Chaowen Yu1,2, Lingfang Niu2, Luo Li2, Ting Li2, Limei Duan2, Zhenting He2, Yan Zhao2, Lin Zou1, Xiaohou Wu3, Chunli Luo2. 1. Center for Clinical Molecular Medicine & Newborn Screening, Children's Hospital of Chongqing Medical University; National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China. 2. The Key Laboratory of Diagnostics Medicine Designated by the Ministry of Education, Chongqing Medical University, Chongqing, China. 3. Department of Urolog, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Abstract
OBJECTIVE: Prostate cancer (PCa) is one of the most commonly diagnosed cancers among men which is associated with profound metabolic changes. Systematic analysis of the metabolic alterations and identification of new biomarkers may benefit PCa diagnosis and a deep understanding of the pathological mechanism. The purpose of this study was to determine the metabolic features of PCa. METHODS: Plasma and urine metabolites from 89 prostate cancer (PCa) patients, 84 benign prostatic hyperplasia (BPH) patients, and 70 healthy males were analyzed using LC-MS/MS and GC-MS. The Orthogonalised Partial Least Squares Discriminant Analysis (OPLS-DA) was used to find the significantly changed metabolites. The clinical value of the candidate markers was examined by receiver operating characteristic curve analysis and compared with prostate-specific antigen (PSA). RESULTS: Multivariate statistical analyses found a series of altered metabolites, which related to the urea cycle, tricarboxylic acid cycle (TCA), fatty acid metabolism, and the glycine cleavage system. Plasma Glu/Gln showed the highest predictive value (AUC = 0.984) when differentiating PCa patients from healthy controls, with a higher sensitivity than PSA (96.6% vs. 94.4%). Both Glu/Gln and PSA displayed a low specificity when differentiating PCa patients from BPH patients (<53.2%), while the combination of Glu/Gln and PSA can further increase the diagnostic specificity to 66.9%. CONCLUSIONS: The present study showed the metabolic features of PCa, provided strong evidence that the amide nitrogen and the energy metabolic pathways could be a valuable source of markers for PCa. Several candidate markers identified in this study were clinically valuable for further assessment.
OBJECTIVE: Prostate cancer (PCa) is one of the most commonly diagnosed cancers among men which is associated with profound metabolic changes. Systematic analysis of the metabolic alterations and identification of new biomarkers may benefit PCa diagnosis and a deep understanding of the pathological mechanism. The purpose of this study was to determine the metabolic features of PCa. METHODS: Plasma and urine metabolites from 89 prostate cancer (PCa) patients, 84 benign prostatic hyperplasia (BPH) patients, and 70 healthy males were analyzed using LC-MS/MS and GC-MS. The Orthogonalised Partial Least Squares Discriminant Analysis (OPLS-DA) was used to find the significantly changed metabolites. The clinical value of the candidate markers was examined by receiver operating characteristic curve analysis and compared with prostate-specific antigen (PSA). RESULTS: Multivariate statistical analyses found a series of altered metabolites, which related to the urea cycle, tricarboxylic acid cycle (TCA), fatty acid metabolism, and the glycine cleavage system. Plasma Glu/Gln showed the highest predictive value (AUC = 0.984) when differentiating PCa patients from healthy controls, with a higher sensitivity than PSA (96.6% vs. 94.4%). Both Glu/Gln and PSA displayed a low specificity when differentiating PCa patients from BPH patients (<53.2%), while the combination of Glu/Gln and PSA can further increase the diagnostic specificity to 66.9%. CONCLUSIONS: The present study showed the metabolic features of PCa, provided strong evidence that the amide nitrogen and the energy metabolic pathways could be a valuable source of markers for PCa. Several candidate markers identified in this study were clinically valuable for further assessment.