Sun Ha Jee1, Jong Ho Lee2,3, Youngmin Han4,5, Hye Jin Yoo6. 1. Institute for Health Promotion, Graduate School of Public Health, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea. jsunha@yuhs.ac. 2. 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, Republic of Korea. jhleeb@yonsei.ac.kr. 3. Research Center for Silver Science, Institute of Symbiotic Life-TECH, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea. jhleeb@yonsei.ac.kr. 4. 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, Republic of Korea. 5. Institute for Health Promotion, Graduate School of Public Health, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea. 6. Research Center for Silver Science, Institute of Symbiotic Life-TECH, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Abstract
INTRODUCTION: Monitoring metabolic biomarkers could be utilized as an effective tool for the early detection of gastric cancer (GC) risk. OBJECTIVE: We aimed to discover predictive serum biomarkers for GC and investigate biomarker-related metabolism. METHODS: Subjects were randomly selected from the Korean Cancer Prevention Study-II cohort and matched by age and sex. We analyzed baseline serum samples of 160 subjects (discovery set; control and GC occurrence group, 80 each) via nontargeted screening. Identified putative biomarkers were validated in baseline serum samples of 140 subjects (validation set; control and GC occurrence group, 70 each) using targeted metabolites analysis. RESULTS: The final analysis was conducted on the discovery set (control, n = 52 vs. GC occurrence, n = 50) and the validation set (control, n = 43 vs. GC occurrence, n = 44) applying exclusion conditions. Eighteen putative metabolite sets differed between two groups found on nontargeted metabolic screening. We focused on fatty acid-related energy metabolism. In targeted analysis, levels of decanoyl-L-carnitine (p = 0.019), L-carnitine (p = 0.033), and citric acid (p = 0.025) were significantly lower in the GC occurrence group, even after adjusting for age, sex, and smoking status. Additionally, L-carnitine and citric acid were confirmed to have an independently significant relationship to GC development. Notably, alkaline phosphatase showed a significant correlation with these two biomarkers. CONCLUSION: Changes in serum L-carnitine and citric acid levels that may result from alterations of fatty-acid-related energy metabolism are expected to be valuable biomarkers for the early diagnosis of GC risk.
INTRODUCTION: Monitoring metabolic biomarkers could be utilized as an effective tool for the early detection of gastric cancer (GC) risk. OBJECTIVE: We aimed to discover predictive serum biomarkers for GC and investigate biomarker-related metabolism. METHODS: Subjects were randomly selected from the Korean Cancer Prevention Study-II cohort and matched by age and sex. We analyzed baseline serum samples of 160 subjects (discovery set; control and GC occurrence group, 80 each) via nontargeted screening. Identified putative biomarkers were validated in baseline serum samples of 140 subjects (validation set; control and GC occurrence group, 70 each) using targeted metabolites analysis. RESULTS: The final analysis was conducted on the discovery set (control, n = 52 vs. GC occurrence, n = 50) and the validation set (control, n = 43 vs. GC occurrence, n = 44) applying exclusion conditions. Eighteen putative metabolite sets differed between two groups found on nontargeted metabolic screening. We focused on fatty acid-related energy metabolism. In targeted analysis, levels of decanoyl-L-carnitine (p = 0.019), L-carnitine (p = 0.033), and citric acid (p = 0.025) were significantly lower in the GC occurrence group, even after adjusting for age, sex, and smoking status. Additionally, L-carnitine and citric acid were confirmed to have an independently significant relationship to GC development. Notably, alkaline phosphatase showed a significant correlation with these two biomarkers. CONCLUSION: Changes in serum L-carnitine and citric acid levels that may result from alterations of fatty-acid-related energy metabolism are expected to be valuable biomarkers for the early diagnosis of GC risk.
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