Gut microbiome-based machine learning model for early colorectal cancer and adenoma screening.

ABSTRACT: Colorectal cancer (CRC) is a major source of cancer-related deaths, but early detection at the adenoma stage markedly improves outcomes. Existing tools such as colonoscopy and fecal immunochemical testing (FIT) are invasive or insensitive to early lesions. To develop a non-invasive screening strategy, we analyzed five publicly available 16 S rRNA sequencing datasets from North American and East Asia. Using Analysis of Compositions of Microbiome with Bias Correction (ANCOM-BC) and chi-square testing, we identified 109 discriminatory microbial taxa and trained random forest (RF) classification models to distinguish healthy controls, adenomas, and CRC. The models performed well in internal validation (AUC = 0.90, 95% CI: 0.869-0.931) and external validation (AUC = 0.82), indicating cross-popu

SUBMITTER: Tsai YJ 

PROVIDER: S-EPMC12509340 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature