Taxonomic and Functional Dysregulation in Salivary Microbiomes During Oral Carcinogenesis

A review of machine learning methods for cancer characterization from microbiome data

Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

Salivary biomarkers can improve the efficacy, efficiency, and timeliness of oral and maxillofacial disease diagnosis and monitoring. Oral and maxillofacial conditions in which salivary biomarkers have been utilized for disease-related outcomes include periodontal diseases, dental caries, oral cancer, temporomandibular joint dysfunction, and salivary gland diseases. However, given the equivocal accuracy of salivary biomarkers during validation, incorporating contemporary analytical techniques for biomarker selection and operationalization from the abundant multi-omics data available may help improve biomarker performance. Artificial intelligence represents one such advanced approach that may optimize the potential of salivary biomarkers to diagnose and manage oral and maxillofacial diseases. Therefore, this review summarized the role and current application of techniques based on artificial intelligence for salivary biomarker discovery and validation in oral and maxillofacial diseases.

No Clear Clustering Dysbiosis from Salivary Microbiota Analysis by Long Sequencing Reads in Patients Affected by Oral Squamous Cell Carcinoma: A Single Center Study

BACKGROUND

Advancements in DNA sequencing technology have facilitated the assessment of the connection between the oral microbiome and various diseases. The aim of the present study was to investigate the salivary microbiota composition employing for the first time in the literature the Oxford Nanopore Technology in patients affected by oral squamous cell carcinoma (OSCC).

METHODS

Unstimulated saliva samples of 31 patients were collected (24 OSCC patients and 7 controls). DNA was extracted using the QIAamp DNA Blood Kit and metagenomic long sequencing reads were performed using the MinION device.

RESULTS

In the OSCC group, 13 were males and 11 were females, with a mean age of 65.5 ± 13.9 years; in the control group, 5 were males and 2 were females, with a mean age of 51.4 ± 19.2 years. The border of the tongue was the most affected OSCC site. The microorganisms predominantly detected in OSCC patients were Prevotella, Chlamydia, Tissierellia, Calothrix, Leotiomycetes, Firmicutes and Zetaproteobacteria.

CONCLUSIONS

This study confirmed the predominance of periodontopathic bacteria in the salivary microbiome in the OSCC group. If a direct correlation between oral dysbiosis and OSCC onset was proven, it could lead to new prevention strategies and early diagnostic tools.

Oral dysbiosis in the onset and carcinogenesis of oral epithelial dysplasia: A systematic review

OBJECTIVE

This systematic review aims to investigate possible connections between the oral microbiome and the onset and carcinogenesis of oral epithelial dysplasia (OED).

METHODS

A systematic search was performed on PubMed, Embase, Cochrane Database, and SCOPUS by two authors independently, addressing the focused question- "Has oral microbiome dysbiosis been involved in the onset and carcinogenesis of oral epithelial dysplasia?" We used the Newcastle-Ottawa scale to assess the quality of studies included in the review.

RESULTS

Out of 580 references screened, ten studies were found eligible for inclusion. All studies were case-control studies, and only qualitative analysis was conducted due to heterogeneous characteristics. The overall risk of bias in the eligible studies was considered as high. Microbiome diversity indices showed inconsistent evidence among studies. A significant increase of phylum Bacteroidetes in OED patients was reported in five studies. Five studies reported an increase of genus Fusobacterium in both the OED and oral squamous cell carcinoma (OSCC) patients and six different studies respectively reported a reduction of genus Streptococcus in both the OED and OSCC groups when compared to normal controls. Other predominant bacteria that were specific to different patient groups varied in each study.

CONCLUSIONS

The results of the included studies showed that the composition of the oral microbiome in patients with OED compared to healthy controls and OSCC patients was inconsistent. However, all ten studies showed non-negligible heterogeneity in the type and size of the sample, and the comparability between groups, which strongly limited the external validity of results. Further studies are strongly recommended.

Phocaeicola oris sp. nov., an anaerobic bacterium isolated from the saliva of a patient with oral squamous cell carcinoma

A Gram-stain-negative or -positive, strictly anaerobic, non-spore-forming and pleomorphic bacterium (designated 14-104^T) was isolated from the saliva sample of a patient with oral squamous cell carcinoma. It was an acid-tolerant neutralophilic mesophile, growing at between 20 and 40 °C (with optimum growth at 30 °C) and pH between pH 3.0 and 7.0 (with optimum growth at pH 6.0-7.0). It contained anteiso-C_15 : 0 and C_15 : 0 as the major fatty acids. The genome size of strain 14-104^T was 2.98 Mbp, and the G+C content was 39.6 mol%. It shared <87 % 16S rRNA sequence similarity, <71 % orthologous average nucleotide identity, <76 % average amino acid identity and <68 %% of conserved proteins with its closest relative, Phocaeicola abscessus CCUG 55929^T. Reconstruction of phylogenetic and phylogenomic trees revealed that strain 14-104^T and P. abscessus CCUG 55929^T were clustered as a distinct clade without any other terminal node. The phylogenetic and phylogenomic analyses along with physiological and chemotaxonomic data indicated that strain 14-104^T represents a novel species in the genus Phocaeicola, for which the name Phocaeicola oris sp. nov. is proposed. The type strain is 14-104^T (=BCRC 81305^T= NBRC 115041^T).

The Composition and Functional Capacities of Saliva Microbiota Differ Between Children With Low and High Sweet Treat Consumption

Excess sugar consumption—common in youth—is associated with poor health. Evidence on the relationship between sugar consumption and the oral microbiome, however, remains scarce and inconclusive. We explored whether the diversity, composition, and functional capacities of saliva microbiota differ based on the consumption of select sugary foods and drinks (“sweet treats”). Using 16S rRNA gene sequencing, we characterized saliva microbiota from 11 to 13-year-old children who participated in the Finnish Health in Teens (Fin-HIT) cohort study. The sample comprised children in the lowest (n = 227) and highest (n = 226) tertiles of sweet treat consumption. We compared differences in the alpha diversity (Shannon, inverse Simpson, and Chao1 indices), beta diversity (principal coordinates analysis based on Bray–Curtis dissimilarity), and abundance (differentially abundant operational taxonomic units (OTUs) at the genus level) between these low and high consumption groups. We performed PICRUSt2 to predict the metabolic pathways of microbial communities. No differences emerged in the alpha diversity between low and high sweet treat consumption, whereas the beta diversity differed between groups (p = 0.001). The abundance of several genera such as Streptococcus, Prevotella, Veillonella, and Selenomonas was higher in the high consumption group compared with the low consumption group following false discovery rate correction (p < 0.05). Children with high sweet treat consumption exhibited higher proportions of nitrate reduction IV and gondoate biosynthesis pathways compared with the low consumption group (p < 0.05). To conclude, sweet treat consumption shapes saliva microbiota. Children who consume a high level of sweet treats exhibited different compositions and metabolic pathways compared with children who consume low levels of sweet treats. Our findings reveal novel insights into the relationship between sugary diets and oral microbiota.