ArticleCancer medicine2023
Integration of metabolomics and machine learning revealed tryptophan metabolites are sensitive biomarkers of pemetrexed efficacy in non-small cell lung cancer.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 14 citations in OpenAlex.
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- Acylcarnitines in Cancer Metabolism: Mechanistic Insights and Stratification Potential.Cancers · 2026Review
- Lipid Biomarkers in Glioma: Unveiling Molecular Heterogeneity Through Tissue and Plasma Profiling.International journal of molecular sciences · 2025Article
- Machine Learning-Driven Insights in Cancer Metabolomics: From Subtyping to Biomarker Discovery and Prognostic Modeling.Metabolites · 2025Review
- The Emerging Roles of Metabolic Reprogramming in Non-Small Cell Lung Cancer Progression.Frontiers in bioscience (Landmark edition) · 2025Review
- Plasma metabolomics for the preoperative diagnosis of parathyroid carcinoma.Endocrine-related cancer · 2025Article
- Emerging role of the TCA cycle and its metabolites in lung disease.Frontiers in physiology · 2025Review
- Explainable Machine Learning Predictions for the Benefit From Chemotherapy in Advanced Non-Small Cell Lung Cancer Without Available Targeted Mutations.The clinical respiratory journal · 2024Article
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- Non-Specific Elevated Serum Free Fatty Acids in Lung Cancer Patients: Nutritional or Pathological?Nutrients · 2024Article
- From multi-omics to predictive biomarker: AI in tumor microenvironment.Frontiers in immunology · 2024Review
- The integrated analysis of gut microbiota and metabolome revealed steroid hormone biosynthesis is a critical pathway in liver regeneration after 2/3 partial hepatectomy.Frontiers in pharmacology · 2024Article
- Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics.Metabolites · 2023Article
- Integration of metabolomics and machine learning revealed tryptophan metabolites are sensitive biomarkers of pemetrexed efficacy in non-small cell lung cancer.Cancer medicine · 2023Article
- Metabolomic Characteristics of Patients With Non-Small Cell Lung Cancer in Plasma, Tissue and Exhaled Breath Condensate: A Case-Control Study.Technology in cancer research & treatmentArticle
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAnti-folate drug pemetrexed is a vital chemotherapy medication for non-small cell lung cancer (NSCLC). Its response varies widely and often develops resistance to the treatment. Therefore, it is urgent to identify biomarkers and establish models for drug efficacy evaluation and prediction for rational drug use.
methodsA total of 360 subjects were screened and 323 subjects were recruited. Using metabolomics in combination with machine learning methods, we are trying to select potential biomarkers to diagnose NSCLC and evaluate the efficacy of pemetrexed in treating NSCLC. Furtherly, we measured the concentration of eight metabolites in the tryptophan metabolism pathway in the validation set containing 201 subjects using a targeted metabolomics method with UPLC-MS/MS.
resultsIn the discovery set containing 122 subjects, the metabolic profile of healthy controls (H), newly diagnosed NSCLC patients (ND), patients who responded well to pemetrexed treatment (S) and pemetrexed-resistant patients (R) differed significantly on the PLS-DA scores plot. Pathway analysis showed that glycine, serine and threonine metabolism occurred in every two group comparisons. TCA cycle, pyruvate metabolism and glycerolipid metabolism are the most significantly changed pathways between ND and H group, pyruvate metabolism was the most altered pathway between S and ND group, and tryptophan metabolism was the most changed pathway between S and R group. We found Random forest method had the maximum area under the curve (AUC) and can be easily interpreted. The AUC is 0.981 for diagnosing patients with NSCLC and 0.954 for evaluating pemetrexed efficiency.
conclusionWe compared eight mathematical models to evaluate pemetrexed efficiency for treating NSCLC. The Random forest model established with metabolic markers tryptophan, kynurenine and xanthurenic acidcan accurately diagnose NSCLC and evaluate the response of pemetrexed.
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