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- Title
The salivary metatranscriptome as an accurate diagnostic indicator of oral cancer.
- Authors
Banavar, Guruduth; Ogundijo, Oyetunji; Toma, Ryan; Rajagopal, Sathyapriya; Lim, Yen Kai; Tang, Kai; Camacho, Francine; Torres, Pedro J.; Gline, Stephanie; Parks, Matthew; Kenny, Liz; Perlina, Ally; Tily, Hal; Dimitrova, Nevenka; Amar, Salomon; Vuyisich, Momchilo; Punyadeera, Chamindie
- 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.
- Subjects
ORAL cancer; CANCER treatment; MACHINE learning; PUBLIC health; HEALTH outcome assessment
- Publication
NPJ Genomic Medicine, 2021, Vol 6, Issue 1, p1
- ISSN
2056-7944
- Publication type
Article
- DOI
10.1038/s41525-021-00257-x