Zhang Mei, Ma Qingkun, Zhao Han, Sun Ge, Zhao Tenghua, Wang Yuqing, Chen Shiji, Jia Lele, Song Yixiang, Mu Yanling
School of Pharmaceutical Sciences & Institute of Materia Medica, Shandong First Medical University & Shandong Academy of Medical Sciences, National Key Laboratory of Advanced Drug Delivery System, Key Laboratory for Biotechnology Drugs of National Health Commission (Shandong Academy of Medical Sciences), Key Lab for Rare & Uncommon Diseases of Shandong Province, Jinan, 250117, Shandong, China.
Department of Pediatric Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, 250117, Shandong Province, China.
Sci Rep. 2025 Jul 1;15(1):21783. doi: 10.1038/s41598-025-04645-6.
Highly sensitive and selective monitoring of amino metabolites such as glutamine, arginine, tryptophan and related proteins played significant roles in early diagnosis and warning of lymphoma. But those limited abundance and lacked chromophore group in vivo were bottleneck of multivariate analysis. This work aims to develop a novel UHPLC-Triple-TOF-HRMS method for simultaneous quantitation of 20 kinds of amino metabolites and tracing different proteins based on a new mass spectrometry probe (3-bromopropyl) triphenylphosphonium (3-BMP) with ability of enhance ionization efficiency and targeted labeling amino functional groups. An excellent linearity with R ≥ 0.9995 and inter- and intra-day RSD were 1.43-5.22% and 1.22-5.87%, respectively. Satisfactory recoveries were 87.09-95.82%. Limit of detection (S/N = 3) was 4.0-12.0 fmol. Further, up-regulated haptoglobin, coagulation factor VII and catalase could directly negatively regulate Ala, Lys and Phe, which caused Trp, His, Ser, Asp and Pro expression decreased significantly in lymphoma patients (p < 0.05). Ultimately, a machine learning model was established to predict lymphoma with accuracy rate of 93.68%. Above all, this study would provide multivariate analysis strategy for in-depth explore relationship aminos associated proteins and pathogenesis and helpful for early warning of lymphoma patients under free-disease state.
对谷氨酰胺、精氨酸、色氨酸等氨基酸代谢物及相关蛋白质进行高灵敏度和高选择性监测,在淋巴瘤的早期诊断和预警中发挥着重要作用。但这些体内丰度有限且缺乏发色团的物质是多变量分析的瓶颈。本研究旨在开发一种新型超高效液相色谱-三重四极杆-高分辨质谱方法,基于一种具有增强电离效率和靶向标记氨基官能团能力的新型质谱探针(3-溴丙基)三苯基鏻(3-BMP),同时定量20种氨基酸代谢物并追踪不同蛋白质。具有出色的线性关系,R≥0.9995,日间和日内相对标准偏差分别为1.43 - 5.22%和1.22 - 5.87%。回收率令人满意,为87.09 - 95.82%。检测限(S/N = 3)为4.0 - 12.0 fmol。此外,上调的触珠蛋白、凝血因子VII和过氧化氢酶可直接负调控丙氨酸、赖氨酸和苯丙氨酸,导致淋巴瘤患者色氨酸、组氨酸、丝氨酸、天冬氨酸和脯氨酸的表达显著下降(p < 0.05)。最终,建立了一个机器学习模型来预测淋巴瘤,准确率为93.68%。综上所述,本研究将为深入探究氨基酸相关蛋白质与发病机制之间的关系提供多变量分析策略,有助于对处于无疾病状态的淋巴瘤患者进行早期预警。