Limitations of using 16S rRNA microbiome sequencing to predict oral squamous cell carcinoma.

A new era of next-generation sequencing has changed our perception of the oral microbiome in health and disease, and with this there is a growing understanding that the oral microbiome is a contributing factor to oral squamous cell carcinoma (OSCC), a malignancy of the oral cavity. This study aimed to analyse the trends and relevant literature based on the 16S rRNA oral microbiome in head and neck cancer using next-generation sequencing technologies, and to conduct a meta-analysis of the studies with OSCC cases and healthy controls. A literature search using the databases Web of Science and PubMed was conducted in a scoping-like review to collect information based on the study design, and plots were generated using RStudio. We selected case-control studies using 16S rRNA oral microbiome sequencing analysis in OSCC cases versus healthy controls for re-analysis. Statistical analyses were conducted using R. Out of 916 original articles, we filtered and selected 58 studies for review, and 11 studies for meta-analysis. Differences between sampling type, DNA extraction methods, next-generation sequencing technology and region of the 16S rRNA were identified. No significant differences in the α- and ß-diversity between health and oral squamous cell carcinoma were observed (p < 0.05). Random Forest classification marginally improved predictability of four studies (training set) when split 80/20. We found an increase in Selenomonas, Leptotrichia and Prevotella species to be indicative of disease. A number of technological advances have been accomplished to study oral microbial dysbiosis in oral squamous cell carcinoma. There is a clear need for standardization of study design and methodology to ensure 16S rRNA outputs are comparable across the discipline in the hope of identifying 'biomarker' organisms for designing screening or diagnostic tools.

Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes.

As one of the most prevalent infective diseases worldwide, it is crucial that we not only know the constituents of the oral microbiome in dental caries but also understand its functionality. Herein, we present a reproducible meta-analysis to effectively report the key components and the associated functional signature of the oral microbiome in dental caries. Publicly available sequencing data were downloaded from online repositories and subjected to a standardized analysis pipeline before analysis. Meta-analyses identified significant differences in alpha and beta diversities of carious microbiomes when compared to healthy ones. Additionally, machine learning and receiver operator characteristic analysis showed an ability to discriminate between healthy and disease microbiomes. We identified from importance values, as derived from random forest analyses, a group of genera, notably containing Selenomonas, Aggregatibacter, Actinomyces and Treponema, which can be predictive of dental caries. Finally, we propose the most appropriate study design for investigating the microbiome of dental caries by synthesizing the studies, which had the most accurate differentiation based on random forest modelling. In conclusion, we have developed a non-biased, reproducible pipeline, which can be applied to microbiome meta-analyses of multiple diseases, but importantly we have derived from our meta-analysis a key group of organisms that can be used to identify individuals at risk of developing dental caries based on oral microbiome inhabitants.