Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.
Bioinformatics. 2009 Dec 1;25(23):3151-7. doi: 10.1093/bioinformatics/btp558. Epub 2009 Sep 25.
For the early detection of cancer, highly sensitive and specific biomarkers are needed. Particularly, biomarkers in bio-fluids are relatively more useful because those can be used for non-biopsy tests. Although the altered metabolic activities of cancer cells have been observed in many studies, little is known about metabolic biomarkers for cancer screening. In this study, a systematic method is proposed for identifying metabolic biomarkers in urine samples by selecting candidate biomarkers from altered genome-wide gene expression signatures of cancer cells. Biomarkers identified by the present study have increased coherence and robustness because the significances of biomarkers are validated in both gene expression profiles and metabolic profiles.
The proposed method was applied to the gene expression profiles and urine samples of 50 breast cancer patients and 50 normal persons. Nine altered metabolic pathways were identified from the breast cancer gene expression signatures. Among these altered metabolic pathways, four metabolic biomarkers (Homovanillate, 4-hydroxyphenylacetate, 5-hydroxyindoleacetate and urea) were identified to be different in cancer and normal subjects (p <0.05). In the case of the predictive performance, the identified biomarkers achieved area under the ROC curve values of 0.75, 0.79 and 0.79, according to a linear discriminate analysis, a random forest classifier and on a support vector machine, respectively. Finally, biomarkers which showed consistent significance in pathways' gene expression as well as urine samples were identified.
Supplementary data are available at Bioinformatics online.
为了实现癌症的早期检测，我们需要高度敏感和特异的生物标志物。特别是，生物体液中的标志物因为可以用于非侵入性检测，所以相对更加有用。尽管在许多研究中都观察到癌细胞代谢活性的改变，但对于癌症筛查的代谢生物标志物却知之甚少。在这项研究中，我们提出了一种通过从癌细胞全基因组基因表达特征中选择候选生物标志物来鉴定尿液样本中代谢生物标志物的系统方法。本研究中鉴定的生物标志物具有更高的一致性和稳健性，因为生物标志物的显著性在基因表达谱和代谢谱中都得到了验证。
本方法应用于 50 例乳腺癌患者和 50 例正常人的基因表达谱和尿液样本。从乳腺癌基因表达特征中鉴定出 9 条改变的代谢途径。在这些改变的代谢途径中，有 4 种代谢生物标志物（高香草酸、4-羟基苯乙酸、5-羟色氨酸乙酸和尿素）在癌症和正常人群中存在差异（p<0.05）。在预测性能方面，根据线性判别分析、随机森林分类器和支持向量机，所鉴定的生物标志物的 ROC 曲线下面积分别为 0.75、0.79 和 0.79。最后，还鉴定出了在通路基因表达和尿液样本中都表现出一致性显著的生物标志物。
补充数据可在“Bioinformatics”在线获取。
