Hye Jin Yoo1, Minjoo Kim2, Minkyung Kim2, Minsik Kang1,3, Keum Ji Jung4, Se-Mi Hwang4, Sun Ha Jee4, Jong Ho Lee5,6,7. 1. Department of Food and Nutrition, Brain Korea 21 PLUS Project, College of Human Ecology, Yonsei University, Seoul, South Korea. 2. Research Center for Silver Science, Institute of Symbiotic Life-TECH, Yonsei University, Seoul, South Korea. 3. National Leading Research Laboratory of Clinical Nutrigenetics/Nutrigenomics, Department of Food and Nutrition, College of Human Ecology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea. 4. Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, South Korea. 5. Department of Food and Nutrition, Brain Korea 21 PLUS Project, College of Human Ecology, Yonsei University, Seoul, South Korea. jhleeb@yonsei.ac.kr. 6. Research Center for Silver Science, Institute of Symbiotic Life-TECH, Yonsei University, Seoul, South Korea. jhleeb@yonsei.ac.kr. 7. National Leading Research Laboratory of Clinical Nutrigenetics/Nutrigenomics, Department of Food and Nutrition, College of Human Ecology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea. jhleeb@yonsei.ac.kr.
Abstract
INTRODUCTION: Since blood is in contact with all tissues in the body and is considered to dynamically reflect the body's pathophysiological status, serum metabolomics changes are important and have diagnostic value in early cancer detection. OBJECTIVES: In this prospective study, we investigated the application of metabolomics to differentiate subjects with incident breast cancer (BC) from subjects who remained free of cancer during a mean follow-up period of 7 years with the aim of identifying valuable biomarkers for BC. METHODS: Baseline serum samples from 84 female subjects with incident BC (BC group) and 88 cancer-free female subjects (control group) were used. Metabolic alterations associated with BC were investigated via metabolomics analysis of the baseline serum samples using ultra-performance liquid chromatography-linear-trap quadrupole-Orbitrap mass spectrometry. RESULTS: A total of 57 metabolites were identified through the metabolic analysis. Among them, 20 metabolite levels were significantly higher and 22 metabolite levels were significantly lower in the BC group than in the control group at baseline. Ten metabolic pathways, including amino acid metabolism, arachidonic acid (AA) metabolism, fatty acid metabolism, linoleic acid metabolism, and retinol metabolism, showed significant differences between the BC group and the control group. Logistic regression revealed that the incidence of BC was affected by leucine, AA, prostaglandin (PG)J2, PGE2, and γ-linolenic acid (GLA). CONCLUSIONS: This prospective study showed the clinical relevance of dysregulation of various metabolisms on the incidence of BC. Additionally, leucine, AA, PGJ2, PGE2, and GLA were identified as independent variables affecting the incidence of BC.
INTRODUCTION: Since blood is in contact with all tissues in the body and is considered to dynamically reflect the body's pathophysiological status, serum metabolomics changes are important and have diagnostic value in early cancer detection. OBJECTIVES: In this prospective study, we investigated the application of metabolomics to differentiate subjects with incident breast cancer (BC) from subjects who remained free of cancer during a mean follow-up period of 7 years with the aim of identifying valuable biomarkers for BC. METHODS: Baseline serum samples from 84 female subjects with incident BC (BC group) and 88 cancer-free female subjects (control group) were used. Metabolic alterations associated with BC were investigated via metabolomics analysis of the baseline serum samples using ultra-performance liquid chromatography-linear-trap quadrupole-Orbitrap mass spectrometry. RESULTS: A total of 57 metabolites were identified through the metabolic analysis. Among them, 20 metabolite levels were significantly higher and 22 metabolite levels were significantly lower in the BC group than in the control group at baseline. Ten metabolic pathways, including amino acid metabolism, arachidonic acid (AA) metabolism, fatty acid metabolism, linoleic acid metabolism, and retinol metabolism, showed significant differences between the BC group and the control group. Logistic regression revealed that the incidence of BC was affected by leucine, AA, prostaglandin (PG)J2, PGE2, and γ-linolenic acid (GLA). CONCLUSIONS: This prospective study showed the clinical relevance of dysregulation of various metabolisms on the incidence of BC. Additionally, leucine, AA, PGJ2, PGE2, and GLA were identified as independent variables affecting the incidence of BC.
Entities:
Keywords:
Breast cancer; Cohort; Early biomarkers; Metabolites; Metabolomics; Prospective study
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