Multi-cohort metagenomics reveals strain functional heterogeneity and demonstrates fecal microbial load correction improves colorectal cancer diagnostic models

Qiucheng Li¹Fang Liu¹Jianfeng Zhong¹Xiaoling Fang¹Xinyi Zhang¹Huizhen Xiong¹Guangyi Li^2*Honglei Chen¹

• ¹The Eighth Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
• ²Hunan University, Changsha, China

Colorectal cancer (CRC) is strongly associated with the gut microbiome, yet most studies focus on genus/species-level taxonomy, overlooking strain-level functional heterogeneity. Here, we integrated 1,123 metagenomic samples from seven global CRC cohorts to perform multi-level metagenome-wide association studies (MWAS) and evaluate the impact of fecal microbial load (FML) correction on disease classification. Strain-level analysis revealed conspecific strains with opposing CRC associations, such as Bacteroides thetaiotaomicron strains exhibiting both protective and risk-increasing effects across cohorts:,::::: with:::::::: genomic:::::::::: functional :::::::::: annotation:::::::::: suggesting::::::::: potential :::::::::: underlying::::::::: biological::::::::::: mechanisms. FML correction mitigated confounds, significantly improving within-cohort and cross-cohort model performance for CRC prediction. Notably, genus/species-level models outperformed strain-resolved counterparts in predictive robustness, likely due to higher abundance and cross-population conservation. Our findings highlight the biological value of strain-level analysis for resolving microbiome functional diversity while demonstrating that higher taxonomic levels offer more clinically translatable diagnostic markers. Integrating FML correction and multi-level taxonomic profiling enhances mechanistic insight into microbiome-CRC interactions and improves diagnostic model generalizability across populations.