School of Chemistry and Molecular Engineering and Shanghai Key Laboratory of Functional Materials Chemistry, and Research Centre of Analysis and Test, East China University of Science and Technology, Shanghai, 200237, China.
School of Chemistry and Chemical Engineering, Qinghai Normal University, Xining, Qinghai, 810016, China.
Talanta. 2024 Feb 1;268(Pt 2):125371. doi: 10.1016/j.talanta.2023.125371. Epub 2023 Oct 31.
The liver is a major organ in metabolism, and alterations in serum lipids are associated with liver disorders. Here, a rapid, easy, and reliable screening technique based on lipidomic profiling was developed using machine learning and surface-assisted laser desorption ionization mass spectrometry (SALDI MS) for liver cancer diagnosis. A graphitized carbon matrix (GCM) was created for serum lipid profiling in SALDI MS and demonstrated a better performance for neutral lipids analysis than conventional organic matrices. The fingerprint of serum lipids, including triacylglycerols (TGs), diacylglycerols (DGs), cholesteryl esters (CEs), glycerophospholipids (GPs), and other components, could be directly obtained by GCM-assisted LDI MS without extraction. Five machine learning methods were applied to distinguish liver cancer (LC) patients from healthy controls (HC) and chronic hepatitis B (CHB) patients. The best diagnostic performance was attained by linear discriminant analysis (LDA), which has a confusion matrix accuracy of 98.3 %. The receiver operating characteristic (ROC) curve for liver cancer exhibited an area under the curve (AUC) of 0.99, indicating a high degree of prediction accuracy. One-way ANOVA analysis revealed that numerous TGs were down-regulated in LC group. The results demonstrated the viability of GCM-assisted LDI MS as a valuable diagnostic tool for liver cancer.
肝脏是代谢的主要器官，血清脂质的改变与肝脏疾病有关。在这里，我们开发了一种基于脂质组学分析的快速、简便、可靠的筛选技术，该技术结合了机器学习和表面辅助激光解吸电离质谱（SALDI MS），用于肝癌诊断。我们创建了一种石墨化碳基质（GCM）用于 SALDI MS 中的血清脂质分析，与传统的有机基质相比，它在中性脂质分析方面表现出更好的性能。GCM 辅助 LDI MS 可直接获得包括三酰甘油（TGs）、二酰甘油（DGs）、胆固醇酯（CEs）、甘油磷脂（GPs）和其他成分在内的血清脂质指纹图谱，无需提取。我们应用了五种机器学习方法来区分肝癌（LC）患者、健康对照（HC）和慢性乙型肝炎（CHB）患者。线性判别分析（LDA）具有最高的诊断性能，其混淆矩阵准确率为 98.3%。肝癌的受试者工作特征（ROC）曲线的曲线下面积（AUC）为 0.99，表明具有很高的预测准确性。单因素方差分析显示，LC 组中有许多 TGs 下调。这些结果表明，GCM 辅助 LDI MS 作为一种有价值的肝癌诊断工具具有可行性。
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