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

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Title: Metabolic changes preceding bladder cancer occurrence among Korean men: a nested case-control study from the KCPS-II cohort
Authors: Youngmin Han, Unchong Kim, Keum Ji Jung, Ji-Young Lee, Kwangbae Lee, Sang Yop Shin, Heejin Kimm, Sun Ha Jee
Source: Cancer & Metabolism, Vol 11, Iss 1, Pp 1-12 (2023)
Publisher Information: BMC, 2023.
Publication Year: 2023
Collection: LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Subject Terms: Bladder cancer, Predictive biomarker, LC/MS metabolomics, Gaussian graphical model, Lysine metabolism, Tryptophan-indole metabolism, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Description: Abstract 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.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2049-3002
Relation: https://doaj.org/toc/2049-3002
DOI: 10.1186/s40170-023-00324-0
Access URL: https://doaj.org/article/9e4c2de6b97f45c19f8aa2fb758279b8
Accession Number: edsdoj.9e4c2de6b97f45c19f8aa2fb758279b8
Database: Directory of Open Access Journals
Description
Abstract:Abstract 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.
ISSN:20493002
DOI:10.1186/s40170-023-00324-0