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SAL_17229 details
Primary information
SALIDSAL_17229
Biomarker namep-Hydroxyphenylacetic acid [HMDB0000020]
Biomarker TypeNA
Sampling MethodAdult (>18 years old); Gender-female
Collection MethodNA
Analysis MethodNA
Collection SiteSaliva
Disease CategoryCancer
Disease/ConditionBreast Cancer
Disease SubtypeNA
Fold Change/ ConcentrationNA
Up/DownregulatedNA
ExosomalNA
OrganismHomo sapiens
PMID19783829
Year of Publication2009
Biomarker ID127
Biomarker CategoryMetabolite
SequenceC1=CC(=CC=C1CC(=O)O)O
Title of studyCombining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification
Abstract of studyMOTIVATION: 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.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.