NMR-based metabolomic techniques identify potential urinary biomarkers for early colorectal cancer detection
NMR-based metabolomic techniques identify potential urinary biomarkers for early colorectal cancer detection
复制标题
DOI：
10.18632/oncotarget.22402
复制
发表时间：
2017-11
期刊：
影响因子：
--
通讯作者：
Zhening Wang;Yan Lin;Jiahao Liang;Yao Huang;Chang-chun Ma;Xingmu Liu;Jurong Yang
中科院分区：
文献类型：
--
作者：
Zhening Wang;Yan Lin;Jiahao Liang;Yao Huang;Chang-chun Ma;Xingmu Liu;Jurong Yang
需要更好的早期检测方法来改善结直肠癌（CRC）患者的预后。质子核磁共振波谱（1H-NMR）是一种潜在的非侵入性早期肿瘤检测方法，用于分析55名结直肠癌患者和40名健康对照（hc）的尿液代谢物。将正交偏最小二乘判别分析（OPLS-DA）的模式识别应用于1H-NMR处理数据。通过与食管癌（EC, n=18）的比较，证实了模型的特异性。独特的代谢组学特征将所有CRC阶段与HC尿液样本区分开来。在I/II期CRC中共鉴定出16种潜在的生物标志物代谢物，表明氨基酸代谢、糖酵解、三羧酸（TCA）循环、尿素循环、胆碱代谢和肠道菌群代谢途径中断。早期结直肠癌和EC患者的代谢物谱也可以明显区分，这表明上、下消化道癌症具有不同的代谢组学谱。我们的研究评估了结直肠癌患者尿液样本中重要的代谢组学变化，提供了与其他基于生物体液的代谢组学分析收集的信息相补充的信息，并阐明了驱动结直肠癌的潜在代谢机制。我们的研究结果支持基于核磁共振的尿液代谢组学指纹图谱在CRC早期诊断中的应用。
Better early detection methods are needed to improve the outcomes of patients with colorectal cancer (CRC). Proton nuclear magnetic resonance spectroscopy (1H-NMR), a potential non-invasive early tumor detection method, was used to profile urine metabolites from 55 CRC patients and 40 healthy controls (HCs). Pattern recognition through orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to 1H-NMR processed data. Model specificity was confirmed by comparison with esophageal cancers (EC, n=18). Unique metabolomic profiles distinguished all CRC stages from HC urine samples. A total of 16 potential biomarker metabolites were identified in stage I/II CRC, indicating amino acid metabolism, glycolysis, tricarboxylic acid (TCA) cycle, urea cycle, choline metabolism, and gut microflora metabolism pathway disruptions. Metabolite profiles from early stage CRC and EC patients were also clearly distinguishable, suggesting that upper and lower gastrointestinal cancers have different metabolomic profiles. Our study assessed important metabolomic variations in CRC patient urine samples, provided information complementary to that collected from other biofluid-based metabolomics analyses, and elucidated potential underlying metabolic mechanisms driving CRC. Our results support the utility of NMR-based urinary metabolomics fingerprinting in early diagnosis of CRC.