Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, Texas.
Dan L. Duncan Cancer Center, Advanced Technology Core, Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, Texas.
Cancer Epidemiol Biomarkers Prev. 2019 Apr;28(4):770-781. doi: 10.1158/1055-9965.EPI-18-0936. Epub 2019 Jan 14.
The current system to predict the outcome of smokers with bladder cancer is insufficient due to complex genomic and transcriptomic heterogeneities. This study aims to identify serum metabolite-associated genes related to survival in this population.
We performed LC/MS-based targeted metabolomic analysis for >300 metabolites in serum obtained from two independent cohorts of bladder cancer never smokers, smokers, healthy smokers, and healthy never smokers. A subset of differential metabolites was validated using Biocrates absoluteIDQ p180 Kit. Genes associated with differential metabolites were integrated with a publicly available cohort of The Cancer Genome Atlas (TCGA) to obtain an intersecting signature specific for bladder cancer smokers.
Forty metabolites (FDR < 0.25) were identified to be differential between bladder cancer never smokers and smokers. Increased abundance of amino acids (tyrosine, phenylalanine, proline, serine, valine, isoleucine, glycine, and asparagine) and taurine were observed in bladder cancer smokers. Integration of differential metabolomic gene signature and transcriptomics data from TCGA cohort revealed an intersection of 17 genes that showed significant correlation with patient survival in bladder cancer smokers. Importantly, catechol-O-methyltransferase, iodotyrosine deiodinase, and tubulin tyrosine ligase showed a significant association with patient survival in publicly available bladder cancer smoker datasets and did not have any clinical association in never smokers.
Serum metabolic profiling of bladder cancer smokers revealed dysregulated amino acid metabolism. It provides a distinct gene signature that shows a prognostic value in predicting bladder cancer smoker survival.
Serum metabolic signature-derived genes act as a predictive tool for studying the bladder cancer progression in smokers.
由于基因组和转录组的异质性复杂，目前预测吸烟膀胱癌患者预后的系统还不够完善。本研究旨在鉴定与该人群生存相关的血清代谢物相关基因。
我们对两个独立队列的膀胱癌从不吸烟者、吸烟者、健康吸烟者和健康从不吸烟者的血清进行了基于 LC/MS 的靶向代谢组学分析，检测了 >300 种代谢物。使用 Biocrates absoluteIDQ p180 Kit 对部分差异代谢物进行了验证。将与差异代谢物相关的基因与公开的癌症基因组图谱（TCGA）队列整合，以获得膀胱癌吸烟者特有的交集特征。
鉴定出 40 种代谢物（FDR<0.25）在膀胱癌从不吸烟者和吸烟者之间存在差异。膀胱癌吸烟者的氨基酸（酪氨酸、苯丙氨酸、脯氨酸、丝氨酸、缬氨酸、异亮氨酸、甘氨酸和天冬酰胺）和牛磺酸的丰度增加。将差异代谢组学基因特征与 TCGA 队列的转录组学数据整合，揭示了 17 个与膀胱癌吸烟者患者生存显著相关的基因的交集。重要的是，儿茶酚-O-甲基转移酶、碘酪氨酸脱碘酶和微管蛋白酪氨酸连接酶与公开的膀胱癌吸烟者数据集的患者生存显著相关，而在从不吸烟者中没有任何临床关联。
膀胱癌吸烟者的血清代谢组学分析显示出氨基酸代谢失调。它提供了一个独特的基因特征，可用于预测膀胱癌吸烟者的生存预后。
血清代谢组学衍生的基因可作为研究吸烟者膀胱癌进展的预测工具。
