ArticleProceedings of the National Academy of Sciences of the United States of America2020
Oral squamous cell carcinoma diagnosed from saliva metabolic profiling.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 3 of them syntheses that pooled it.
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Who cites it
79 citing papers in PubMed, 3 syntheses or guidelines pooled it, 162 citations in OpenAlex.
- Artificial Intelligence Innovations in Understanding Oral Submucous Fibrosis: A Nouvelle Modernistic Approach: A Systematic Review.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled it
- Extensive comparison of salivary collection, transportation, preparation, and storage methods: a systematic review.BMC oral health · 2024Pooled it
- Salivary metabolomics in oral potentially malignant disorders and oral cancer patients-a systematic review with meta-analysis.Clinical oral investigations · 2024Pooled it
- Microbiota and head and neck cancers: Mechanistic insights and translational potential.Virulence · 2026Review
- Salivary metabolic profile in patients with oral squamous cell carcinoma and potentially malignant disorders.Clinical oral investigations · 2026Article
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Systematic verification of saliva-based biosignatures for oral cancer via targeted metabolomics.Journal of food and drug analysis · 2026Article
- Linking metabolism and metastasis: elevated α-hydroxybutyric acid in oral squamous cell carcinoma patients with lymph node metastasis.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Review
- Innovative Biomarkers for Oral Immunity: Advancements and Clinical Applications.Advances in experimental medicine and biology · 2026Review
- Salivary biomarkers in oral cancer diagnosis: advancing conventional treatment strategies.Military Medical Research · 2026Review
- Serum metabolomics reveals distinctive signature of cancer staging in oral squamous cell carcinoma.BMC oral health · 2025Article
- Exploring Metabolic and Immunological Biomarkers for Oral Squamous Cell Carcinoma: Potential Targets for Precision Therapy.Biology · 2025Review
- Cell-Free DNA as a Prognostic Biomarker in Oral Carcinogenesis and Oral Squamous Cell Carcinoma: A Translational Perspective.Cancers · 2025Review
- Salivary Metabolomics Discloses Metabolite Signatures of Oral Leukoplakia with and Without Dysplasia.International journal of molecular sciences · 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
- Recent advancements in artificial intelligence-powered cancer prediction from oral microbiome.Periodontology 2000 · 2025Review
- Saliva metabolomics: a non-invasive frontier for diagnosing and managing oral diseases.Journal of translational medicine · 2025Review
- Malignant Transformed and Non-Transformed Oral Leukoplakias Are Metabolically Different.International journal of molecular sciences · 2025Article
- Evaluation of Peri-Implantitis through Fourier-Transform Infrared Spectroscopy on Saliva.Journal of proteome research · 2025Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Saliva is a noninvasive biofluid that can contain metabolite signatures of oral squamous cell carcinoma (OSCC). Conductive polymer spray ionization mass spectrometry (CPSI-MS) is employed to record a wide range of metabolite species within a few seconds, making this technique appealing as a point-of-care method for the early detection of OSCC. Saliva samples from 373 volunteers, 124 who are healthy, 124 who have premalignant lesions, and 125 who are OSCC patients, were collected for discovering and validating dysregulated metabolites and determining altered metabolic pathways. Metabolite markers were reconfirmed at the primary tissue level by desorption electrospray ionization MS imaging (DESI-MSI), demonstrating the reliability of diagnoses based on saliva metabolomics. With the aid of machine learning (ML), OSCC and premalignant lesions can be distinguished from the normal physical condition in real time with an accuracy of 86.7%, on a person by person basis. These results suggest that the combination of CPSI-MS and ML is a feasible tool for accurate, automated diagnosis of OSCC in clinical practice.
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Registered trials
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