Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China; Department of Laboratory Medicine, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China.
Department of Laboratory Medicine, Zhongshan City People's Hospital, Zhongshan, Guangdong, China.
Cell Rep Med. 2024 Aug 20;5(8):101689. doi: 10.1016/j.xcrm.2024.101689.
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. Furthermore, we developed an EV-related random forest (RF) model with the highest diagnostic efficiency, achieving AUC values of 0.960 and 0.963 in the train and test sets, respectively. Notably, this model demonstrated reliable diagnostic performance for early-stage CRC and distinguishing CRC from benign colorectal diseases. Additionally, multi-omics approaches were employed to predict the functions and potential sources of serum EV-derived proteins. 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.
血清细胞外囊泡 (EVs) 作为非侵入性生物标志物用于诊断结直肠癌 (CRC) 的潜力仍然难以捉摸。我们采用了深入的 4D-DIA 蛋白质组学和机器学习 (ML) 管道，在来自 37 例病例的发现队列的血清 EV 样本中鉴定出关键蛋白 PF4 和 AACT，用于 CRC 诊断。PF4 和 AACT 优于 ELISA 检测到的传统生物标志物 CEA 和 CA19-9，在 912 个人中。此外，我们开发了一个具有最高诊断效率的 EV 相关随机森林 (RF) 模型，在训练集和测试集中的 AUC 值分别为 0.960 和 0.963。值得注意的是，该模型对早期 CRC 和区分 CRC 与良性结直肠疾病具有可靠的诊断性能。此外，采用多组学方法预测血清 EV 衍生蛋白的功能和潜在来源。总之，我们的研究鉴定了血清 EV 中的关键蛋白质组学特征，并建立了一个有前途的 CRC 诊断 EV 相关 RF 模型。
