, CA, USA) with
HILIC columns at 25°C, a flow rate of 0.3 mL/minute, and an injection volume of
2 μL. Solutions A (water, 25 mM ammonium acetate, and 25 mM ammonia) and B
(acetonitrile) were used as the mobile phases. The gradient started at 95% B,
reached 65% B from 1 to 14 minutes, 40% B in the next 2 minutes, and then
reached 95% B from 18 to 18.1 minutes, and was maintained at 95% B from 18.1 to
23 minutes. Samples were placed in an autosampler at 4°C throughout the
analysis. The separated samples were subjected to mass spectrometry using a
Triple TOF 5600 mass spectrometer (AB SCIEX). Mass spectrometry was performed
using electrospray ionization (ESI), with positive and negative ion modes,
respectively.
Data processing and statistical analyses
Principal component analysis (PCA), partial least squares discriminant analysis
(PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA)
were performed to maximize the separation between groups using SIMCA-P+ 14.1
software (Umetrics, Umeå, Sweden). Statistical significance was analyzed using
the Student’s t-test, and statistical significance was defined
as p < 0.05.Pathway analysis combined with expression data has recently been emphasized to
reveal potential functional interactions between multiple candidate metabolites.
Functional interactions between the different groups of differentially expressed
metabolites were examined using the Kyoto Encyclopedia of Genes and Genomes
(KEGG) pathway database (http://www.kegg.jp/).[20]
We performed ROC curve analysis using IBM SPSS Statistics for Windows, version
19.0 (IBM Corp., Armonk, NY, USA) to analyze each candidate biomarker and
inspect its utility for predicting lung adenocarcinoma or benign lung tumors.
Sensitivity and specificity trade-offs were summarized for each variable using
the area under the ROC curve (AUC). The AUCs of the selected metabolites were
compared to judge the performance of the candidate metabolites in lung
adenocarcinoma. An AUC value of 1.0 corresponded to a prediction model with 100%
sensitivity and 100% specificity, whereas an AUC value of 0.5 corresponded to a
poor predictive model. The level of significance was set at
p < 0.05.
Results
Quality control of the experiments
The system stability of this project was analyzed and evaluated by QC sample
spectrum comparison and PCA analysis. The results of comparing the total ion
flow charts (TIC) of QC samples showed that the response intensity and retention
time of each chromatographic peak basically overlapped, which indicated that the
variation caused by instrument errors was small during the experiment.
Additionally, the PCA of the total sample showed that the QC samples were
closely clustered in the positive and negative ion modes, demonstrating that the
experiment had good repeatability. Moreover, Hotelling’s T2 analysis of
population samples showed that all samples were within 99% confidence interval,
without outlier samples. Therefore, these findings clarified that the system of
this study was stable and could be used for subsequent analysis.
Identification of differently expressed metabolites
The data produced by LC-MS/MS were analyzed to identify significantly
differentially expressed metabolites. We compared the levels of metabolites
between the lung adenocarcinoma and control groups. As shown in Supplemental
Table 1 and Supplemental Figure 1, 119 metabolites in tumor tissues showed
significant differences compared with non-tumor normal tissue
(p < 0.05). Meanwhile, 105 metabolites were detected in
benign lung tumors tissue, indicating significant differences compared with
control groups (p < 0.05) (Supplemental Table 2 and
Supplemental Figure 2). Additionally, 32 metabolites in lung adenocarcinoma
tissue were significantly altered compared with benign lung tumors tissue
(p < 0.05) (Supplemental Table 3 and Supplemental Figure
3). Therefore, these remarkably different metabolites were selected for
subsequent bioinformatics analysis.
Statistical analysis of differentially expressed metabolites
PCA, PLS-DA, and OPLS-DA were used to evaluate differences in the expression of
tissue metabolites between lung adenocarcinomapatients (both tumor and
non-tumor tissues) and the benign lung tumorpatients. PCA analysis showed that
there was apparent distinct clustering between the control and lung
adenocarcinoma tissues of patients with lung adenocarcinoma. Meanwhile, the
comparison between benign lung tumor tissue and control tissue was consistent
with those of lung adenocarcinoma tissue and benign lung tumor tissue. To
further distinguish the differences of benign lung tissue, lung adenocarcinoma
tissue, and para-cancerous tissue, PLS-DA and OPLS-DA were used to supervise
analyses of pattern recognition. In PLS-DA and OPLS-DA, the score plots showed
good visual separation among benign lung tissue, lung adenocarcinoma, and
para-cancerous tissue (Figure
1–2 and Table 2).
Figure 1.
PCA, PLS-DA, and OPLS-DA score maps from the positive ion mode. PCA,
principle component analysis; PLS-DA, partial least squares discriminant
analysis; OPLS-DA, orthogonal partial least squares discriminant
analysis.
Figure 2.
PCA, PLS-DA, and OPLS-DA score maps from the negative ion mode. PCA,
principle component analysis; PLS-DA, partial least squares discriminant
analysis; OPLS-DA, orthogonal partial least squares discriminant
analysis.
Table 2.
Model parameters for PCA, PLS-DA, and OPLS-DA.
Models
Model parameter
Cancer-Control
Lump-Control
Cancer-Lump
Positive ions
Negative ion
Positive ions
Negative ion
Positive ions
Negative ion
PCA
RX2 (cum)
0.693
0.552
0.676
0.57
0.601
0.625
PLS-DA
RY2 (cum)
0.983
0.991
0.973
0.997
0.682
0.888
Q2 (cum)
0.839
0.892
0.892
0.878
0.0307
0.00537
OPLS-DA
R Y2 (cum)
0.998
0.999
0.992
1
0.682
0.991
Q2 (cum)
0.876
0.783
0.892
0.848
−0.00178
0.359
PCA, principle component analysis; PLS-DA, partial least squares
discriminant analysis; OPLS-DA, orthogonal partial least squares
discriminant analysis.
PCA, PLS-DA, and OPLS-DA score maps from the positive ion mode. PCA,
principle component analysis; PLS-DA, partial least squares discriminant
analysis; OPLS-DA, orthogonal partial least squares discriminant
analysis.PCA, PLS-DA, and OPLS-DA score maps from the negative ion mode. PCA,
principle component analysis; PLS-DA, partial least squares discriminant
analysis; OPLS-DA, orthogonal partial least squares discriminant
analysis.Model parameters for PCA, PLS-DA, and OPLS-DA.PCA, principle component analysis; PLS-DA, partial least squares
discriminant analysis; OPLS-DA, orthogonal partial least squares
discriminant analysis.
Metabolic pathway and functional analysis
KEGG pathway analysis of the differential expression data was used to reveal
potential functional interactions between multiple candidate metabolites by
Student t-test. The results showed a significant enrichment of
43 pathways out of 112 pathways in lung adenocarcinoma tissue
(p < 0.05). Among them, the most significantly enriched
10 signaling pathways were central carbon metabolism in cancer
(p = 0), protein digestion and absorption
(p = 8.88E-16), aminoacyl-tRNA biosynthesis
(p = 1.26E-13), mineral absorption
(p = 8.29E-11), ABC transporters
(p = 4.85E-09), choline metabolism in cancer
(p = 5.62E-06), alanine, aspartate, and glutamate
metabolism (p = 6.84E-06), glycine, serine, and threonine
metabolism (p = 4.89E-05), alcoholism
(p = 9.78E-05), and purine metabolism
(p = 1.65E-04) (Figure 3a).
Figure 3.
