Xu Zhiyuan, Huang Yida, Hu Can, Du Lingbin, Du Yi-An, Zhang Yanqiang, Qin Jiangjiang, Liu Wanshan, Wang Ruimin, Yang Shouzhi, Wu Jiao, Cao Jing, Zhang Juxiang, Chen Gui-Ping, Lv Hang, Zhao Ping, He Weiyang, Wang Xiaoliang, Xu Min, Wang Pingfang, Hong Chuanshen, Yang Li-Tao, Xu Jingli, Chen Jiahui, Wei Qing, Zhang Ruolan, Yuan Li, Qian Kun, Cheng Xiangdong
Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China.
Gut. 2023 Nov;72(11):2051-2067. doi: 10.1136/gutjnl-2023-330045. Epub 2023 Jul 17.
Metabolic biomarkers are expected to decode the phenotype of gastric cancer (GC) and lead to high-performance blood tests towards GC diagnosis and prognosis. We attempted to develop diagnostic and prognostic models for GC based on plasma metabolic information.
We conducted a large-scale, multicentre study comprising 1944 participants from 7 centres in retrospective cohort and 264 participants in prospective cohort. Discovery and verification phases of diagnostic and prognostic models were conducted in retrospective cohort through machine learning and Cox regression of plasma metabolic fingerprints (PMFs) obtained by nanoparticle-enhanced laser desorption/ionisation-mass spectrometry (NPELDI-MS). Furthermore, the developed diagnostic model was validated in prospective cohort by both NPELDI-MS and ultra-performance liquid chromatography-MS (UPLC-MS).
We demonstrated the high throughput, desirable reproducibility and limited centre-specific effects of PMFs obtained through NPELDI-MS. In retrospective cohort, we achieved diagnostic performance with areas under curves (AUCs) of 0.862-0.988 in the discovery (n=1157 from 5 centres) and independent external verification dataset (n=787 from another 2 centres), through 5 different machine learning of PMFs, including neural network, ridge regression, lasso regression, support vector machine and random forest. Further, a metabolic panel consisting of 21 metabolites was constructed and identified for GC diagnosis with AUCs of 0.921-0.971 and 0.907-0.940 in the discovery and verification dataset, respectively. In the prospective study (n=264 from lead centre), both NPELDI-MS and UPLC-MS were applied to detect and validate the metabolic panel, and the diagnostic AUCs were 0.855-0.918 and 0.856-0.916, respectively. Moreover, we constructed a prognosis scoring system for GC in retrospective cohort, which can effectively predict the survival of GC patients.
We developed and validated diagnostic and prognostic models for GC, which also contribute to advanced metabolic analysis towards diseases, including but not limited to GC.
代谢生物标志物有望解读胃癌（GC）的表型，并带来针对GC诊断和预后的高性能血液检测。我们试图基于血浆代谢信息开发GC的诊断和预后模型。
我们开展了一项大规模、多中心研究，包括来自7个中心的1944名回顾性队列参与者和264名前瞻性队列参与者。通过对纳米颗粒增强激光解吸/电离质谱（NPELDI-MS）获得的血浆代谢指纹（PMF）进行机器学习和Cox回归，在回顾性队列中进行诊断和预后模型的发现与验证阶段。此外，所开发的诊断模型在前瞻性队列中通过NPELDI-MS和超高效液相色谱-质谱（UPLC-MS）进行验证。
我们证明了通过NPELDI-MS获得的PMF具有高通量、良好的重现性和有限的中心特异性效应。在回顾性队列中，通过对PMF进行5种不同的机器学习，包括神经网络、岭回归、套索回归、支持向量机和随机森林，我们在发现阶段（来自5个中心的n=1157）和独立外部验证数据集（来自另外2个中心的n=787）中实现了曲线下面积（AUC）为0.862-0.988的诊断性能。此外，构建并鉴定了一个由21种代谢物组成的代谢组用于GC诊断，在发现和验证数据集中的AUC分别为0.921-0.971和0.907-0.940。在前瞻性研究（来自牵头中心的n=264）中，应用NPELDI-MS和UPLC-MS检测并验证了该代谢组，诊断AUC分别为0.855-0.918和0.856-0.916。此外，我们在回顾性队列中构建了GC的预后评分系统，该系统可以有效预测GC患者的生存情况。
我们开发并验证了GC的诊断和预后模型，这也有助于对包括但不限于GC在内的疾病进行先进的代谢分析。
