血浆EVs的高效代谢组学图谱可准确诊断早期胃癌

核心信息

  • 论文英文标题: Efficient Metabolomics Profiling from Plasma Extracellular Vesicles Enables Accurate Diagnosis of Early Gastric Cancer
  • 期刊: J Am Chem Soc
  • 发表年份: 2025
  • DOI: 10.1021/jacs.4c18110
  • PMID/PMCID: 40071449

研究概览

Accurate diagnosis of early gastric cancer is valuable for asymptomatic populations, while current endoscopic examination combined with pathological tissue biopsy often encounters bottlenecks for early-stage cancer and causes pain to patients. Liquid biopsy shows promise for noninvasive diagnosis of early gastric cancer; however, it remains a challenge to achieve accurate diagnosis due to the lack of highly sensitive and specific biomarkers.

核心发现

Accurate diagnosis of early gastric cancer is valuable for asymptomatic populations, while current endoscopic examination combined with pathological tissue biopsy often encounters bottlenecks for early-stage cancer and causes pain to patients.

Liquid biopsy shows promise for noninvasive diagnosis of early gastric cancer; however, it remains a challenge to achieve accurate diagnosis due to the lack of highly sensitive and specific biomarkers.

Herein, we propose a protocol combining metabolomics profiling from plasma extracellular vesicles (EVs) and machine learning to identify the metabolomics discrepancies of early gastric cancer individuals from other populations.

Efficient metabolomics profiling is achieved by efficient, high-purity, and damage-free plasma EVs separation using elaborately designed nanotrap-structured microparticles (NanoFisher) by taking advantage of stereoscopic interaction and affinity interaction.

Echo Biotech 角色

Exosupur ES911分离血浆外泌体与客户开发方法对比&Nanoluc外泌体

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: Efficient Metabolomics Profiling from Plasma Extracellular Vesicles Enables Accurate Diagnosis of Early Gastric Cancer Journal of the American Chemical Society. 2025. DOI: 10.1021/jacs.4c18110. PMID/PMCID: 40071449.