Deciphering Gut Microbiome in Colorectal Cancer via Robust Learning Methods

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide and is closely linked to the gut microbiota. Identifying reproducible and generalizable microbial signatures holds significant potential for enhancing early detection and advancing treatment for this deadly disease.

This study integrated various publicly available case-control datasets to identify microbial signatures for CRC. Alpha and beta diversity metrics were evaluated to characterize differences in gut microbial richness, evenness, and overall composition between CRC patients and healthy controls. Differential abundance analysis was conducted using ANCOM-BC and LEfSe to pinpoint individual taxa that were enriched or depleted in CRC patients. Additionally, sccomp, a Bayesian machine learning method from single-cell analysis, was adapted to provide a more robust validation of compositional differences in individual microbial markers.

Gut microbial richness is significantly higher in CRC patients, and overall microbiome composition differs significantly between CRC patients and healthy controls. Several taxa, such as Fusobacterium and Peptostreptococcus, are enriched in CRC patients, while others, including Anaerostipes, are depleted. The microbial signatures identified from the integrated data are reproducible and generalizable, with many aligning with findings from previous studies. Furthermore, the use of sccomp enhanced the precision of individual microbial marker identification.

Biologically, the microbial signatures identified from the integrated data improve our understanding of the gut microbiota's role in CRC pathogenesis and may contribute to the development of translational targets and microbiota-based therapies. Methodologically, this study demonstrates the effectiveness of adapting robust techniques from single-cell research to improve the precision of microbial marker discovery.