KEGG pathway analysis of differential expression data. (a) Cancer vs.
control; (b) lump vs. control; and (c) cancer vs. lump. KEGG, Kyoto
Encyclopedia of Genes and Genomes
KEGG pathway analysis of differential expression data. (a) Cancer vs.
control; (b) lump vs. control; and (c) cancer vs. lump. KEGG, Kyoto
Encyclopedia of Genes and GenomesA significant enrichment (p < 0.05) of 39 pathways out of 116
pathways was found by the KEGG pathway analysis in benign lung tumor tissue. The
most significantly enriched 10 signaling pathways included protein digestion and
absorption (p = 0), central carbon metabolism in cancer
(p = 0), aminoacyl-tRNA biosynthesis
(p = 4.17E-14), mineral absorption
(p = 1.35E-12), ABC transporters
(p = 1.92E-10), choline metabolism in cancer
(p = 4.04E-06), glycine, serine, and threonine metabolism
(p = 3.00E-05), retrograde endocannabinoid signaling
(p = 8.64E-05), purine metabolism
(p = 9.37E-05), and glycerophospholipid metabolism
(p = 2.99E-04) (Figure 3b).By comparing lung adenocarcinoma tissue with benign lung tumor tissue, we found
that 17 of the 85 pathways were significantly enriched
(p < 0.05). The most significantly enriched 10 signaling
pathways included ABC transporters (p = 3.28E-05), taurine and
hypotaurine metabolism (p = 6.08E-05), beta-alanine metabolism
(p = 0.000278), retrograde endocannabinoid signaling
(p = 0.00087), galactose metabolism
(p = 0.001138), ascorbate and aldarate metabolism
(p = 0.001235), unsaturated fatty acid biosynthesis
(p = 0.00208), pantothenate and CoA biosynthesis
(p = 0.002759), long-term depression
(p = 0.003555), and central carbon metabolism in cancer
(p = 0.006142) (Figure 3c).
Variation in the levels of metabolites involved in central carbon
metabolism
Among the metabolites involved in central carbon metabolism, 17 were observed in
lung adenocarcinoma tissue and para-cancerous tissue, and the levels of the
following metabolites were significantly changed: L-alanine, L-arginine,
L-asparagine, L-aspartate, L-glutamate, L-glutamine, L-histidine, L-leucine,
L-malic acid, L-methionine, L-tryptophan, L-tyrosine, L-valine, D-glucose
6-phosphate, glycine, L-isoleucine, and L-serine. In the benign lung tumor
tissue, 16 metabolites were significantly changed, including, L-alanine,
L-arginine, L-asparagine, L-glutamate, L-glutamine, L-histidine, L-leucine,
L-methionine, L-phenylalanine, L-tryptophan, L-tyrosine, L-valine, D-glucose
6-phosphate, glycine, L-isoleucine, and L-serine.
Variation in the levels of metabolites involved in protein digestion and
absorption
Seventeen metabolites that were significantly altered in lung adenocarcinoma
tissue and para-cancerous tissue were related to protein digestion and
absorption, including indole, L-alanine, L-arginine, L-asparagine, L-aspartate,
L-glutamate, L-glutamine, L-histidine, L-leucine, L-methionine, L-tryptophan,
L-tyrosine, L-valine, glycine, L-isoleucine, L-serine, and L-threonine. In
benign lung tumor tissue, levels of the following 18 metabolites were
significantly altered compared with the control group: L-alanine, L-arginine,
L-asparagine, L-glutamate, L-glutamine, L-histidine, L-leucine, L-methionine,
L-phenylalanine, L-tryptophan, L-tyrosine, L-valine, glycine, L-cystine,
L-isoleucine, L-serine, L-threonine, and tyramine.
