ArticleExperimental and therapeutic medicine2019
Targeted metabolomics for serum amino acids and acylcarnitines in patients with lung cancer.
Article in Experimental and therapeutic medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 48 citations in OpenAlex.
- A nomogram based on metabolic profiling to discriminate lung cancer among patients with lung nodules.The Journal of international medical research · 2023Trial
- SEFA: Semantic Embedding-Based Feature Augmentation of Biomedical Language-Model Embeddings Improves Interpretable Metabolomic Prediction of Lung Cancer.Biomedicines · 2026Article
- Acylcarnitines in Cancer Metabolism: Mechanistic Insights and Stratification Potential.Cancers · 2026Review
- HPLC-HRMS and interpretable machine learning decipher serum lipidomic signatures in NSCLC.PeerJ · 2026Article
- Plasma metabolites as biomarkers for screening and differential diagnosis of lung cancer.Journal of cardiothoracic surgery · 2025Article
- Identification of novel serum lipid metabolism potential markers and metabolic pathways for oral cancer: a population-based study.BMC cancer · 2025Article
- Evaluation of the role of EGFR exon 19 747-750 deletion mutation and plasma amino acid profile in the development of lung cancer.Molecular biology reports · 2024Article
- Nitric oxide brings innate immune resistance toJournal of biosciences · 2024Article
- A metabolomics study on carcinogenesis of ground-glass nodules.CytoJournal · 2024Article
- Integrated models of blood protein and metabolite enhance the diagnostic accuracy for Non-Small Cell Lung Cancer.Biomarker research · 2023Article
- Construction of the XGBoost model for early lung cancer prediction based on metabolic indices.BMC medical informatics and decision making · 2023Article
- Development and internal validation of nomograms based on plasma metabolites to predict non-small cell lung cancer risk in smoking and nonsmoking populations.Thoracic cancer · 2023Article
- Article
- Signaling pathways in cancer metabolism: mechanisms and therapeutic targets.Signal transduction and targeted therapy · 2023Review
- High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers.Frontiers in molecular biosciences · 2023Article
- Genetic variants in ALDH1L1 and GLDC influence the serine-to-glycine ratio in Hispanic children.The American journal of clinical nutrition · 2022Article
- Review
- A HILIC-MS/MS method development and validation for the quantitation of 13 acylcarnitines in human serum.Analytical and bioanalytical chemistry · 2022Article
- Metabolomic Fingerprinting for the Detection of Early-Stage Lung Cancer: From the Genome to the Metabolome.International journal of molecular sciences · 2022Review
- Integrative Analysis of Pharmacokinetic and Metabolomic Profiles for Predicting Metabolic Phenotype and Drug Exposure Caused by Sotorasib in Rats.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is one of the most prevalent types of cancer, but accurate diagnosis remains a challenge. The aim of the present study was to create a model using amino acids and acylcarnitines for lung cancer screening. Serum samples were obtained from two groups of patients with lung cancer recruited in 2015 (including 40 patients and 100 matched controls) and 2017 (including 17 patients and 30 matched controls). Using a metabolomics method, 21 metabolites (13 types of amino acids and 8 types of acylcarnitines) were measured using liquid chromatography-tandem mass spectrometry. Data (from the 2015 and 2017 data sets) were analysed using a Mann-Whitney U test, Student's t-test, Welch's F test, receiver-operator characteristic curve or logistic regression in order to investigate the potential biomarkers. Six metabolites (glycine, valine, methionine, citrulline, arginine and C16-carnitine) were indicated to be involved in distinguishing patients with lung cancer from healthy controls. The six discriminating metabolites from the 2017 data set were further analysed using Partial least squares-discriminant analysis (PLS-DA). The PLS-DA model was verified using Spearman's correlation analysis and receiver operating characteristic curve analysis. These results demonstrated that the PLS-DA model using the six metabolites (glycine, valine, methionine, citrulline, arginine and C16-carnitine) had a strong ability to identify lung cancer. Therefore, the PLS-DA model using glycine, valine, methionine, citrulline, arginine and C16-carnitine may become a novel screening tool in patients with lung cancer.
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