Frontiers in microbiology · 2026
Unveiling the unique gut microbial signatures in colorectal adenomas: establishment and validation of a cross-kingdom microbiome predictive model.
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He Bingfeng, Xiao Zhiyong, Zou Linfeng, Wei Jinmei, Xiang Zhou, Sang FangFang, Guo Xuefeng
Аннотация
BACKGROUND: Colorectal adenoma (CA), the main precancerous lesion of colorectal cancer (CRC), originates in approximately 85-90% of CRC cases. With increasing demands for early diagnosis and treatment, gut microbiome research has become a forefront area. While numerous studies have shown that gut bacteria are closely related to the development of colorectal adenomas and cancer, research on viruses, archaea, and fungi is limited.
METHODS: From January 2019 to January 2024, this study collected 296 fecal samples from multiple centers and performed metagenomic analysis using shotgun sequencing. Principal coordinate analysis (PCoA) was conducted based on Bray-Curtis distance at the species level, α-diversity was calculated, and LEfSe analysis identified differential microorganisms. A random forest model was developed to distinguish adenoma patients from healthy individuals, with performance evaluated through internal validation using Bootstrap sampling and external validation with an independent cohort.
FINDINGS: Significant differences in the relative abundance of certain bacteria (e.g., Phocaeicola_vulgatus and Prevotella_copri), fungi (Candida_albicans), archaea (Methanobrevibacter_oralis), and viruses (Streptococcus satellite phage Javan301) were observed in adenoma patients. Spearman correlation analysis revealed complex network relationships among these microorganisms. The prediction model achieved a mean AUC of 0.80 ± 0.05 and an external validation AUC of 0.75, demonstrating stability and generalizability.
CONCLUSION: This study shows significant cross-kingdom microbial signatures in colorectal adenoma patients, providing potential for developing new preventive and therapeutic methods. The predictive model, based on these differential microorganisms, exhibits robust and promising classification performance, offering potential for early adenoma detection.
METHODS: From January 2019 to January 2024, this study collected 296 fecal samples from multiple centers and performed metagenomic analysis using shotgun sequencing. Principal coordinate analysis (PCoA) was conducted based on Bray-Curtis distance at the species level, α-diversity was calculated, and LEfSe analysis identified differential microorganisms. A random forest model was developed to distinguish adenoma patients from healthy individuals, with performance evaluated through internal validation using Bootstrap sampling and external validation with an independent cohort.
FINDINGS: Significant differences in the relative abundance of certain bacteria (e.g., Phocaeicola_vulgatus and Prevotella_copri), fungi (Candida_albicans), archaea (Methanobrevibacter_oralis), and viruses (Streptococcus satellite phage Javan301) were observed in adenoma patients. Spearman correlation analysis revealed complex network relationships among these microorganisms. The prediction model achieved a mean AUC of 0.80 ± 0.05 and an external validation AUC of 0.75, demonstrating stability and generalizability.
CONCLUSION: This study shows significant cross-kingdom microbial signatures in colorectal adenoma patients, providing potential for developing new preventive and therapeutic methods. The predictive model, based on these differential microorganisms, exhibits robust and promising classification performance, offering potential for early adenoma detection.