Variation in the levels of metabolites involved in fatty acid
metabolism
Twenty-five significantly altered fatty acids and their derivatives were detected
in tissues from lung adenocarcinomapatients and the control groups:
1-Aminocyclopropanecarboxylic acid, all cis-(6,9,12)-linolenic acid,
cis-9-ialmitoleic acid, DL-Indole-3-lactic acid, D-pipecolinic acid, palmitic
acid, L-pipecolic acid, L-pyroglutamic acid, trans-2-hydroxycinnamic acid,
argininosuccinic acid, phenyllactic acid, 2-hydroxy-3-methylbutyric acid,
caproic acid, caprylic acid, DL-3-phenyllactic acid, DL-mandelic acid, erucic
acid, hydroxyphenyllactic acid, N-acetylneuraminic acid, stearic acid,
2-oxoadipic acid, alpha-linolenic acid, dodecanoic acid, L-malic acid, and
N-acetyl-L-aspartic acid. In the benign lung tumor tissues, levels of the
following 21 metabolites were notably changed compared with the control groups:
16-hydroxypalmitic acid, 1-aminocyclopropanecarboxylic acid,
20-hydroxyarachidonic acid, all cis-(6,9,12)-linolenic acid, arachidonic Acid
(peroxide free), DL-indole-3-lactic acid, eicosapentaenoic acid, linoleic acid,
L-pyroglutamic acid, N-acetylneuraminic acid, cis-9-palmitoleic acid,
D-pipecolinic acid, phenyllactic acid, trans-2-hydroxycinnamic acid, 2-oxoadipic
acid, erucic acid, hydroxyphenyllactic acid, alpha-linolenic acid, caproic acid,
caprylic acid, and palmitic acid.
Variation in the levels choline-associated metabolites
In the lung adenocarcinomapatients, levels of
1,2-dioleoyl-sn-glycero-3-phosphatidylcholine,
1-palmitoyl-sn-glycero-3-phosphocholine,
1-stearoyl-2-hydroxy-sn-glycero-3-phosphocholine, 1-stearoyl-sn-glycerol
3-phosphocholine, choline, glycerophosphocholine, phosphorylcholine,
1-oleoyl-sn-glycero-3-phosphocholine, and 1-stearoyl-2-oleoyl-sn-glycerol
3-phosphocholine (SOPC) were significantly changed compared with the control
group. In tissues from benign lung tumorpatients, levels of
1,2-dioleoyl-sn-glycero-3-phosphatidylcholine,
1-oleoyl-sn-glycero-3-phosphocholine, 1-palmitoyl-sn-glycero-3-phosphocholine,
1-stearoyl-2-hydroxy-sn-glycero-3-phosphocholine, 1-stearoyl-sn-glycerol
3-phosphocholine, choline, glycerophosphocholine, phosphorylcholine, and SOPC
were significantly changed compared with the control group.
Differential metabolites involved in enrichment pathways between lung
adenocarcinoma and benign lung tumor tissues
By comparing lung adenocarcinoma tissue with benign lung tumor tissue, we found
17 differential metabolites involved in ABC transporters, taurine and
hypotaurine metabolism, and beta-alanine metabolism. The specific metabolites
that were significantly differentially expressed in lung adenocarcinoma and
benign lung tumor tissues were acetyl phosphate, D-galactarate, eicosapentaenoic
acid, glycerol, L-alanine, L-glutamate (both positive and negative ions),
L-gulonic gamma-lactone, linoleic acid, L-threonate, oleic acid, arachidonic
acid (peroxide free), myo-inositol, dihydrouracil, L-histidine, stachyose, and
uracil.
Validation of differential metabolites as potential biomarkers for lung
adenocarcinoma or benign lung tumors
All metabolites involved in the 10 most significantly enrichment pathways from
each group were compared with the differential metabolites from the mass
spectrometry results, and finally metabolites involved in both were selected.
