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Cited by 12Gastric Cancer Management and OutcomesMetabolomics and Mass Spectrometry StudiesCancer, Hypoxia, and Metabolism

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Association of serum levels of deoxyribose 1‑phosphate and S‑lactoylglutathione with neoadjuvant chemotherapy sensitivity in patients with gastric cancer: A metabolomics study

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Wang, Daguang, Li, Wei, Yin, Lei, Du, Yechao, Zhang, Shaopeng, Suo, Jian·2020·Oncology Letters
DOI 10.3892/ol.2020.11350PMC PMC7039117
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Abstract

The present study screened serum samples from patients with advanced-stage gastric cancer and known sensitivities to neoadjuvant chemotherapy, in order to identify metabolites that may serve as potential biomarkers for chemotherapy sensitivity. A total of 47 patients with stage III (T4b) or IV gastric cancer, including 31 in the training group and 16 in a validation group, were classified based on their responses to conversion therapy consisting of oxaliplatin, tegafur and continuous hyperthermic peritoneal perfusion with cisplatin. Serum samples were analyzed by liquid chromatography-mass spectrometry to obtain a metabolite profile of each patient. Patients who were responsive and non-responsive to neoadjuvant chemotherapy exhibited significant differences in serum levels of deoxyribose 1-phosphate, S-lactoylglutathione, lysophosphatidylcholine (16:0) and O-arachidonoyl ethanolamine. Logistic regression analysis indicated that deoxyribose 1-phosphate and S-lactoylglutathione were independently associated with chemosensitivity. Serum levels of deoxyribose 1-phosphate and S-lactoylglutathione were independently associated with the sensitivity of gastric cancer to neoadjuvant chemotherapy, therefore, serving as potential predictors of patient response.

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Figure 1. TIC chromatograms of metabolites in the sera of four representative patients with gastric cancer in the training group. (A) TIC profiles of chemo-sensitive in the positive ion mode; (B) TIC profiles of chemo-resistant in the positive ion mode. (C) TIC profiles of chemo-sensitive in the negative ion mode; (D) TIC profiles of chemo-resistant in the negative ion mode. Red arrows indicate metabolites subsequently identified as having different abundances in the two groups. TIC, total ion current.
Figure 1. TIC chromatograms of metabolites in the sera of four representative patients with gastric cancer in the training group. (A) TIC profiles of chemo-sensitive in the positive ion mode; (B) TIC profiles of chemo-resistant in the positive ion mode. (C) TIC profiles of chemo-sensitive in the negative ion mode; (D) TIC profiles of chemo-resistant in the negative ion mode. Red arrows indicate metabolites subsequently identified as having different abundances in the two groups. TIC, total ion current.CC BY-NC-ND · PubMed Central
Figure 2. Principal component analysis of serum samples from the training group (n=31) and the QC sample using the (A) positive ion mode and (B) negative ion mode. Log I Pareto (DA) is a PCA method, which refers to the analysis in Pareto mode after Log conversion. Each dot represents a single sample. Red square represents the chemo-sensitive group, the black diamond represents the chemo-resistant group, and the green triangle represents the QC group. QC, quality control.
Figure 2. Principal component analysis of serum samples from the training group (n=31) and the QC sample using the (A) positive ion mode and (B) negative ion mode. Log I Pareto (DA) is a PCA method, which refers to the analysis in Pareto mode after Log conversion. Each dot represents a single sample. Red square represents the chemo-sensitive group, the black diamond represents the chemo-resistant group, and the green triangle represents the QC group. QC, quality control.CC BY-NC-ND · PubMed Central
Figure 3. Hierarchical clustering analysis of metabolome data for seven metabolites in the training group (n=31). Metabolite levels were used to color code individual samples from green (low concentration) to red (high concentration), with grey denoting absence of the substance. Tree clusters and shorter Euclidean distances indicate greater similarities between samples or metabolites. The numbers represent the patient IDs. The blue color of the top bars represents the chemo-resistant group, and the black color of the top bars represents the chemo-sensitive group. PC, phosphatidylcholine.
Figure 3. Hierarchical clustering analysis of metabolome data for seven metabolites in the training group (n=31). Metabolite levels were used to color code individual samples from green (low concentration) to red (high concentration), with grey denoting absence of the substance. Tree clusters and shorter Euclidean distances indicate greater similarities between samples or metabolites. The numbers represent the patient IDs. The blue color of the top bars represents the chemo-resistant group, and the black color of the top bars represents the chemo-sensitive group. PC, phosphatidylcholine.CC BY-NC-ND · PubMed Central
Figure 4. ROC curves of the seven metabolites and Levels of the four metabolites. (A) ROC curves of the diagnostic performance of each of the seven metabolites in the training group (n=31) in predicting chemosensitivity. Curves closer to the top left corner are metabolites with higher concentrations in chemo-sensitive patients; curves closer to the bottom right corner are metabolites with higher concentrations in chemo-resistant patients. (B) Levels of the four metabolites identified by ROC analysis in patients with chemo-sensitive and chemo-resistant cancer. ROC, receiver operating characteristic; PC, phosphatidylcholine.
Figure 4. ROC curves of the seven metabolites and Levels of the four metabolites. (A) ROC curves of the diagnostic performance of each of the seven metabolites in the training group (n=31) in predicting chemosensitivity. Curves closer to the top left corner are metabolites with higher concentrations in chemo-sensitive patients; curves closer to the bottom right corner are metabolites with higher concentrations in chemo-resistant patients. (B) Levels of the four metabolites identified by ROC analysis in patients with chemo-sensitive and chemo-resistant cancer. ROC, receiver operating characteristic; PC, phosphatidylcholine.CC BY-NC-ND · PubMed Central
Figure 5. Hierarchical clustering analysis of metabolome data for four metabolites in the validation group (n=16). Metabolite levels were used to color code individual samples from green (low concentration) to red (high concentration), with grey denoting absence of the substance. Tree clusters and shorter Euclidean distances indicate the greater similarities between samples or metabolites. The numbers represent the patient IDs. The blue color of the top bars represents the chemo-resistant group, and the black color of the top bars represents the chemo-sensitive group. PC, phosphatidylcholine.
Figure 5. Hierarchical clustering analysis of metabolome data for four metabolites in the validation group (n=16). Metabolite levels were used to color code individual samples from green (low concentration) to red (high concentration), with grey denoting absence of the substance. Tree clusters and shorter Euclidean distances indicate the greater similarities between samples or metabolites. The numbers represent the patient IDs. The blue color of the top bars represents the chemo-resistant group, and the black color of the top bars represents the chemo-sensitive group. PC, phosphatidylcholine.CC BY-NC-ND · PubMed Central

