Ossolińska Anna, Płaza-Altamer Aneta, Ossoliński Krzysztof, Ossoliński Tadeusz, Ruman Tomasz, Nizioł Joanna
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.
Metabolomics. 2025 Jul 1;21(4):97. doi: 10.1007/s11306-025-02294-4.
Kidney cancer (KC) is a significant global health burden. Early diagnosis remains challenging due to the limited sensitivity and specificity of existing biomarkers. Metabolomics enables the detection of disease-specific metabolic alterations, offering potential for improved non-invasive biomarker discovery.
This study aims to characterize metabolic signatures distinguishing KC patients from non-cancer controls and evaluate the diagnostic potential of annotated metabolites in serum and urine.
An untargeted metabolomic analysis was performed on serum and urine samples from 56 KC patients and 200 controls using ultra-high-resolution mass spectrometry coupled with ultra-high-performance liquid chromatography (UHPLC-UHRMS in both positive and negative ionization modes with vacuum insulated probe heated electrospray ionization (VIP-HESI)). Samples were collected from the same individuals, which helped minimize inter-individual variability and enabled cross-biofluid comparison of metabolic profiles. Multivariate statistical techniques were applied to detect metabolic differences, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). An external validation strategy using training and validation subsets was employed to assess the robustness of candidate metabolite biomarkers matched in the discovery dataset.
Distinct metabolic signatures were observed between KC patients and controls, with key metabolic pathways involving lipid metabolism, amino acid biosynthesis, and glycerophospholipid metabolism. 19 serum and 12 urine metabolites showed high diagnostic potential (AUC > 0.90), demonstrating strong sensitivity and specificity.
These findings support the application of metabolomics for RCC detection and highlight the metabolic alterations associated with kidney cancer. Further validation in larger cohorts is necessary to confirm the clinical utility of these potential biomarkers.
肾癌是一项重大的全球健康负担。由于现有生物标志物的敏感性和特异性有限，早期诊断仍然具有挑战性。代谢组学能够检测疾病特异性的代谢改变，为改进非侵入性生物标志物的发现提供了潜力。
本研究旨在表征区分肾癌患者与非癌症对照的代谢特征，并评估血清和尿液中注释代谢物的诊断潜力。
使用超高分辨率质谱联用超高效液相色谱（在正离子和负离子模式下均采用真空绝热探针加热电喷雾电离（VIP-HESI）的超高效液相色谱-超高分辨率质谱法），对56例肾癌患者和200例对照的血清和尿液样本进行非靶向代谢组学分析。样本取自同一批个体，这有助于最小化个体间差异，并能够对代谢谱进行跨生物流体比较。应用多变量统计技术检测代谢差异，包括主成分分析（PCA）和正交偏最小二乘判别分析（OPLS-DA）。采用使用训练集和验证子集的外部验证策略，评估发现数据集中匹配的候选代谢物生物标志物的稳健性。
在肾癌患者和对照之间观察到明显的代谢特征，关键代谢途径涉及脂质代谢、氨基酸生物合成和甘油磷脂代谢。19种血清代谢物和12种尿液代谢物显示出较高的诊断潜力（AUC>0.90），具有很强的敏感性和特异性。
这些发现支持代谢组学在肾癌检测中的应用，并突出了与肾癌相关的代谢改变。需要在更大的队列中进行进一步验证，以确认这些潜在生物标志物的临床实用性。
