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Article Open Access

Non‑targeted metabolomics reveals diagnostic biomarker in the plasma of patients with lung cancer

  • Authors:
    • Yan Liu
    • Yangmin Li
    • Haijun Hou
    • Yanyan Dong
    • Haitong Tian
    • Yanan Zhao
    • Ke Li
    • Jiayuan Zhou
    • Fujie Song
    • Yan Li
  • View Affiliations / Copyright

    Affiliations: Department of Respiratory Medicine, Tongzhou Branch of Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 101121, P.R. China, Emergency Department, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100007, P.R. China, Department of Thoracic Surgery, Tongzhou Branch of Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 101121, P.R. China, Out‑Patient Department, Tongzhou Branch of Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 101121, P.R. China, Department of Radiology, Tongzhou Branch of Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 101121, P.R. China
    Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 16
    |
    Published online on: October 31, 2025
       https://doi.org/10.3892/ol.2025.15369
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Abstract

Lung cancers are malignant tumors with high incidence and mortality rates and a 5‑year survival rate that depends on the stage at the time of diagnosis. Screening methods for lung cancer are becoming increasingly diverse, but existing approaches lack sensitivity or specificity for early lesions, posing notable challenges for the early diagnosis of lung cancer. Therefore, it is essential to explore potential biomarkers with high sensitivity and specificity to achieve early diagnosis. The present study employed liquid chromatography‑mass spectrometry to analyze plasma metabolic changes in patients with lung cancer or pulmonary nodules and in healthy individuals. By combining quantitative and qualitative methods, differential metabolites were identified and the performance of these metabolites was evaluated using receiver operating characteristic curve analyses to screen for potential biomarkers. A total of 50 differential metabolites, six of which had an area under the curve of >0.9, were identified among the three groups and regarded as potential biomarkers for distinguishing between lung cancer, pulmonary nodules and healthy individuals, as well as between lung cancer and pulmonary nodules. In addition, metabolic pathways were screened using the Kyoto Encyclopedia of Genes and Genomes databases. The results demonstrated that significant changes were observed in the metabolism of substances including linoleic acid, α‑linolenic acid, arginine and proline, suggesting that the development of lung cancer may be associated with alterations in amino acid and lipid metabolism. In conclusion, the present findings provided potential biomarkers to differentiate between lung cancer, pulmonary nodules and healthy individuals, offering insights into the pathological mechanisms of lung cancer.
View Figures

Figure 1

Examples of chest CT images for lung
cancer and pulmonary nodule groups. (A) Chest CT image of a
50-year-old female patient with lung cancer. (B) Chest CT image of
a 60-year-old male patient with lung cancer. (C) Chest CT image of
a 63-year-old female patient with pulmonary nodule who underwent
pathological examination. (D) Chest CT image of a 51-year-old
female patient with pulmonary nodule who underwent pathological
examination. (E) Chest CT image of a 45-year-old male patient with
pulmonary nodule without pathological examination. (F) Chest CT
image of a 44-year-old female patient with pulmonary nodule without
pathological examination. The lesions are highlighted by red boxes
in all images.

Figure 2

PCA score plots of plasma data from
QC samples, pn, lc and hlt groups. (A) PCA score chart of plasma
data from QC samples and other samples in the negative ion mode.
(B) PCA score chart of plasma data from the pn, the lc and the hlt
groups in the negative ion mode. (C) PCA score chart of plasma data
from QC samples and other samples in the positive ion mode. (D) PCA
score chart of plasma data from the pn, the lc and the hlt groups
in the positive ion mode. Red indicates the hlt group; blue
indicates the pn group; yellow indicates the lc group. PCA,
principal component analysis; QC, quality control; hlt, healthy
group; pn, pulmonary nodule group; lc, lung cancer group.

Figure 3

OPLS-DA score scatter plots and
permutation test plots of plasma data from the pn and the hlt
groups. (A and B) OPLS-DA score scatter plots in (A) negative ion
mode (R2Y=0.986, Q2=0.593) and (B) positive ion mode (R2Y=0.978,
Q2=0.527). (C and D) permutation test plots of OPLS-DA in the (C)
negative ion mode (intercept of Q2 with the y-axis, −0.308) and (D)
positive ion mode (intercept of Q2 with the y-axis, −0.275). R2Y
estimates the goodness of fit of the model that represents the
fraction of explained Y-variation and Q2 estimates the ability of
prediction. OPLS-DA, orthogonal partial least squares-discriminant
analysis; hlt, healthy group; pn, pulmonary nodule group; lc, lung
cancer group.

Figure 4

OPLS-DA score scatter plots and
permutation test plots of plasma data from the lc and the hlt
groups. (A and B) OPLS-DA score scatter plots in (A) negative ion
mode (R2Y=0.967, Q2=0.564) and (B) positive ion mode (R2Y=0.993,
Q2=0.630). (C and D) permut ation test plots of OPLS-DA in (C)
negative ion mode (intercept of Q2 with the y-axis, −0.347) and (D)
positive ion mode (intercept of Q2 with the y-axis, −0.265). R2Y
estimates the goodness of fit of the model that represents the
fraction of explained Y-variation and Q2 estimates the ability of
prediction. OPLS-DA, orthogonal partial least squares-discriminant
analysis; hlt, healthy group; pn, pulmonary nodule group; lc, lung
cancer group.

