Identifying Robust Microbiota Signatures and Interpretable Rules to Distinguish Cancer Subtypes.

Area of Science:

• Microbiology
• Oncology
• Bioinformatics

Background:

• Cancer etiology involves genetic and environmental factors, but the role of microorganisms in tumorigenesis remains incompletely understood.
• Previous research on microbe-cancer links is limited to single microbe-cancer subtype associations, lacking comprehensive microbiome analysis.
• The specific contribution of the microbiome to tumorigenesis requires further investigation.

Purpose of the Study:

• To systematically analyze microbiome data from cancer patients and controls to identify key microbial players in tumorigenesis.
• To develop interpretable quantitative predictive models for cancer classification based on microbiome signatures.
• To compare different bioinformatics tools for robust microbiome analysis in cancer research.

Main Methods:

• Systematic microbiome analysis of blood and cancer-associated tissues from public databases.
• Application of multiple machine learning methods for developing quantitative predictive models.
• Comparative analysis of Kraken and SHOGUN tools for microbiome profiling and feature identification.

Main Results:

• Identification of several core regulatory microorganisms contributing to the classification of multiple tumor subtypes.
• Establishment of quantitative, interpretable predictive models for cancer discrimination using microbiome data.
• Comparison of optimal features and classification rules derived from different microbiome analysis pipelines.

Conclusions:

• The study identified novel microbiome signatures and interpretable classification rules for cancer discrimination.
• This research provides a methodological comparison for robust cancer microbiome analyses.
• Findings promote a deeper understanding of tumor etiology at the microbiome level.