背景：韩国人的膀胱癌（BLCA）研究仍然缺乏，特别是在关注BLCA的预测方面。目前的研究旨在发现与BLCA发病相关的代谢特征，并确认其作为生物标志物的潜力。
方法：我们使用韩国癌症预防研究（KCPS）-II设计了两项巢式病例对照研究。仅随机选择35-69岁的男性，并由招募组织分为两组[第1组，BLCA（n=35）与对照(n=35)；组2，BLCA(n=31)与控制(n=31)]。通过非靶向代谢组学分析基线血清样品，进行了OPLS-DA和网络分析。来自所有KCPS参与者的BLCA的计算的遗传风险评分(GRS)用于解释代谢组学数据。
结果：在BLCA组中显示的关键代谢特征是赖氨酸代谢和色氨酸-吲哚代谢的失调。此外，由代谢物(赖氨酸，色氨酸,吲哚,吲哚丙烯酸,和吲哚乙醛）反映这些代谢特征显示出强大的BLCA预测能力（AUC：0.959[0.929-0.989]）。BLCA中GRS高组和GRS低组之间的代谢差异结果表明，BLCA的发病机理与遗传易感性有关。此外,在使用GRS和5种显著代谢物的模型上,BLCA的预测能力是强大的(AUC:0.990[0.980-1.000])。
结论：本研究显示的代谢特征可能与BLCA发病机制密切相关。参与这些的代谢物可能是BLCA的预测性生物标志物。它可以用于早期诊断，预后诊断,和BLCA的治疗目标。
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]).
CONCLUSIONS: 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.
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