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Updated: May 27, 2025

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Predicting nasal diseases based on microbiota relationship network.

Yibo Liang1, Jie Mao2, Tianlei Qiu3

  • 1Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Institute of Otolaryngology of Tianjin, Key Laboratory of Auditory Speech and Balance Medicine, Key Medical Discipline of Tianjin (Otolaryngology), Quality Control Centre of Otolaryngology, Tianjin, China.

Science Progress
|February 18, 2025
PubMed
Summary
This summary is machine-generated.

Researchers identified key nasal bacteria (Moraxella, Prevotella, Rothia) that act as markers for nasal diseases. This discovery aids in predicting disease states and developing targeted prevention strategies.

Keywords:
Nasal microbiomedisease predictiongraph theorymachine learningrelationship network

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Area of Science:

  • Microbiome research
  • Medical diagnostics
  • Bioinformatics

Background:

  • The local microbiome's potential for predicting host disease states is increasingly recognized.
  • Developing predictive models with minimal features remains a significant challenge in microbiome research.

Purpose of the Study:

  • To establish a nasal microbiome database and identify key bacterial genera for predicting nasal diseases.
  • To develop a machine learning framework integrated with graph theory for efficient feature selection.

Main Methods:

  • A nasal microbiome database was created from 132 chronic rhinosinusitis patients, 27 nasal inverted papilloma patients, and 45 controls.
  • 16S rRNA gene sequencing was employed to determine bacterial species and abundance.
  • A machine learning framework combined with graph theory analysis of bacterial correlation networks was used to select predictive features.

Main Results:

  • A distinct nasal microbiome signature was observed in patients with nasal diseases.
  • Moraxella, Prevotella, and Rothia were identified as keystone genera indicative of nasal disease.
  • Graph theory analysis revealed these genera as critical control routes within the nasal microbiota.

Conclusions:

  • The developed framework effectively identifies key bacterial genera for predicting nasal disease states.
  • This approach can inform disease prevention and control policies.
  • The methodology is applicable to other diseases for identifying influential keystone genera and predicting disease states.