Bioinformatics Advance Access originally published online on September 25, 2009
Bioinformatics 2009 25(23):3151-3157; doi:10.1093/bioinformatics/btp558
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Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification
1 Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, 2 Bioanalysis and Biotransformation Research Center, KIST, Chengryang, Seoul 130-605 and 3 Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 443-749, Korea
* To whom correspondence should be addressed.
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Abstract |
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Motivation: 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.
Results: 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.
Contact: dhlee{at}biosoft.kaist.ac.kr
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Limsoon Wong
Received on May 27, 2009; revised on September 8, 2009; accepted on September 21, 2009
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