The relationship between sensitivity and 1−specificity was plotted to construct
a ROC curve, and the AUC was calculated; the ROC and AUC values of the selected
candidate biomarkers were calculated using binary logistic regression.The ROC curves that distinguished cancer from the control group showed that the
AUC values of the following 15 metabolites were all greater than 0.850:
adenosine 3′-monophosphate, creatine, glycerol, guanosine 5′-monophosphate
(GMP), indole, L-alanine, L-glutamate, phosphorylcholine, taurine, xanthine,
xanthosine, glycine, L-serine, N-acetyl-D-glucosamine, and phosphatidylcholine
(PC) (16:0/16:0) (p < 0.05) (Table 3). Similarly, in the lump group
vs. control group, the ROC curve showed the AUC values of 16 metabolites were
greater than 0.850 (p < 0.05): adenosine 3′-monophosphate,
creatine, GMP, inosine, L-alanine, L-asparagine, L-methionine,
O-phosphoethanolamine, phosphorylcholine, sn-glycerol 3-phosphoethanolamine,
arachidonic acid (peroxide free), glycerophosphocholine, L-cystine, L-threonine,
N-acetyl-D-glucosamine, and PC (16:0/16:0) (Table 4). Moreover, compared with the
lump group, only four cancer-associated metabolites had AUC values greater than
0.850: D-galactarate (0.890 ± 0.074), L-alanine (0.890 ± 0.073), myo-inositol
(0.850 ± 0.100), and uracil (0.850 ± 0.104) (all, p < 0.05)
(Table 5). To
make the results more intuitive, three different metabolites were selected for
display as ROC curves (Figure
4).
Table 3.
The AUC, specificity, and sensitivity of the diagnostic efficacy of
potential lung cancer biomarkers in a comparison of the cancer and
control groups (AUC > 0.850).
Potential biomarkers
p value
AUC ± Sem
Sensitivity
Specificity
Adenosine 3'-monophosphate
0.000924
0.860 ± 0.083
0.800
0.800
Creatine
0.00061
0.930 ± 0.069
1.000
0.900
Glycerol
0.000229
0.960 ± 0.044
1.000
0.900
Guanosine 5'-monophosphate (GMP)
0.001083
0.930 ± 0.056
1.000
0.800
Indole
0.00019
0.980 ± 0.026
0.900
1.000
L-Alanine
8.13E-05
0.990 ± 0.016
0.900
1.000
L-Glutamate
0.002521
0.880 ± 0.080
0.800
0.900
Phosphorylcholine
0.015352
0.970 ± 0.035
0.900
1.000
Taurine
0.00155
0.880 ± 0.078
0.800
0.900
Xanthine
0.000752
0.860 ± 0.087
1.000
0.700
Xanthosine
0.003153
0.900 ± 0.073
1.000
0.800
Glycine
0.002043
0.900 ± 0.076
1.000
0.800
L-Serine
0.005319
0.870 ± 0.085
0.900
0.800
N-Acetyl-D-glucosamine
0.000394
0.950 ± 0.045
0.800
1.000
PC (16:0/16:0)
0.000512
0.900 ± 0.073
1.000
0.800
AUC, area under the ROC curve.
Table 4.
The AUC, specificity, and sensitivity of the diagnostic efficacy of
potential lung cancer biomarkers in a comparison of the lump and control
groups (AUC > 0.850).
Potential biomarkers
p value
AUC ± Sem
Sensitivity
Specificity
Adenosine 3'-monophosphate
0.000271
0.910 ± 0.065
0.800
0.900
Creatine
0.001514
0.900 ± 0.080
0.900
0.900
Guanosine 5'-monophosphate (GMP)
0.000218
0.970 ± 0.032
0.800
1.000
Inosine
0.024261
0.850 ± 0.085
0.600
1.000
L-Alanine
0.000193
0.970 ± 0.035
1.000
0.900
L-Asparagine
0.009937
0.860 ± 0.099
0.900
0.800
L-Methionine
0.012289
0.860 ± 0.083
0.600
1.000
O-Phosphoethanolamine
0.004335
0.880 ± 0.080
0.700
1.000
Phosphorylcholine
0.02622
0.880 ± 0.075
0.600
1.000
sn-Glycerol 3-phosphoethanolamine
0.010908
0.870 ± 0.087
0.900
0.800
Arachidonic Acid (peroxide free)
0.002591
0.880 ± 0.075
0.900
0.700
Glycerophosphocholine
0.004279
0.900 ± 0.073
1.000
0.800
L-Cystine
0.037211
0.890 ± 0.073
0.800
0.900
L-Threonine
0.005467
0.880 ± 0.078
0.900
0.800
N-Acetyl-D-glucosamine
0.002906
0.920 ± 0.060
0.900
0.800
PC (16:0/16:0)
0.000388
0.940 ± 0.051
1.000
0.800
AUC, area under the ROC curve.