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  1. [37]Salway JG Metabolism at a Glance 3rd Wiley-Blackwell Oxford p128 2013

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Metadata

Authors
Wang, D., Li, W., Yin, L., Du, Y., Zhang, S., & Suo, J.
Affiliations
  • First Hospital of Jilin University (CN)
  • Department of Gastrointestinal Surgery, First Hospital of Jilin University, Changchun, Jilin 130021, P.R. China
Corresponding author
  • Zhang, Shaopeng
  • Suo, Jian
ORCID
Venue
Oncology Letters
Publisher
Spandidos Publications
Published
2020-01-24
Volume
19
Issue
3
Pages
2231-2242
ISSN
  • 1792-1074 (ISSN-L)
  • 1792-1082
DOI
10.3892/ol.2020.11350
PMC
PMC7039117
OpenAlex
W3002528560
Cited by
12
References
50
Citations by year
2025: 3 · 2024: 3 · 2023: 2 · 2022: 2 · 2021: 2
FWCI
0.85
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Oncology Letters (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
Jilin Scientific and Technological Development Program
Topics
  • Health Sciences › Medicine › Oncology › Gastric Cancer Management and Outcomes
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Hypoxia, and Metabolism
Keywords
gastric cancer, neoadjuvant chemotherapy, chemosensitivity, serum metabolites, predictive biomarkers, oxaliplatin, tegafur, continuous hyperthermic peritoneal perfusion, cisplatin, lysophosphatidylcholine, metabolomics

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CoChat. (2026). Citation check: Association of serum levels of deoxyribose 1‑phosphate and S‑lactoylglutathione with neoadjuvant chemotherapy sensitivity in patients with gastric cancer: A metabolomics study. https://app.cochat.ai/papers/10.3892/ol.2020.11350
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Wang, D., Li, W., Yin, L., Du, Y., Zhang, S., & Suo, J. (2020). Association of serum levels of deoxyribose 1‑phosphate and S‑lactoylglutathione with neoadjuvant chemotherapy sensitivity in patients with gastric cancer: A metabolomics study. Oncology Letters. https://doi.org/10.3892/ol.2020.11350