Zhang Heyuhan, Tao Ping, Tong Hanxing, Zhang Yong, Sun Nianrong, Deng Chunhui
Department of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
Department of Laboratory Medicine, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200082, China.
Small Methods. 2025 Apr;9(4):e2401421. doi: 10.1002/smtd.202401421. Epub 2025 Jan 6.
The rarity and heterogeneity of liposarcomas (LPS) pose significant challenges in their diagnosis and management. In this work, a series of metal-organic frameworks (MOFs) engineering is designed and implemented. Through comprehensive characterization and performance evaluations, such as stability, thermal-driven desorption efficiency, as well as energy- and charge-transfer capacity, the engineering of group IV bimetallic MOFs emerges as particularly noteworthy. This is especially true for their derivative products, which exhibit superior performance across a range of laser desorption/ionization mass spectrometry (LDI MS) performance tests, including those involving practical sample assessments. The top-performing product is utilized to enable high-throughput recording of LPS metabolic fingerprints (PMFs) within seconds using LDI MS. With machine learning on PMFs, both the LPSrecognizer and LPSclassifier are developed, achieving accurate recognition and classification of LPS with area under the curves (AUCs) of 0.900-1.000. Simplified versions are also developed of the LPSrecognizer and LPSclassifier by screening metabolic biomarker panels, achieving considerable predictive performance, and conducting basic pathway exploration. The work highlights the MOFs engineering for the matrix design and their potential application in developing metabolic analysis and screening tools for rare diseases in clinical settings.
脂肪肉瘤（LPS）的罕见性和异质性给其诊断和管理带来了重大挑战。在这项工作中，设计并实施了一系列金属有机框架（MOF）工程。通过全面的表征和性能评估，如稳定性、热驱动解吸效率以及能量和电荷转移能力，IV族双金属MOF的工程脱颖而出，值得特别关注。对于其衍生产品而言尤其如此，这些衍生产品在一系列激光解吸/电离质谱（LDI MS）性能测试中表现出卓越性能，包括涉及实际样品评估的测试。性能最佳的产品被用于通过LDI MS在数秒内实现LPS代谢指纹图谱（PMF）的高通量记录。借助对PMF的机器学习，开发了LPS识别器和LPS分类器，实现了对LPS的准确识别和分类，曲线下面积（AUC）为0.900 - 1.000。还通过筛选代谢生物标志物面板开发了LPS识别器和LPS分类器的简化版本，实现了可观的预测性能，并进行了基本途径探索。这项工作突出了用于基质设计的MOF工程及其在临床环境中开发罕见病代谢分析和筛查工具方面的潜在应用。