Figure 5

OPLS-DA score scatter plots and
permutation test plots of plasma data from the pn and the lc
groups. (A and B) OPLS-DA score scatter plots in (A) negative ion
mode (R2Y=0.994, Q2=0.319) and (B) positive ion mode (R2Y=0.922,
Q2=0.563). (C and D) permutation test plots of OPLS-DA in (C)
negative ion mode (intercept of Q2 with the y-axis, −0.398) and (D)
positive ion mode (intercept of Q2 with the y-axis, −0.274).
OPLS-DA, orthogonal partial least squares-discriminant analysis;
hlt, healthy group; pn, pulmonary nodule group; lc, lung cancer
group. R2Y estimates the goodness of fit of the model that
represents the fraction of explained Y-variation and Q2 estimates
the ability of prediction.

Figure 6

Heatmap and Venn diagram of
differential metabolites. (A) Heatmap of differential metabolites
for the pn and the hlt groups. (B) Heatmap of differential
metabolites for the lc and the hlt groups. (C) Heatmap of
differential metabolites for the pn and the lc groups. (D) Venn
diagram of the differential metabolites of the three comparisons.
In the heatmap, blue represents downregulation and red represents
upregulation. hlt, healthy group; pn, pulmonary nodule group; lc,
lung cancer group.

Figure 7

ROC curves of differential
metabolites. (A) ROC curve for distinguishing between the pn and
hlt groups based on pyroglutamic acid,
2-hydroxyhexadecanoylcarnitine, pantothenic acid and urocanic acid.
(B) ROC curve for distinguishing between the lc and hlt groups
based on 3-methoxytyrosine, pantothenic acid and
2-hydroxyhexadecanoylcarnitine. (C) ROC curve for distinguishing
between the pn and lc groups based on dodecanedioic acid. ROC,
receiver operating characteristic; hlt, healthy group; pn,
pulmonary nodule group; lc, lung cancer group; AUC, area under the
curve.

Figure 8

Bubble plot of KEGG pathway analysis.
(A) Bubble plot of KEGG pathway analysis for the pn and the hlt
groups. (B) Bubble plot of KEGG pathway analysis for the lc and the
hlt groups. (C) Bubble plot of KEGG pathway analysis for the pn and
the lc groups. KEGG, Kyoto Encyclopedia of Genes and Genomes; hlt,
healthy group; pn, pulmonary nodule group; lc, lung cancer group.
Node color and radius are based on the P-value and pathway impact
value, respectively. The larger the circle is, the larger the
influence factor is. The deeper red color has the larger value of-
log(p), which means the more significant enrichment.
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Copy and paste a formatted citation
Spandidos Publications style
Liu Y, Li Y, Hou H, Dong Y, Tian H, Zhao Y, Li K, Zhou J, Song F, Li Y, Li Y, et al: Non‑targeted metabolomics reveals diagnostic biomarker in the plasma of patients with lung cancer. Oncol Lett 31: 16, 2026.
APA
Liu, Y., Li, Y., Hou, H., Dong, Y., Tian, H., Zhao, Y. ... Li, Y. (2026). Non‑targeted metabolomics reveals diagnostic biomarker in the plasma of patients with lung cancer. Oncology Letters, 31, 16. https://doi.org/10.3892/ol.2025.15369
MLA
Liu, Y., Li, Y., Hou, H., Dong, Y., Tian, H., Zhao, Y., Li, K., Zhou, J., Song, F., Li, Y."Non‑targeted metabolomics reveals diagnostic biomarker in the plasma of patients with lung cancer". Oncology Letters 31.1 (2026): 16.
Chicago
Liu, Y., Li, Y., Hou, H., Dong, Y., Tian, H., Zhao, Y., Li, K., Zhou, J., Song, F., Li, Y."Non‑targeted metabolomics reveals diagnostic biomarker in the plasma of patients with lung cancer". Oncology Letters 31, no. 1 (2026): 16. https://doi.org/10.3892/ol.2025.15369
Copy and paste a formatted citation
x
Spandidos Publications style
Liu Y, Li Y, Hou H, Dong Y, Tian H, Zhao Y, Li K, Zhou J, Song F, Li Y, Li Y, et al: Non‑targeted metabolomics reveals diagnostic biomarker in the plasma of patients with lung cancer. Oncol Lett 31: 16, 2026.
APA
Liu, Y., Li, Y., Hou, H., Dong, Y., Tian, H., Zhao, Y. ... Li, Y. (2026). Non‑targeted metabolomics reveals diagnostic biomarker in the plasma of patients with lung cancer. Oncology Letters, 31, 16. https://doi.org/10.3892/ol.2025.15369
MLA
Liu, Y., Li, Y., Hou, H., Dong, Y., Tian, H., Zhao, Y., Li, K., Zhou, J., Song, F., Li, Y."Non‑targeted metabolomics reveals diagnostic biomarker in the plasma of patients with lung cancer". Oncology Letters 31.1 (2026): 16.
Chicago
Liu, Y., Li, Y., Hou, H., Dong, Y., Tian, H., Zhao, Y., Li, K., Zhou, J., Song, F., Li, Y."Non‑targeted metabolomics reveals diagnostic biomarker in the plasma of patients with lung cancer". Oncology Letters 31, no. 1 (2026): 16. https://doi.org/10.3892/ol.2025.15369
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