基于机器学习分析识别并验证血清外泌体蛋白质组学特征以诊断结直肠癌

核心信息

• 论文英文标题: Machine learning-based analysis identifies and validates serum exosomal proteomic signatures for the diagnosis of colorectal cancer
• 期刊: Cell Rep Med
• 发表年份: 2024
• DOI: 10.1016/j.xcrm.2024.101689
• PMID/PMCID: 39168094; PMC11384723

研究概览

Collectively, our study identified the crucial proteomic signatures in serum EVs and established a promising EV-related RF model for CRC diagnosis in the clinic. Additionally, multi-omics approaches were employed to predict the functions and potential sources of serum EV-derived proteins.

核心发现

The potential of serum extracellular vesicles (EVs) as non-invasive biomarkers for diagnosing colorectal cancer (CRC) remains elusive.

We employed an in-depth 4D-DIA proteomics and machine learning (ML) pipeline to identify key proteins, PF4 and AACT, for CRC diagnosis in serum EV samples from a discovery cohort of 37 cases.

PF4 and AACT outperform traditional biomarkers, CEA and CA19-9, detected by ELISA in 912 individuals.

963 in the train and test sets, respectively.

Echo Biotech 角色

试剂盒ES9P11e

关联平台: Exoomics®, Research Reagents & Tools

关联服务与能力: Biofluid EV Isolation & Purification, Research Reagent / Product Supply

关联产品或试剂: Exosupur® EV Isolation/Purification Kit

参考文献

原始论文: Machine learning-based analysis identifies and validates serum exosomal proteomic signatures for the diagnosis of colorectal cancer Cell reports. Medicine. 2024. DOI: 10.1016/j.xcrm.2024.101689. PMID/PMCID: 39168094; PMC11384723.