Miyake Makito, Iida Kota, Nishimura Nobutaka, Ohnishi Sayuri, Owari Takuya, Fujii Tomomi, Oda Yuki, Miyamoto Tatsuki, Shimizu Takuto, Ohnishi Kenta, Hori Shunta, Morizawa Yosuke, Gotoh Daisuke, Nakai Yasushi, Tanaka Nobumichi, Fujimoto Kiyohide
Department of Urology, Nara Medical University, Kashihara, Nara, Japan.
Division of Cancer Immunology, National Cancer Center, Tsukiji, Chuo-ku, Tokyo, Japan.
Bladder Cancer. 2025 Mar 18;11(1):23523735251325100. doi: 10.1177/23523735251325100. eCollection 2025 Jan-Mar.
Metabolomic research and metabolomics-based biomarkers predicting treatment outcomes in bladder cancer remain limited.
We explored the serum metabolites potentially associated with the risk of recurrence after intravesical Bacillus Calmette-Guérin (BCG) therapy.
Two independent cohorts, a discovery cohort (n = 23) and a validation cohort (n = 40), were included in this study. Blood was collected before the induction of BCG therapy (pre-BCG blood; both discovery and validation cohorts) and after six doses of BCG (post-BCG blood; only discovery cohort). Metabolome analysis of serum samples was conducted using capillary electrophoresis time-of-flight mass spectrometry. The endpoint was intravesical recurrence-free survival, which was analysed using Kaplan-Meier estimates, the log-rank test, and the Cox proportional hazard model.
Of the 353 metabolites quantified, nine (2.5%) and four (1.1%) were significantly upregulated and downregulated, respectively. The heatmap of hierarchical clustering analysis and principal coordinate analysis for the fold changes and in serum metabolites differentiated 10 recurrent cases and 13 non-recurrent cases in the discovery cohort. A metabolome response-based scoring model using 16 metabolites, including threonine and N6,N6,N6-trimethyl-lysine effectively stratified the risk of post-BCG recurrence. Additionally, pre-BCG metabolome-based score models using six metabolites, octanoylcarnitine, S-methylcysteine-S-oxide, theobromine, carnitine, indole-3-acetic acid, and valeric acid, were developed from the discovery cohort. Univariate and multivariate analyses confirmed a high predictive accuracy in the validation and combination cohorts.
We demonstrated that numerous types of serum metabolites were altered in response to intravesical BCG and developed high-performance score models which might effectively differentiated the risk of post-BCG tumour recurrence.
代谢组学研究以及基于代谢组学预测膀胱癌治疗结果的生物标志物仍然有限。
我们探索了与膀胱内卡介苗(BCG)治疗后复发风险潜在相关的血清代谢物。
本研究纳入了两个独立队列,一个发现队列(n = 23)和一个验证队列(n = 40)。在BCG治疗诱导前(BCG治疗前血液;发现队列和验证队列均采集)以及六剂BCG治疗后(BCG治疗后血液;仅发现队列采集)采集血液。使用毛细管电泳飞行时间质谱对血清样本进行代谢组分析。终点是膀胱内无复发生存期,使用Kaplan-Meier估计、对数秩检验和Cox比例风险模型进行分析。
在定量的353种代谢物中,分别有9种(2.5%)显著上调和4种(1.1%)显著下调。发现队列中10例复发病例和13例未复发病例的血清代谢物倍数变化的层次聚类分析热图和主坐标分析。使用包括苏氨酸和N6,N6,N6-三甲基赖氨酸在内的16种代谢物的基于代谢组反应的评分模型有效地分层了BCG治疗后复发的风险。此外,从发现队列中开发了使用六种代谢物(辛酰肉碱、S-甲基半胱氨酸-S-氧化物、可可碱、肉碱、吲哚-3-乙酸和戊酸)的基于BCG治疗前代谢组的评分模型。单变量和多变量分析证实了在验证队列和联合队列中的高预测准确性。
我们证明了多种类型的血清代谢物因膀胱内BCG治疗而发生改变,并开发了高性能评分模型,这些模型可能有效地区分BCG治疗后肿瘤复发的风险。