ArticleNPJ genomic medicine2021
The salivary metatranscriptome as an accurate diagnostic indicator of oral cancer.
Article in NPJ genomic medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for oral cancer diagnosis: a systematic review and meta-analysis of image-based and non-imaging models.BMC cancer · 2026Pooled it
- Artificial Intelligence Innovations in Understanding Oral Submucous Fibrosis: A Nouvelle Modernistic Approach: A Systematic Review.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled it
- "Enhancing early detection of oral cancer: a comparative study of artificial intelligence models and clinical specialist in lesion classification".BMC cancer · 2025Article
- MicroSTNet: a spatio-temporal graph-based framework for time-series microbiome analysis.Microbial genomics · 2025Article
- A Unified YOLOv8 Approach for Point-of-Care Diagnostics of SalivaryBiosensors · 2025Article
- Recent advancements in artificial intelligence-powered cancer prediction from oral microbiome.Periodontology 2000 · 2025Review
- Preparation of robust synthetic control samples and their use in a metatranscriptomic clinical test.Scientific reports · 2025Article
- Review
- How Have Massively Parallel Sequencing Technologies Furthered Our Understanding of Oncogenesis and Cancer Progression?Methods in molecular biology (Clifton, N.J.) · 2025Review
- Mass Spectrometry-Based Metabolomics Reveals a Salivary Signature for Low-Severity COVID-19.International journal of molecular sciences · 2024Article
- Is Short-Read 16S rRNA Sequencing of Oral Microbiome Sampling a Suitable Diagnostic Tool for Head and Neck Cancer?Pathogens (Basel, Switzerland) · 2024Article
- A review of machine learning methods for cancer characterization from microbiome data.NPJ precision oncology · 2024Review
- Microbiota changes: the unseen players in cervical cancer progression.Frontiers in microbiology · 2024Review
- Microbiomes, Their Function, and Cancer: How Metatranscriptomics Can Close the Knowledge Gap.International journal of molecular sciences · 2023Review
- A salivary biomarker panel to detect liver cirrhosis.iScience · 2023Article
- Investigating the association between the symptoms of women with Fibromyalgia, Digestive function, and markers of the microbiota of the Gastrointestinal Tract (The FIDGIT Study): study protocol.BMC musculoskeletal disorders · 2023Article
- The hitchhikers' guide to RNA sequencing and functional analysis.Briefings in bioinformatics · 2023Article
- Implications of oral dysbiosis and HPV infection in head and neck cancer: from molecular and cellular mechanisms to early diagnosis and therapy.Frontiers in oncology · 2023Review
- Saliva-based microfluidic point-of-care diagnostic.Theranostics · 2023Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
Despite advances in cancer treatment, the 5-year mortality rate for oral cancers (OC) is 40%, mainly due to the lack of early diagnostics. To advance early diagnostics for high-risk and average-risk populations, we developed and evaluated machine-learning (ML) classifiers using metatranscriptomic data from saliva samples (n = 433) collected from oral premalignant disorders (OPMD), OC patients (n = 71) and normal controls (n = 171). Our diagnostic classifiers yielded a receiver operating characteristics (ROC) area under the curve (AUC) up to 0.9, sensitivity up to 83% (92.3% for stage 1 cancer) and specificity up to 97.9%. Our metatranscriptomic signature incorporates both taxonomic and functional microbiome features, and reveals a number of taxa and functional pathways associated with OC. We demonstrate the potential clinical utility of an AI/ML model for diagnosing OC early, opening a new era of non-invasive diagnostics, enabling early intervention and improved patient outcomes.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.