Deciphering Gut Microbiome in Colorectal Cancer via Robust Learning Methods

Area of Science:

• Microbiome research
• Cancer biology
• Computational biology

Background:

• Colorectal cancer (CRC) is a global health concern with strong ties to gut microbiota composition.
• Identifying reliable microbial signatures is crucial for CRC early detection and treatment strategies.

Purpose of the Study:

• To identify reproducible and generalizable microbial signatures associated with colorectal cancer.
• To characterize differences in gut microbial composition between CRC patients and healthy individuals.
• To validate microbial markers using advanced computational methods.

Main Methods:

• Integrated multiple public case-control datasets for CRC microbiome analysis.
• Evaluated alpha and beta diversity metrics to assess microbial richness and composition.
• Employed ANCOM-BC, LEfSe, and adapted sccomp for differential abundance and robust marker validation.

Main Results:

• CRC patients exhibit significantly higher gut microbial richness and altered microbiome composition compared to controls.
• Specific taxa like *Fusobacterium* and *Peptostreptococcus* were enriched, while *Anaerostipes* was depleted in CRC patients.
• Identified reproducible microbial signatures, with sccomp enhancing marker identification precision.

Conclusions:

• The identified microbial signatures deepen the understanding of CRC pathogenesis and offer potential for novel therapeutic targets.
• Adapting single-cell analysis techniques like sccomp improves microbial marker discovery precision.
• Findings support the development of microbiota-based diagnostics and therapeutics for colorectal cancer.