Predicting nasal diseases based on microbiota relationship network

文献类型： 外文期刊

第一作者： Liang, Yibo

作者： Liang, Yibo;Zhang, Chenting;Zhang, Guimin;Mao, Jie;Sun, Zhe;Qiu, Tianlei;Li, Binghua;Zhang, Kai

作者机构：

关键词： Nasal microbiome; relationship network; graph theory; machine learning; disease prediction

期刊名称：SCIENCE PROGRESS （ 影响因子：2.9； 五年影响因子：2.7 ）

ISSN： 0036-8504

年卷期： 2025 年 108 卷 1 期

页码：

收录情况： SCI

摘要： Objectives Increasing evidence indicates that the local microbiome can be used to predict host disease states. However, constructing models that obtain better results with fewer features is still challenging.Methods In this study, we established a nasal microbiome database consisting of 132 chronic rhinosinusitis patients, 27 nasal inverted papilloma patients, and 45 control patients. 16S rRNA gene sequencing was used to identify the species and abundance of bacteria in each sample, and a nasal microbiome database was generated after low-abundance bacteria were eliminated. The correlation data network of different groups of bacteria was constructed by calculating the correlation coefficient among bacterial genera, and the correlation parameters of the network were calculated based on graph theory. Through the development and application of a machine learning framework to optimize the screening process, combined with microbiome relationship network parameters based on graph theory, basic bacteria with high contributions to classification prediction were selected for the prediction of nasal diseases.Results We found that patients with nasal disease have a specific nasal microbiome signature and identified Moraxella, Prevotella, and Rothia as keystone genera that are markers of nasal disease; these markers can be interpreted as key control routes through graph theory analysis of the microbiota. With this strategy, we were able to characterize microbial community changes in nasal disease patients, which could reveal the potential role of the nasal microbiome in nasal disease.Conclusion This study can provide a reference for the formulation of disease prevention and control policies. Our framework can be applied to other diseases to identify keystone genera that influence disease states and can be used to predict disease states.

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