Xu Runhao, Wang Jiongran, Zhu Qingqing, Zou Chen, Wei Zehao, Wang Hao, Ding Zian, Meng Minjie, Wei Huimin, Xia Shijin, Wei Dongqing, Deng Li, Zhang Shulin
Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Department of Clinical Laboratory, Renji Hospital, Shanghai, 200001, China.
Biomark Res. 2023 Jul 20;11(1):71. doi: 10.1186/s40364-023-00497-2.
For early screening and diagnosis of non-small cell lung cancer (NSCLC), a robust model based on plasma proteomics and metabolomics is required for accurate and accessible non-invasive detection. Here we aim to combine TMT-LC-MS/MS and machine-learning algorithms to establish models with high specificity and sensitivity, and summarize a generalized model building scheme.
TMT-LC-MS/MS was used to discover the differentially expressed proteins (DEPs) in the plasma of NSCLC patients. Plasma proteomics-guided metabolites were selected for clinical evaluation in 110 NSCLC patients who were going to receive therapies, 108 benign pulmonary diseases (BPD) patients, and 100 healthy controls (HC). The data were randomly split into training set and test set in a ratio of 80:20. Three supervised learning algorithms were applied to the training set for models fitting. The best performance models were evaluated with the test data set.
Differential plasma proteomics and metabolic pathways analyses revealed that the majority of DEPs in NSCLC were enriched in the pathways of complement and coagulation cascades, cholesterol and bile acids metabolism. Moreover, 10 DEPs, 14 amino acids, 15 bile acids, as well as 6 classic tumor biomarkers in blood were quantified using clinically validated assays. Finally, we obtained a high-performance screening model using logistic regression algorithm with AUC of 0.96, sensitivity of 92%, and specificity of 89%, and a diagnostic model with AUC of 0.871, sensitivity of 86%, and specificity of 78%. In the test set, the screening model achieved accuracy of 90%, sensitivity of 91%, and specificity of 90%, and the diagnostic model achieved accuracy of 82%, sensitivity of 77%, and specificity of 86%.
Integrated analysis of DEPs, amino acid, and bile acid features based on plasma proteomics-guided metabolite profiling, together with classical tumor biomarkers, provided a much more accurate detection model for screening and differential diagnosis of NSCLC. In addition, this new mathematical modeling based on plasma proteomics-guided metabolite profiling will be used for evaluation of therapeutic efficacy and long-term recurrence prediction of NSCLC.
为了实现非小细胞肺癌（NSCLC）的早期筛查和诊断，需要一个基于血浆蛋白质组学和代谢组学的强大模型，以进行准确且便捷的非侵入性检测。在此，我们旨在结合TMT-LC-MS/MS和机器学习算法，建立具有高特异性和敏感性的模型，并总结一种通用的模型构建方案。
使用TMT-LC-MS/MS发现NSCLC患者血浆中差异表达的蛋白质（DEP）。选择血浆蛋白质组学指导的代谢物，对110例即将接受治疗的NSCLC患者、108例良性肺部疾病（BPD）患者和100例健康对照（HC）进行临床评估。数据以80:20的比例随机分为训练集和测试集。应用三种监督学习算法对训练集进行模型拟合。使用测试数据集评估性能最佳的模型。
差异血浆蛋白质组学和代谢途径分析表明，NSCLC中大多数DEP富集于补体和凝血级联、胆固醇和胆汁酸代谢途径。此外，使用临床验证的检测方法对10种DEP、14种氨基酸、15种胆汁酸以及血液中的6种经典肿瘤生物标志物进行了定量。最后，我们使用逻辑回归算法获得了一个高性能筛查模型，其AUC为0.96，敏感性为92%，特异性为89%，以及一个诊断模型，其AUC为0.871，敏感性为86%，特异性为78%。在测试集中，筛查模型的准确率为90%，敏感性为91%，特异性为90%，诊断模型的准确率为82%，敏感性为77%，特异性为86%。
基于血浆蛋白质组学指导的代谢物谱分析对DEP、氨基酸和胆汁酸特征进行综合分析，结合经典肿瘤生物标志物，为NSCLC的筛查和鉴别诊断提供了一个更为准确的检测模型。此外，这种基于血浆蛋白质组学指导的代谢物谱分析的新数学模型将用于评估NSCLC的治疗效果和长期复发预测。
