Zhang Jin, Gao Zhaoya, Xiao Weidi, Jin Ningxin, Zeng Jiaming, Wang Fengzhang, Jin Xiaowei, Dong Liguang, Lin Jian, Gu Jin, Wang Chu
Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University Beijing China
Department of Gastrointestinal Surgery, Peking University Shougang Hospital Beijing China
Chem Sci. 2024 Oct 15;15(44):18419-30. doi: 10.1039/d4sc05518g.
Colorectal cancer (CRC) is a major cause of cancer-related death worldwide and an effective screening strategy for diagnosis of early-stage CRC is highly desired. Although extracellular vesicles (EVs) are expected to become some of the most promising tools for liquid biopsy of early disease diagnosis, the existing EV-based proteomics methods for practical application in clinical samples are limited by technical challenges in high-throughput isolation and detection of EVs. In the current study, we have developed a simplified and efficient EV-based proteomics strategy for early diagnosis of CRC. DSPE-functionalized beads were specifically designed that enabled direct capture of EVs from plasma samples in 10 minutes with good reproducibility and comprehensive proteome coverage. The single-pot, solid-phase-enhanced sample-preparation (SP3) technology was then combined with data-independent acquisition mass spectrometry (DIA-MS) for in-depth analysis and quantification of EV proteomes. From a cohort with 30 individuals including 11 healthy controls, 8 patients with adenomatous polyp and 11 patients with early-stage CRC, our streamlined workflow reproducibly quantified over 800 proteins from their plasma-derived EV samples, from which dysregulated protein signatures for molecular diagnosis of CRC were revealed. We selected a panel of 10 protein markers to train a machine learning (ML) model, which resulted in accurate prediction of polyp and early-stage CRC in an independent and single-blind validation cohort with excellent diagnostic ability of 89.3% accuracy. Our simplified and efficient clinical proteomic strategy will serve as a valuable tool for fast, accurate, and cost-effective diagnosis of CRC that can be easily extended to other disease samples for discovery of unique EV-based biomarkers.
结直肠癌（CRC）是全球癌症相关死亡的主要原因，因此非常需要一种有效的早期CRC诊断筛查策略。尽管细胞外囊泡（EVs）有望成为早期疾病诊断液体活检中最有前景的工具之一，但现有的基于EVs的蛋白质组学方法在临床样本中的实际应用受到EVs高通量分离和检测技术挑战的限制。在本研究中，我们开发了一种简化高效的基于EVs的蛋白质组学策略用于早期CRC诊断。我们专门设计了DSPE功能化磁珠，可在10分钟内从血浆样本中直接捕获EVs，具有良好的重现性和全面的蛋白质组覆盖范围。然后将单管、固相增强样品制备（SP3）技术与数据非依赖采集质谱（DIA-MS）相结合，用于深入分析和定量EV蛋白质组。在一个由30人组成的队列中，包括11名健康对照、8名腺瘤性息肉患者和11名早期CRC患者，我们简化的工作流程可重复定量来自血浆来源的EV样本中的800多种蛋白质，从中揭示了用于CRC分子诊断的失调蛋白质特征。我们选择了一组10种蛋白质标志物来训练机器学习（ML）模型，该模型在一个独立的单盲验证队列中准确预测了息肉和早期CRC，诊断能力出色，准确率达89.3%。我们简化高效的临床蛋白质组学策略将成为快速、准确且经济高效的CRC诊断的宝贵工具，并且可以轻松扩展到其他疾病样本，以发现独特的基于EVs的生物标志物。
