Suppr超能文献

人类膀胱组织提取物的代谢组学分析。

Metabolomic profiling of human bladder tissue extracts.

机构信息

Department of Urology, John Paul II Hospital, Grunwaldzka 4 St., 36-100, Kolbuszowa, Poland.

Faculty of Chemistry, Rzeszów University of Technology, 6 Powstańców Warszawy Ave., 35-959, Rzeszów, Poland.

出版信息

Metabolomics. 2024 Jan 24;20(1):14. doi: 10.1007/s11306-023-02076-w.

Abstract

INTRODUCTION

Bladder cancer is a common malignancy affecting the urinary tract and effective biomarkers and for which monitoring therapeutic interventions have yet to be identified.

OBJECTIVES

Major aim of this work was to perform metabolomic profiling of human bladder cancer and adjacent normal tissue and to evaluate cancer biomarkers.

METHODS

This study utilized nuclear magnetic resonance (NMR) and high-resolution nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS) methods to investigate polar metabolite profiles in tissue samples from 99 bladder cancer patients.

RESULTS

Through NMR spectroscopy, six tissue metabolites were identified and quantified as potential indicators of bladder cancer, while LDI-MS allowed detection of 34 compounds which distinguished cancer tissue samples from adjacent normal tissue. Thirteen characteristic tissue metabolites were also found to differentiate bladder cancer tumor grades and thirteen metabolites were correlated with tumor stages. Receiver-operating characteristics analysis showed high predictive power for all three types of metabolomics data, with area under the curve (AUC) values greater than 0.853.

CONCLUSION

To date, this is the first study in which bladder human normal tissues adjacent to cancerous tissues are analyzed using both NMR and MS method. These findings suggest that the metabolite markers identified in this study may be useful for the detection and monitoring of bladder cancer stages and grades.

摘要

简介

膀胱癌是一种常见的影响尿路的恶性肿瘤,目前尚未发现有效的生物标志物来监测治疗干预措施。

目的

本研究的主要目的是对人膀胱癌及相邻正常组织进行代谢组学分析,并评估癌症生物标志物。

方法

本研究采用核磁共振(NMR)和基于高分辨率纳米颗粒的激光解吸/电离质谱(LDI-MS)方法,对 99 例膀胱癌患者的组织样本进行了研究。

结果

通过 NMR 光谱分析,确定并定量了 6 种组织代谢物,它们可能是膀胱癌的指标,而 LDI-MS 则可以检测到 34 种化合物,这些化合物可以区分癌症组织样本和相邻的正常组织。还发现 13 种特征组织代谢物可以区分膀胱癌的肿瘤分级,13 种代谢物与肿瘤分期相关。受试者工作特征分析显示,所有三种代谢组学数据的预测能力均较高,曲线下面积(AUC)值均大于 0.853。

结论

迄今为止,这是首次同时使用 NMR 和 MS 方法对人膀胱癌及相邻正常组织进行分析的研究。这些发现表明,本研究中确定的代谢标志物可能有助于膀胱癌分期和分级的检测和监测。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验