Table 5.
The AUC, specificity, and sensitivity of the diagnostic efficacy of
potential lung cancer biomarkers in a comparison of the cancer and lump
groups (AUC > 0.850).
Potential biomarkers
p value
AUC ± Sem
Sensitivity
Specificity
D-Galactarate
0.044737
0.890 ± 0.074
1.000
0.700
L-Alanine
0.015104
0.890 ± 0.073
0.800
0.900
myo-Inositol
0.011372
0.850 ± 0.100
1.000
0.700
Uracil
0.008811
0.850 ± 0.104
0.900
0.900
AUC, area under the ROC curve.
Figure 4.
ROC curve analysis was used to examine the diagnostic efficacy of the
metabolite candidates. (a) Cancer vs. control; (b) lump vs. control; and
(c) cancer vs. lump. AUC, area under the ROC curve; ROC, receiver
operating characteristic.
The AUC, specificity, and sensitivity of the diagnostic efficacy of
potential lung cancer biomarkers in a comparison of the cancer and
control groups (AUC > 0.850).AUC, area under the ROC curve.The AUC, specificity, and sensitivity of the diagnostic efficacy of
potential lung cancer biomarkers in a comparison of the lump and control
groups (AUC > 0.850).AUC, area under the ROC curve.The AUC, specificity, and sensitivity of the diagnostic efficacy of
potential lung cancer biomarkers in a comparison of the cancer and lump
groups (AUC > 0.850).AUC, area under the ROC curve.ROC curve analysis was used to examine the diagnostic efficacy of the
metabolite candidates. (a) Cancer vs. control; (b) lump vs. control; and
(c) cancer vs. lump. AUC, area under the ROC curve; ROC, receiver
operating characteristic.
Discussion
In this study, adenocarcinoma and para-cancerous tissues from 10 patients with lung
cancer, as well as benign lung tumor tissue from 10 patients with benign tumors were
studied using LC-MS/MS-based metabolomics to discover potential lung adenocarcinoma
biomarkers for the diagnosis and prognosis of early-stage lung adenocarcinoma. The
results showed that compared with para-cancerous tissue, 119 and 105 significant
differential metabolites were identified from lung adenocarcinoma and benign tumor
tissues, respectively. Moreover, the comparison between the cancer group and lump
group screened 32 significant differential metabolites. Based on the KEGG pathway
analysis, 43 and 39 significantly enriched metabolic pathways were determined from
lung adenocarcinoma and benign lung tumor samples, respectively. Moreover, when the
lung adenocarcinoma group was compared with the lump group, 17 enriched pathways
were detected.Previous studies have demonstrated that lung cancer may alter levels of the
metabolites involved in the TCA cycle and its related signaling pathways.[21,22] In this study,
levels of metabolites involved with central carbon metabolism in cancer; i.e.,
L-alanine, L-arginine, L-glutamate, and L-asparagine were found. Most cancer cells
depend on aerobic glycolysis rather than oxidative phosphorylation for energy
production, and the presence of glutamine has a significant effect on the production
of ATP in cancer cells. Additionally, glutamine is also a major component of cancer cells,[23] which is in line with our results. Therefore, central carbon metabolism for
energy production in cancer cells is different from that in normal cells. Thus,
differences in metabolic pathways may result in changes in the levels of certain
metabolites in lung adenocarcinoma tissues.Several studies have revealed that aminoacyl-tRNAs function to transfer amino acids
to ribosomes during protein synthesis; therefore, the increased protein synthesis
rate of cancer cells indicates that the level of aminoacyl-tRNA in cancer tissues is
significantly higher than in normal tissue.[23-25] In this study, levels of
following metabolites related to the aminoacyl-tRNA biosynthesis in cancer were
