中国科学院理化技术研究所王树涛团队在研究中取得进展。他们的研究认为血浆细胞外囊泡的高效代谢组学分析有助于准确诊断早期胃癌。这一研究成果发表在2025年3月2日出版的国际学术期刊《美国化学会杂志》上。

在此，该课题组提出了一种结合血浆细胞外囊泡（EVs）代谢组学分析和机器学习的方案，以确定其他人群早期胃癌个体的代谢组学差异。高效的代谢组学分析是通过利用立体相互作用和亲和相互作用，对精心设计的纳米陷阱结构微粒（NanoFisher）进行高效、高纯度和无损伤的等离子体EV分离来实现的。从150份早期胃癌（50份）、良性胃疾病（50份）和非疾病对照（50份）血浆样本中获得了显著的代谢组学差异。

机器学习能够很好的区分早期胃癌和非疾病对照样本，曲线下面积（ AUC）为1.000，区分早期胃癌和胃良性疾病的AUC为0.875-0.975，直接对这三类进行分类的总体准确率为92%。NanoFisher材料支持的血浆EV代谢组学分析与机器学习相结合，具有广泛的临床应用前景，可以提高胃癌筛查结果。

据悉，早期胃癌的准确诊断对无症状人群具有重要价值，而目前内镜检查结合病理组织活检对早期胃癌和患者的疼痛往往遇到瓶颈。液体活检显示了早期胃癌无创诊断的希望；然而，由于缺乏高度敏感和特异性的生物标志物，实现准确诊断仍然是一个挑战。

附：英文原文

Title: Efficient Metabolomics Profiling from Plasma Extracellular Vesicles Enables Accurate Diagnosis of Early Gastric Cancer

Author: Fanqin Bu, Xinyi Shen, Haosu Zhan, Duanda Wang, Li Min, Yongyang Song, Shutao Wang

Issue&Volume: March 2, 2025

Abstract: 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. Significant metabolomics discrepancies are obtained from 150 early gastric cancer (50), benign gastric disease (50), and non-disease control (50) plasma samples. Machine learning enables ideal distinction between early gastric cancer and non-disease control samples with an area under the curve (AUC) of 1.000, achieves an AUC of 0.875–0.975 for differentiating early gastric cancer from benign gastric diseases, and demonstrates an overall accuracy of 92% in directly classifying these three categories. The plasma EV metabolomics profiling enabled by NanoFisher materials, integrated with machine learning, holds considerable promise for broad clinical acceptance, enhancing gastric cancer screening outcomes.

DOI: 10.1021/jacs.4c18110

Source: https://pubs.acs.org/doi/full/10.1021/jacs.4c18110