Chan Angela W, Mercier Pascal, Schiller Daniel, Bailey Robert, Robbins Sarah, Eurich Dean T, Sawyer Michael B, Broadhurst David
Department of Surgery, University of Alberta Hospital, 8440-112 Street, Edmonton, AB T6G 2B7, Canada.
Department of Biochemistry, NANUC, University of Alberta, Edmonton, AB T6G 2M8, Canada.
Br J Cancer. 2016 Jan 12;114(1):59-62. doi: 10.1038/bjc.2015.414. Epub 2015 Dec 8.
Metabolomics has shown promise in gastric cancer (GC) detection. This research sought to identify whether GC has a unique urinary metabolomic profile compared with benign gastric disease (BN) and healthy (HE) patients.
Urine from 43 GC, 40 BN, and 40 matched HE patients was analysed using (1)H nuclear magnetic resonance ((1)H-NMR) spectroscopy, generating 77 reproducible metabolites (QC-RSD <25%). Univariate and multivariate (MVA) statistics were employed. A parsimonious biomarker profile of GC vs HE was investigated using LASSO regularised logistic regression (LASSO-LR). Model performance was assessed using Receiver Operating Characteristic (ROC) curves.
GC displayed a clear discriminatory biomarker profile; the BN profile overlapped with GC and HE. LASSO-LR identified three discriminatory metabolites: 2-hydroxyisobutyrate, 3-indoxylsulfate, and alanine, which produced a discriminatory model with an area under the ROC of 0.95.
GC patients have a distinct urinary metabolite profile. This study shows clinical potential for metabolic profiling for early GC diagnosis.
代谢组学在胃癌（GC）检测中显示出前景。本研究旨在确定与良性胃病（BN）和健康（HE）患者相比，GC是否具有独特的尿液代谢组学特征。
使用氢核磁共振（¹H-NMR）光谱分析43例GC患者、40例BN患者和40例匹配的HE患者的尿液，产生77种可重复的代谢物（质量控制相对标准偏差<25%）。采用单变量和多变量（MVA）统计方法。使用套索正则化逻辑回归（LASSO-LR）研究GC与HE的简约生物标志物特征。使用受试者工作特征（ROC）曲线评估模型性能。
GC显示出明显的鉴别生物标志物特征；BN的特征与GC和HE重叠。LASSO-LR鉴定出三种鉴别性代谢物：2-羟基异丁酸、3-吲哚硫酸盐和丙氨酸，它们产生了一个ROC曲线下面积为0.95的鉴别模型。
GC患者具有独特的尿液代谢物特征。本研究显示了代谢谱分析在早期GC诊断中的临床潜力。