altered: L-alanine, L-arginine, and L-asparagine. These differential metabolites in
lung adenocarcinoma tissue affect the synthesis of aminoacyl-tRNA biosynthesis,
which affect protein synthesis and further regulate tumor cell proliferation. Levels
of most amino acids in lung adenocarcinoma tissues were higher than in normal
tissues. Therefore, the levels of metabolites related to the protein digestion and
absorption were significantly altered in cancer and normal tissues.Choline is essential for the synthesis of the major membrane phospholipidphosphatidylcholine (PC), the methyl donor betaine, and the neurotransmitter
acetylcholine (ACh). It has been reported that abnormal choline metabolism is a
metabolic hallmark of oncogenesis and tumor progression.[26] Previous studies have demonstrated abnormalities in choline uptake and
cholinephospholipid metabolism in cancer cells by imaging tumors with positron
emission tomography (PET) and magnetic resonance spectroscopy (MRS).[27] Higher levels of choline and up-regulated choline kinase activity have been
detected in various cancers.[26,28] Consistently, metabolites in this study, such as
1,2-dioleoyl-sn-glycero-3-phosphatidylcholine,
1-palmitoyl-sn-glycero-3-phosphocholine, and
1-stearoyl-2-hydroxy-sn-glycero-3-phosphocholine, increased choline levels in lung
adenocarcinoma and benign lung tumor tissues compared with non-cancer tissues.Furthermore, it was worth noting that seven metabolites, namely adenosine
3′-monophosphate, creatine, GMP, L-alanine, phosphorylcholine,
N-acetyl-D-glucosamine, and PC (16:0/16:0), were involved in the enriched pathways
and were significantly differentially expressed in either the cancer group or the
lump group compared with the control group. Early studies have reported the
expression and application of L-alanine, phosphorylcholine, and
N-acetyl-D-glucosamine in lung malignancies.[29-31] In a recent study of non-small
cell carcinoma, Ye et al.[32] combined N-acetyl-D-glucosamine with TRAIL, and their results uncovered the
molecular mechanism through which GlcNAc sensitized cancer cells to TRAIL-induced
apoptosis. Thus, with more verification in the future, these metabolites could be
used for lung tumor screening.In summary, this study presents preliminary comparative proteomics data from the
discovery of serum biomarkers in lung adenocarcinoma, and generates a robust set of
candidate proteins for lung adenocarcinoma diagnosis. However, the sample size of
this study was relatively small, and a larger sample size is required for future
systematic studies. To the best of our knowledge, this is the first time that
differences between metabolites and metabolic pathways have been detected by
LC-MS/MS among different lung tissues. The clinical utility of these candidate lung
adenocarcinoma serum biomarker proteins needs to be validated with additional
analytical platforms as well as in independent case/control sample sets in the
future.
Authors: Ching-Hsien Chen; Sarah Statt; Chun-Lung Chiu; Philip Thai; Muhammad Arif; Kenneth B Adler; Reen Wu Journal: Am J Respir Crit Care Med Date: 2014-11-15 Impact factor: 21.405
Authors: Jérôme R Lechien; Amir Nassri; Nadege Kindt; David N Brown; Fabrice Journe; Sven Saussez Journal: Head Neck Date: 2017-09-30 Impact factor: 3.147
Authors: Suryanarayana V Vulimiri; Manoj Misra; Jonathan T Hamm; Matthew Mitchell; Alvin Berger Journal: Chem Res Toxicol Date: 2009-03-16 Impact factor: 3.739
Authors: Craig M Forester; Qian Zhao; Nancy J Phillips; Anatoly Urisman; Robert J Chalkley; Juan A Oses-Prieto; Li Zhang; Davide Ruggero; Alma L Burlingame Journal: Proc Natl Acad Sci U S A Date: 2018-02-21 Impact factor: 11.205
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