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Metabolic changes preceding bladder cancer occurrence among Korean men: a nested case-control study from the KCPS-II cohort

BACKGROUND: Bladder cancer (BLCA) research in Koreans is still lacking, especially in focusing on the prediction of BLCA. The current study aimed to discover metabolic signatures related to BLCA onset and confirm its potential as a biomarker. METHODS: We designed two nested case-control studies usin...

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Detalles Bibliográficos
Autores principales: Han, Youngmin, Kim, Unchong, Jung, Keum Ji, Lee, Ji-Young, Lee, Kwangbae, Shin, Sang Yop, Kimm, Heejin, Jee, Sun Ha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696702/
http://dx.doi.org/10.1186/s40170-023-00324-0
Descripción
Sumario:BACKGROUND: Bladder cancer (BLCA) research in Koreans is still lacking, especially in focusing on the prediction of BLCA. The current study aimed to discover metabolic signatures related to BLCA onset and confirm its potential as a biomarker. METHODS: We designed two nested case-control studies using Korean Cancer Prevention Study (KCPS)-II. Only males aged 35–69 were randomly selected and divided into two sets by recruitment organizations [set 1, BLCA (n = 35) vs. control (n = 35); set 2, BLCA (n = 31) vs. control (n = 31)]. Baseline serum samples were analyzed by non-targeted metabolomics profiling, and OPLS-DA and network analysis were performed. Calculated genetic risk score (GRS) for BLCA from all KCPS participants was utilized for interpreting metabolomics data. RESULTS: Critical metabolic signatures shown in the BLCA group were dysregulation of lysine metabolism and tryptophan-indole metabolism. Furthermore, the prediction model consisting of metabolites (lysine, tryptophan, indole, indoleacrylic acid, and indoleacetaldehyde) reflecting these metabolic signatures showed mighty BLCA predictive power (AUC: 0.959 [0.929–0.989]). The results of metabolic differences between GRS-high and GRS-low groups in BLCA indicated that the pathogenesis of BLCA is associated with a genetic predisposition. Besides, the predictive ability for BLCA on the model using GRS and five significant metabolites was powerful (AUC: 0.990 [0.980–1.000]). CONCLUSION: Metabolic signatures shown in the present research may be closely associated with BLCA pathogenesis. Metabolites involved in these could be predictive biomarkers for BLCA. It could be utilized for early diagnosis, prognostic diagnosis, and therapeutic targets for BLCA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40170-023-00324-0.