乳腺癌
生物标志物
代谢组学
生物标志物发现
癌症生物标志物
癌症
计算生物学
生物
转录组
尿
生物信息学
基因表达
基因
蛋白质组学
遗传学
内分泌学
作者
Hojung Nam,Bong Chul Chung,Young-Hoon Kim,KiYoung Lee,Doheon Lee
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2009-09-25
卷期号:25 (23): 3151-3157
被引量:114
标识
DOI:10.1093/bioinformatics/btp558
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI