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. 2015 Apr;141(4):705-18.
doi: 10.1007/s00432-014-1846-5. Epub 2014 Oct 8.

Metabolomic profiling of human serum in lung cancer patients using liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry and gas chromatography/mass spectrometry

Yingrong Chen  1 , Zhihong Ma, Aiying Li, Hongwei Li, Bin Wang, Jing Zhong, Lishan Min, Licheng Dai
Affiliations

Metabolomic profiling of human serum in lung cancer patients using liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry and gas chromatography/mass spectrometry

Yingrong Chen et al. J Cancer Res Clin Oncol. 2015 Apr.

Abstract

Purpose: Lung cancer is one of the most common causes of death from cancer. Serum markers that enable diagnosis of the disease in the early stage have not been found.

Methods: Serum samples were collected from 30 healthy volunteers and from 30 lung cancer patients preoperatively and postoperatively. Samples were subjected to metabolomic analysis using liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry and gas chromatography/mass spectrometry. Differences in metabolomic profiles among the three groups were characterized by multivariate statistical techniques such as principal components analysis and partial least squares discriminant analysis (PLS-DA). An independent t test was used to determine whether levels of biomarker candidates identified using PLS-DA modeling were significantly different among groups at the univariate analysis level (p < 0.05).

Results: Based on pattern recognition results and univariate analysis, we showed that levels of ten potential biomarkers in serum were significantly different in the preoperative lung cancer patients compared with healthy volunteers and/or the postoperative lung cancer patients. The levels of sphingosine, phosphorylcholine, glycerophospho-N-arachidonoyl ethanolamine, γ-linolenic acid, 9,12-octadecadienoic acid, oleic acid, and serine were significantly different in preoperative lung cancer patients compared to healthy volunteers and to postoperative lung cancer patients. For prasterone sulfate, α-hydroxyisobutyric acid, 2,3,4-trihydroxybutyric acid, the levels were statistically different in preoperative and postoperative lung cancer patients compared with the healthy volunteers.

Conclusions: Our study identified potential metabolic biomarkers for diagnosis of lung cancer.

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Conflict of interest statement

None.

Figures

Fig. 1
Fig. 1
Representative LC/MS total ion chromatograms of the serum samples from a a healthy volunteer, b a preoperative lung cancer patient, and c a postoperative lung cancer patient
Fig. 2
Fig. 2
Representative GC/MS total ion chromatograms of the serum samples from a a healthy volunteer, b a preoperative lung cancer patient, and c a postoperative lung cancer patient
Fig. 3
Fig. 3
Principal component analysis (PCA) scores plots based on LC/MS data of serum samples. a PCA score plot data from healthy volunteers (green) versus preoperative lung cancer patients (red), b PCA score plot data from healthy volunteers (green) versus postoperative lung cancer patients (blue), c PCA score plot data from preoperative lung cancer patients (red) versus postoperative lung cancer patients (blue), and d PCA score plot data from all three groups
Fig. 4
Fig. 4
Principal component analysis (PLS-DA) scores plots based on LC/MS data of serum samples. a PLS-DA score plot data from healthy volunteers (green) versus preoperative lung cancer patients (red), b PLS-DA score plot data from healthy volunteers (green) versus postoperative lung cancer patient (blue), and c PLS-DA score plot data from preoperative lung cancer patients (red) versus postoperative lung cancer patient (blue)
Fig. 5
Fig. 5
Principal component analysis (PCA) scores plots based on GC/MS data of serum samples. a PCA score plot data from healthy volunteers (green) versus preoperative lung cancer patients (red), b PCA score plot data from healthy volunteers (green) versus postoperative lung cancer patients (blue), c PCA score plot data from preoperative lung cancer patients (red) versus postoperative lung cancer patients (blue), and d PCA score plot data from all three groups
Fig. 6
Fig. 6
Principal component analysis (PLS-DA) scores plots based on GC/MS data of serum samples. a PLS-DA score plot data from healthy volunteers (green) versus preoperative lung cancer patients (red), b PLS-DA score plot data from healthy volunteers (green) versus postoperative lung cancer patient (blue), and c PLS-DA score plot data from preoperative lung cancer patients (red) versus postoperative lung cancer patient (blue)
Fig. 7
Fig. 7
Box plots of the most important potential biomarkers based on LC/MS (panels on the left) and GC/MS (panels on the right) data. The boxes are drawn from the 25th to 75th percentiles in the intensity distribution. The median, or 50th percentile, is drawn as black horizontal line inside the box. Potential biomarkers are as follows: a sphingosine, b phosphorylcholine, c glycerophospho-N-arachidonoyl ethanolamine, d γ-linolenic acid, e prasterone sulfate, f 9,12-octadecadienoic acid, g oleic acid, h serine, i α-hydroxyisobutyric acid, and j 2,3,4-trihydroxybutyric acid
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