The intricate interplay between the human oral microbiome and systemic health is increasingly being recognized, particularly in the context of central nervous system pathologies such as glioblastoma. In this study, we aimed to elucidate gender-specific differences in the salivary microbiome of glioma patients by utilizing 16S rRNA sequencing data from publicly available salivary microbiome datasets. We conducted comprehensive bioinformatics analysis, encompassing quality control, noise reduction, species classification, and microbial community composition analysis at various taxonomic levels. Machine learning algorithms were employed to identify microbial signatures associated with glioma. When compared to healthy controls, our analysis revealed distinct differences in the salivary microbiota of glioma patients. Notably, the genera Leptotrichia and Atopobium exhibited significant variations in abundance between genders. Leptotrichia was prevalent in healthy females but exhibited a reduced abundance in female glioma patients. In contrast, Atopobium was more abundant in male glioma patients. These findings suggest that hormonal influences might play a role in shaping the salivary microbiome and its association with glioma. We utilized a combination of LASSO-logistic regression and random forest models for feature selection, and identified key microbial features that differentiated glioma patients from healthy controls. We developed a diagnostic model with high predictive accuracy and area under the curve and principal component analysis metrics confirmed its robustness. The analysis of microbial markers, including Atopobium and Leptotrichia, highlighted the potential of the salivary microbiota as a non-invasive biomarker for the diagnosis and prognosis of glioma. Our findings highlight significant gender-specific disparities in the salivary microbiome of patients with glioma, offering new insights into the pathogenesis of glioma and paving the way for innovative diagnostic and therapeutic strategies. The use of saliva as a diagnostic fluid, given its ease of collection and non-invasive nature, holds immense promise for monitoring systemic health and the trajectory of disease. Future research should focus on investigating the underlying mechanisms by which the salivary microbiome influences the development of glioma and identifying potential microbiome-targeted therapies to enhance the management of glioma.
Keywords: Disease markers; Glioma; Machine learning; Risk model; Salivary flora.
© 2024 The Authors.