ArticleThoracic cancer2022
Differential diagnosis of lung cancer and benign lung lesion using salivary metabolites: A preliminary study.
Article in Thoracic cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 31 citations in OpenAlex.
- Systematic verification of saliva-based biosignatures for oral cancer via targeted metabolomics.Journal of food and drug analysis · 2026Article
- Cross-sectional associations between CES-D scores and the salivary metabolome in nonclinical adults.Discover mental health · 2026Article
- Breath-based lung cancer detection using an ML-driven low-cost sensor array.Scientific reports · 2025Article
- Metabolite biomarkers in lung cancer: unlocking the potential of body fluid analysis for early detection and prognosis-a narrative review.Translational lung cancer research · 2025Review
- LncRNA MIR99AHG serves as a diagnostic marker for early lung cancer and regulates cancer cell growth and metastasis via miR-194-5p.Discover oncology · 2025Article
- Rapid and Noninvasive Early Detection of Lung Cancer by Integration of Machine Learning and Salivary Metabolic Fingerprints Using MS LOC Platform: A Large-Scale Multicenter Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics.BMC pulmonary medicine · 2025Article
- Salivary metabolomic biomarkers for esophageal and gastric cancers by liquid chromatography-mass spectrometry.Cancer science · 2024Article
- Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics.Breast cancer research and treatment · 2024Article
- Salivary metabolites as potential predictive biomarkers for lung surgery complications: a retrospective cross-sectional study.Surgery today · 2024Article
- Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study.BMC cancer · 2024Article
- Liquid biopsy techniques and lung cancer: diagnosis, monitoring and evaluation.Journal of experimental & clinical cancer research : CR · 2024Review
- Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types.International journal of medical sciences · 2024Review
- Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring.Frontiers in oncology · 2024Review
- Diagnostic Value of Salivary Amino Acid Levels in Cancer.Metabolites · 2023Review
- Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges?Cancers · 2023Review
- A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images.Frontiers in oncology · 2023Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSaliva is often used as a biomarker for the diagnosis of some oral and systematic diseases, owing to the non-invasive attribute of the fluid. In this study, we aimed to identify salivary biomarkers for distinguishing lung cancer (LC) from benign lung lesion (BLL). MATERIALS AND
methodsUnstimulated saliva samples were collected from 41 patients with LC and 21 with BLL. Salivary metabolites were comprehensively analyzed using capillary electrophoresis mass spectrometry. To differentiate between patients with LCs and BLLs, the discriminatory ability of each biomarker was assessed. Furthermore, a multiple logistic regression (MLR) model was developed for evaluating discriminatory ability of each salivary metabolite.
resultsThe profiles of 10 salivary metabolites were remarkably different between the LC and BLL samples. Among them, the concentration of salivary tryptophan was significantly lower in the samples from patients with LC than in those from patients with BLL, and the area under the curve (AUC) for discriminating patients with LC from those with BLL was 0.663 (95% confidence interval [CI] = 0.516-0.810, p = 0.036). Furthermore, from the MLR model developed using these metabolites, diethanolamine, cytosine, lysine, and tyrosine, were selected using the back-selection regression method. The MLR model based on these four metabolites had a high discriminatory ability for patients with LC and those with BLL (AUC = 0.729, 95% CI = 0.598-0.861, p = 0.003).
conclusionThe four salivary metabolites can serve as potential non-invasive biomarkers for distinguishing LC from BLL.
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