Machine Learning-Aided Identification of Fecal Extracellular Vesicle microRNA Signatures for Noninvasive Detection of Colorectal Cancer

Colorectal cancer (CRC) remains a formidable threat to human health, with considerable challenges persisting in its diagnosis, particularly during the early stages of the malignancy. In this study, we elucidated that fecal extracellular vesicle microRNA signatures (FEVOR) could serve as potent noninvasive CRC biomarkers. FEVOR was first revealed by miRNA sequencing, followed by the construction of a CRISPR/Cas13a-based detection platform to interrogate FEVOR expression across a diverse spectrum of clinical cohorts. Machine learning-driven models were subsequently developed within the realms of CRC diagnostics, prognostics, and early warning systems. In a cohort of 38 CRC patients, our diagnostic model achieved…

• NN
  National Natural Science Foundation of China

  Awards: 82373115, 82202603, 22174072, 22425401
• CP
  China Petrochemical Corporation

  Award: 223086