Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification

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 s...

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Bibliographic Details
Published in:Bioinformatics 2009-12, Vol.25 (23), p.3151-3157
Main Authors: Nam, Hojung, Chung, Bong Chul, Kim, Younghoon, Lee, KiYoung, Lee, Doheon
Format: Article
Language:English
Subjects:
Biological and medical sciences
Biomarkers, Tumor - metabolism
Biomarkers, Tumor - urine
Breast Neoplasms - genetics
Breast Neoplasms - metabolism
Breast Neoplasms - pathology
Female
Fundamental and applied biological sciences. Psychology
Gene Expression Profiling - methods
Gene Expression Regulation, Neoplastic
General aspects
Homovanillic Acid - metabolism
Homovanillic Acid - urine
Humans
Hydroxyindoleacetic Acid - metabolism
Hydroxyindoleacetic Acid - urine
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Metabolomics - methods
Phenylacetates - metabolism
Phenylacetates - urine
Proteomics - methods
ROC Curve
Urea - metabolism
Urea - urine
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Summary: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
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btp558