Oral microbiome-derived biomarkers for non-invasive diagnosis of head and neck squamous cell carcinoma.

Abstract

Mounting evidence suggests that sustained microbial dysbiosis is associated with the development of multiple cancers, while the species-level bacterial taxa and metabolic dysfunction of oral microbiome in patients with head and neck squamous cell carcinoma (HNSCC) remains unclear. In this cross-sectional study, comprehensive metagenomic and 16S rRNA amplicon sequencing analyses of oral swab samples from 172 patients were performed. Unsupervised clustering algorithms of relative microbial abundance profiles revealed three distinctive microbiome clusters. Based on the metagenomic and 16S rRNA amplicon sequencing data, machine learning-based methods were used to construct the HNSCC diagnostic classifier, which exhibited high area under the curve values of 0.78-0.89. Our study provided the first exhaustive metagenomic and 16S rRNA amplicon sequencing analyses to date, revealing that microbial-metabolic dysbiosis is a potential risk factor for HNSCC progression and therefore providing a robust theoretical basis for potential diagnostic and therapeutic strategies for HNSCC patients.