Qin Hao, Liu Jie, Li Yang-Yang, Xu Ya-Lan, Yan Yi-Fang
State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Medical Records, Air Force Medical Center, PLA, Air Force Medical University, Beijing, China.
Heliyon. 2024 Aug 31;10(17):e37284. doi: 10.1016/j.heliyon.2024.e37284. eCollection 2024 Sep 15.
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 and exhibited significant variations in abundance between genders. was prevalent in healthy females but exhibited a reduced abundance in female glioma patients. In contrast, 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 and , 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.
人类口腔微生物群与全身健康之间复杂的相互作用越来越受到认可，尤其是在胶质母细胞瘤等中枢神经系统疾病的背景下。在本研究中，我们旨在利用公开可用的唾液微生物群数据集的16S rRNA测序数据，阐明胶质瘤患者唾液微生物群中的性别特异性差异。我们进行了全面的生物信息学分析，包括质量控制、降噪、物种分类以及不同分类水平下的微生物群落组成分析。采用机器学习算法来识别与胶质瘤相关的微生物特征。与健康对照相比，我们的分析揭示了胶质瘤患者唾液微生物群的明显差异。值得注意的是，某些属在不同性别之间的丰度存在显著差异。某属在健康女性中普遍存在，但在女性胶质瘤患者中丰度降低。相比之下，另一属在男性胶质瘤患者中更为丰富。这些发现表明，激素影响可能在塑造唾液微生物群及其与胶质瘤的关联中发挥作用。我们使用套索逻辑回归和随机森林模型相结合的方法进行特征选择，并确定了区分胶质瘤患者与健康对照的关键微生物特征。我们开发了一个具有高预测准确性的诊断模型，曲线下面积和主成分分析指标证实了其稳健性。对包括某些菌属在内的微生物标志物的分析突出了唾液微生物群作为胶质瘤诊断和预后的非侵入性生物标志物的潜力。我们的研究结果突出了胶质瘤患者唾液微生物群中显著的性别特异性差异，为胶质瘤的发病机制提供了新的见解，并为创新的诊断和治疗策略铺平了道路。鉴于唾液易于收集且具有非侵入性，将其用作诊断液体对于监测全身健康和疾病轨迹具有巨大潜力。未来的研究应专注于调查唾液微生物群影响胶质瘤发展的潜在机制，并确定潜在的针对微生物群的疗法，以加强胶质瘤的管理。
