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Identification and predictive machine learning model construction of gut microbiota associated with carcinoembryonic antigens in colorectal cancer
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Identification and predictive machine learning model construction of gut microbiota associated with carcinoembryonic antigens in colorectal cancer
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Journal Article
Identification and predictive machine learning model construction of gut microbiota associated with carcinoembryonic antigens in colorectal cancer
2025
Overview
This study reveals R. callidus as a key gut microbiota species enriched in CRC patients with high CEA levels, demonstrating its novel pro-tumor associations through positive correlations with mast cell infiltration and CXCL1 chemokine and upregulation of long-chain fatty acid metabolism. Concurrently, we identify distinct immune micro-environments: elevated resting memory CD4+ T cells in high-CEA patients versus increased T follicular helper cells in low-CEA cohorts. Critically, by leveraging 30 differential microbial features, we develop ML models for noninvasive prediction of CEA levels. These findings establish gut microbiota as both a mechanistic mediator of CEA-driven CRC progression and a foundation for microbiome-based diagnostic tools.
Publisher
American Society for Microbiology
Subject
/ Aged
/ Bacteria - isolation & purification
/ Carcinoembryonic Antigen - genetics
/ Colorectal Neoplasms - immunology
/ Colorectal Neoplasms - microbiology
/ Female
/ Gastrointestinal Microbiome - genetics
/ Host-Microbiome Interactions
/ Humans
/ Male
/ Microbial Pathogenesis and Immunology
/ Microbial Physiology and Genetics
/ Microbiome and Disease Susceptibility
/ Microbiome-Based Diagnostics
