肾细胞癌（RCC）是泌尿系统的一种重要病理学，其患病率不断增加。然而，由于肾细胞癌表现的异质性，目前的临床方法在治疗肾细胞癌方面存在局限性。代谢分析被认为是临床上首选的无创方法，它可以极大地有益于 RCC 的表征。本研究构建了纳米粒子增强激光解吸电离质谱 (NELDI MS) 来分析肾肿瘤 (n = 456) 和健康对照 (n = 200) 的代谢指纹。分类模型得出的区分肾肿瘤与健康对照的曲线下面积 (AUC) 为 0.938（95% 置信区间 (CI)，0.884-0.967），区分恶性与良性肿瘤的曲线下面积 (AUC) 为 0.850（95% CI，0.821-0.915） 、RCC 亚型分类为 0.925-0.932（95% CI，0.821-0.915）。对于早期RCC亚型，测试集的平均诊断灵敏度为90.5%，特异性为91.3%。代谢生物标志物被确定为亚型诊断的潜在指标（p < 0.05）。为了验证预后性能，构建了 RCC 参与者的预测模型并实现疾病预测 (p = 0.003)。该研究为应用代谢分析工具进行 RCC 表征提供了广阔的前景。© 2024 作者。 《Advanced Science》由 Wiley‐VCH GmbH 出版。

Renal cell carcinoma (RCC) is a substantial pathology of the urinary system with a growing prevalence rate. However, current clinical methods have limitations for managing RCC due to the heterogeneity manifestations of the disease. Metabolic analyses are regarded as a preferred noninvasive approach in clinics, which can substantially benefit the characterization of RCC. This study constructs a nanoparticle-enhanced laser desorption ionization mass spectrometry (NELDI MS) to analyze metabolic fingerprints of renal tumors (n = 456) and healthy controls (n = 200). The classification models yielded the areas under curves (AUC) of 0.938 (95% confidence interval (CI), 0.884-0.967) for distinguishing renal tumors from healthy controls, 0.850 for differentiating malignant from benign tumors (95% CI, 0.821-0.915), and 0.925-0.932 for classifying subtypes of RCC (95% CI, 0.821-0.915). For the early stage of RCC subtypes, the averaged diagnostic sensitivity of 90.5% and specificity of 91.3% in the test set is achieved. Metabolic biomarkers are identified as the potential indicator for subtype diagnosis (p < 0.05). To validate the prognostic performance, a predictive model for RCC participants and achieve the prediction of disease (p = 0.003) is constructed. The study provides a promising prospect for applying metabolic analytical tools for RCC characterization.© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.