Li Pengfei, Xu Shuxin, Han Yanjie, He Hui, Liu Zhen
State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University 163 Xianlin Avenue Nanjing 210023 China
Chem Sci. 2023 Feb 13;14(10):2553-2561. doi: 10.1039/d2sc05541d. eCollection 2023 Mar 8.
-diol metabolic reprogramming evolves during primary liver cancer (PLC) initiation and progression. However, owing to the low concentrations and highly structural heterogeneity of -diols , severe interference from complex biofluids and limited profiling coverage of existing methods, in-depth profiling of -diol metabolites and linking their specific changes with PLC remain challenging. Besides, due to the low specificity of widely used protein biomarkers, accurate classification of PLC from hepatitis still represents an unmet need in clinical diagnostics. Herein, to high-coverage profile -diols and explore the translational potential of them as biomarkers, a machine learning-empowered boronate affinity extraction-solvent evaporation assisted enrichment-mass spectrometry (MLE-BESE-MS) was developed. A single analytical platform integrated with multiple complementary functions, including pH-controlled boronate affinity extraction, solvent evaporation-assisted enrichment and nanoelectrospray ionization-based -diol identification, was constructed, which significantly improved the metabolite coverage. Meanwhile, by virtue of machine learning (principal components analysis, orthogonal partial least-squares discrimination analysis and random forest), collected -diols were statistically screened to extract efficient features for precise PLC diagnosis, and the results outperform the routinely used protein biomarker-based methods both in sensitivity (87.5% less than 70%) and specificity (85.7% 80%). This machine learning-empowered integrated MS platform advanced the targeted metabolic analysis for early cancer diagnosis, rendering great promise for clinical translation.
二醇代谢重编程在原发性肝癌（PLC）的起始和进展过程中演变。然而，由于二醇的低浓度和高度结构异质性、复杂生物流体的严重干扰以及现有方法的有限分析覆盖范围，二醇代谢物的深入分析以及将其特定变化与PLC联系起来仍然具有挑战性。此外，由于广泛使用的蛋白质生物标志物的低特异性，从肝炎中准确分类PLC在临床诊断中仍然是一个未满足的需求。在此，为了实现二醇的高覆盖分析并探索它们作为生物标志物的转化潜力，开发了一种机器学习赋能的硼酸酯亲和萃取 - 溶剂蒸发辅助富集 - 质谱法（MLE - BESE - MS）。构建了一个集成多种互补功能的单一分析平台，包括pH控制的硼酸酯亲和萃取、溶剂蒸发辅助富集和基于纳米电喷雾电离的二醇鉴定，这显著提高了代谢物覆盖范围。同时，借助机器学习（主成分分析、正交偏最小二乘判别分析和随机森林），对收集的二醇进行统计筛选以提取用于精确PLC诊断的有效特征，结果在灵敏度（87.5% 大于70%）和特异性（85.7% 大于80%）方面均优于常规使用的基于蛋白质生物标志物的方法。这种机器学习赋能的集成质谱平台推进了用于早期癌症诊断的靶向代谢分析，为临床转化带来了巨大希望。
