Kim Kyoungmi, Aronov Pavel, Zakharkin Stanislav O, Anderson Danielle, Perroud Bertrand, Thompson Ian M, Weiss Robert H
Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, California 95616, USA.
Mol Cell Proteomics. 2009 Mar;8(3):558-70. doi: 10.1074/mcp.M800165-MCP200. Epub 2008 Nov 13.
Renal cell carcinoma (RCC) accounts for 11,000 deaths per year in the United States. When detected early, generally serendipitously by imaging conducted for other reasons, long term survival is generally excellent. When detected with symptoms, prognosis is poor. Under these circumstances, a screening biomarker has the potential for substantial public health benefit. The purpose of this study was to evaluate the utility of urine metabolomics analysis for metabolomic profiling, identification of biomarkers, and ultimately for devising a urine screening test for RCC. Fifty urine samples were obtained from RCC and control patients from two institutions, and in a separate study, urine samples were taken from 13 normal individuals. Hydrophilic interaction chromatography-mass spectrometry was performed to identify small molecule metabolites present in each sample. Cluster analysis, principal components analysis, linear discriminant analysis, differential analysis, and variance component analysis were used to analyze the data. Previous work is extended to confirm the effectiveness of urine metabolomics analysis using a larger and more diverse patient cohort. It is now shown that the utility of this technique is dependent on the site of urine collection and that there exist substantial sources of variation of the urinary metabolomic profile, although group variation is sufficient to yield viable biomarkers. Surprisingly there is a small degree of variation in the urinary metabolomic profile in normal patients due to time since the last meal, and there is little difference in the urinary metabolomic profile in a cohort of pre- and postnephrectomy (partial or radical) renal cell carcinoma patients, suggesting that metabolic changes associated with RCC persist after removal of the primary tumor. After further investigations relating to the discovery and identity of individual biomarkers and attenuation of residual sources of variation, our work shows that urine metabolomics analysis has potential to lead to a diagnostic assay for RCC.
在美国，每年有11000人死于肾细胞癌（RCC）。若早期发现，通常是因其他原因进行影像学检查时偶然发现，其长期生存率一般很高。若出现症状时才被发现，则预后较差。在这种情况下，一种筛查生物标志物有可能带来巨大的公共卫生效益。本研究的目的是评估尿液代谢组学分析在代谢组学图谱绘制、生物标志物鉴定以及最终设计RCC尿液筛查试验方面的效用。从两个机构的RCC患者和对照患者中获取了50份尿液样本，在另一项研究中，从13名正常个体中采集了尿液样本。采用亲水作用色谱 - 质谱法来鉴定每个样本中存在的小分子代谢物。使用聚类分析、主成分分析、线性判别分析、差异分析和方差成分分析来分析数据。扩展先前的工作以使用更大且更多样化的患者队列来确认尿液代谢组学分析的有效性。现已表明，该技术的效用取决于尿液采集部位，并且尿液代谢组学图谱存在大量变异来源，尽管组间变异足以产生可行的生物标志物。令人惊讶的是，正常患者的尿液代谢组学图谱因距上次进餐时间不同而存在小程度的变异，并且在一组肾切除术前和术后（部分或根治性）的肾细胞癌患者中，尿液代谢组学图谱几乎没有差异，这表明与RCC相关的代谢变化在原发肿瘤切除后仍然存在。在对个体生物标志物的发现和鉴定以及残余变异来源的衰减进行进一步研究之后，我们的工作表明尿液代谢组学分析有潜力导致一种RCC诊断检测方法。
