Nam, Hojung Chung, Bong Chul Kim, Younghoon Lee, KiYoung Lee, Doheon 2024-01-20T20:04:15Z 2024-01-20T20:04:15Z 2021-09-05 2009-12-01 1367-4803 https://pubs.kist.re.kr/handle/201004/131888 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&apos; gene expression as well as urine samples were identified. English OXFORD UNIV PRESS GENE-EXPRESSION METABOLISM TRANSFAC(R) MAMMOGRAPHY PREDICTION PATHWAY KEGG Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification Article 10.1093/bioinformatics/btp558 1 BIOINFORMATICS, v.25, no.23, pp.3151 - 3157 BIOINFORMATICS 25 23 3151 3157 scie scopus 000272080800016 2-s2.0-75949130452 Biochemical Research Methods Biotechnology & Applied Microbiology Computer Science, Interdisciplinary Applications Mathematical & Computational Biology Statistics & Probability Biochemistry & Molecular Biology Biotechnology & Applied Microbiology Computer Science Mathematical & Computational Biology Mathematics Article GENE-EXPRESSION METABOLISM TRANSFAC(R) MAMMOGRAPHY PREDICTION PATHWAY KEGG biomarker breast cancer metabolomics transcriptomics