Efficient plasma metabolic fingerprinting as a novel tool for diagnosis and prognosis of gastric cancer: a large-scale, multicentre study-猫眼课题宝

DOI：

10.1136/gutjnl-2023-330045

发表时间：

2023-07

期刊：

Gut

影响因子：

24.5

通讯作者：

中科院分区：

医学1区

文献类型：

作者：

Zhiyuan Xu;Yida Huang;Can Hu;L. Du;Yi-an Du;Yanqiang Zhang;Jiangjiang Qin;Wanshan Liu;Ruimin Wang;Shouzhi Yang;Jiao Wu;Jing Cao;Juxiang Zhang;Gui-Ping Chen;Hang Lv;Ping Zhao;We He;Xiaoliang Wang;Min Xu;Ping‐fang Wang;C. Hong;Li-tao Yang;Jingli Xu;Jiahui Chen;Q. Wei;Ruolan Zhang;Li Yuan;Kun Qian;Xiang-Liu Cheng

目的代谢生物标志物有望解码胃癌（GC）的表型，并为GC的诊断和预后提供高性能的血液检测。我们试图建立基于血浆代谢信息的GC诊断和预后模型。设计我们进行了一项大规模的多中心研究，回顾性队列包括来自7个中心的1944名参与者，前瞻性队列包括264名参与者。诊断和预后模型的发现和验证阶段在回顾性队列中通过机器学习和考克斯回归对通过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。此外，通过NPELDI-MS和超高效液相色谱-MS（UPLC-MS）在前瞻性队列中验证了所开发的诊断模型。结果NPELDI-MS获得的PMF具有高通量、良好的重复性和有限的中心特异性效应。在回顾性队列研究中，我们发现PMF的曲线下面积（AUC）为0.862-0.988，（n=1157，来自5个中心）和独立外部验证数据集（来自另外2个中心的n=787），通过5种不同的PMF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。此外，构建了由21种代谢物组成的代谢组，并在发现和验证数据集中分别鉴定了AUC为0.921-0.971和0.907-0.940的GC诊断。在前瞻性研究中（n=264，来自牵头中心），NPELDI-MS和UPLC-MS均用于检测和验证代谢组，诊断AUC分别为0.855-0.918和0.856-0.916。此外，我们建立了一个回顾性队列胃癌预后评分系统，它可以有效地预测胃癌患者的生存。结论建立了胃癌的诊断和预后模型，为胃癌等疾病的代谢分析奠定了基础。

Objective 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. Design 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). Results 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. Conclusion 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.

Efficient plasma metabolic fingerprinting as a novel tool for diagnosis and prognosis of gastric cancer: a large-scale, multicentre study

文献摘要

相似文献