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

ACS Nano. 2025-03; 
Zhaowei Zhang, Xuyang Liu, Chuanyue Peng, Rui Du, Xiaoqin Hong, Jia Xu, Jiaming Chen, Xiaomin Li, Yujing Tang, Yuwei Li, Yang Liu, Chen Xu, Dingbin Liu
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Synthetic sgRNA and crRNA Service The sequences of miRNA and corresponding crRNA were provided in Table S7 and synthesized by GenScript. LbuCas13a [20 nM] (GenCRISPR LbuCas13a Nuclease, GenScript, Nanjing, China) was preincubated with crRNA [10nM] in reaction buffer at 37 °C for 10 min. Get A Quote
GenCrispr Cas9 Genome Editing Get A Quote

摘要

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 ... More

关键词

colorectal cancer; diagnostic and prognostic biomarker; extracellular vesicle; machine learning; microRNA.