ArticleClinical oral investigations2021
Evaluation of salivary VOC profile composition directed towards oral cancer and oral lesion assessment.
Article in Clinical oral investigations, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Exploring Components, Sensors, and Techniques for Cancer Detection via eNose Technology: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- GC-IMS-based analysis of serum volatile organic compounds for diagnosis of gastric cancer.Scientific reports · 2026Article
- Application of artificial intelligence in oral potentially malignant disorders: current opinions and future barriers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Comprehensive GC/MS Profiling of Volatile Organic Compounds in Whole and Glandular Saliva Using MonoTrap Micro-Extraction.Metabolites · 2025Article
- Characterizing volatile organic compound profiles in oral cancer using multiple sample collection approaches by GC-IMS and TD-GC-MS.Scientific reports · 2025Article
- Review
- Recent advancements in artificial intelligence-powered cancer prediction from oral microbiome.Periodontology 2000 · 2025Review
- Non-invasive multiple cancer screening using trained detection canines and artificial intelligence: a prospective double-blind study.Scientific reports · 2024Article
- Mediating role of systemic inflammation in the association between volatile organic compounds exposure and periodontitis: NHANES 2011-2014.BMC oral health · 2024Article
- The human volatilome meets cancer diagnostics: past, present, and future of noninvasive applications.Metabolomics : Official journal of the Metabolomic Society · 2024Review
- From multi-omics to predictive biomarker: AI in tumor microenvironment.Frontiers in immunology · 2024Review
- Identification of urinary volatile organic compounds as a potential non-invasive biomarker for esophageal cancer.Scientific reports · 2023Article
- Differences in the Volatilomic Urinary Biosignature of Prostate Cancer Patients as a Feasibility Study for the Detection of Potential Biomarkers.Current oncology (Toronto, Ont.) · 2023Article
- Volatilomics: An Emerging and Promising Avenue for the Detection of Potential Prostate Cancer Biomarkers.Cancers · 2022Review
- Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS.Metabolites · 2022Article
- Applications of Medium-Chain Triglycerides in Foods.Frontiers in nutrition · 2022Review
- Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner.Frontiers in oncology · 2022Article
- Advances in the Diagnosis, Monitoring, and Progression of Oral Cancer through Saliva: An Update.BioMed research international · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
objectivesEndogenous substances have been analyzed in biological samples in order to be related with metabolic dysfunctions and diseases. The study aimed to investigate profiles of volatile organic compounds (VOCs) from fresh and incubated saliva donated by healthy controls, individuals with oral tissue lesions and with oral cancer, in order to assess case-specific biomarkers of oxidative stress. MATERIALS AND
methodsVOCs were pre-concentrated using headspace-solid phase microextraction and analyzed using gas chromatography-mass spectrometry. Then, VOCs positively modulated by incubation process were subtracted, yielding profiles with selected features. Principal component analysis and hierarchical cluster analysis were used to inspect data distribution, while univariate statistics was applied to indicate potential markers of oral cancer. Machine learning algorithm was implemented, aiming multiclass prediction.
resultsThe removal of bacterial contribution to VOC profiles allowed the obtaining of more specific case-related patterns. Artificial neural network model included 9 most relevant compounds (1-octen-3-ol, hexanoic acid, E-2-octenal, heptanoic acid, octanoic acid, E-2-nonenal, nonanoic acid, 2,4-decadienal and 9-undecenoic acid). Model performance was assessed using 10-fold cross validation and receiver operating characteristic curves. Obtained overall accuracy was 90%. Oral cancer cases were predicted with 100% of sensitivity and specificity.
conclusionsThe selected VOCs were ascribed to lipid oxidation mechanism and presented potential to differentiate oral cancer from other inflammatory conditions. CLINICAL RELEVANCE: These results highlight the importance of interpretation of saliva composition and the clinical value of salivary VOCs. Elucidated metabolic alterations have the potential to aid the early detection of oral cancer and the monitoring of oral lesions.
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