通过测量与炎症相关的92种蛋白质的相对浓度，对小型ccRCC患者的细胞自由尿液上清液和血浆进行个体分析。然后，借助癌症基因组图谱（TCGA），对编码上述蛋白质的基因进行有针对性的mRNA分析，并确定其对总体生存率（OS）的影响。样本是从ccRCC患者中前瞻性收集的。使用复合近距离扩展法测量细胞自由尿液上清液和血浆中92种与炎症有关的蛋白质的浓度。从TCGA获得来自ccRCC患者的转录组和临床信息。对蛋白质浓度数据进行无监督聚类和差异蛋白质表达分析。使用TCGA对编码显著差异表达蛋白质的基因进行有针对性的mRNA分析。使用后退逐步回归分析构建了一个名词对数表。通过判别和校准以及决策曲线分析（DCA）评估了名数表的性能和临床效益。无监督聚类分析揭示了ccRCC患者细胞自由尿液上清液中的炎症标志。使用TCGA数据进行后退逐步回归分析，确定了与OS相关的转录组风险因素和风险组。使用这些风险因素开发了预测2年和5年OS的名数表。DCA显示，与全治疗 / 无治疗策略相比，我们的模型与净益改善相关。结论：我们使用蛋白质组学和转录组学数据定义了四个新型生物标志物，可区分ccRCC的预后严重程度。我们表明，这些生物标志物可用于预测不同肿瘤阶段的ccRCC的2年和5年OS。如果将来得到验证，这种分析可提供非侵入性的预后信息，从而为患者管理或监测策略提供指导。本文受版权保护。保留所有权利。

To profile cell-free urine supernatant and plasma of a small cohort of ccRCC patients by measuring the relative concentrations of 92 proteins related to inflammation. Using The Cancer Genome Atlas (TCGA), we then performed a targeted mRNA analysis of genes encoding the above proteins and defined their effects on overall survival (OS).Samples were collected prospectively from ccRCC patients. A multiplex proximity extension assay was used to measure the concentrations of 92 inflammation-related proteins in cell-free urine supernatants and plasma. Transcriptomic and clinical information from ccRCC patients was obtained from TCGA. Unsupervised clustering and differential protein expression analyses were performed on protein concentration data. Targeted mRNA analysis on genes encoding significant differentially expressed proteins was performed using TCGA. Backward stepwise regression analyses were used to build a nomogram. The performance of the nomogram and clinical benefit was assessed by discrimination and calibration, and a decision curve analysis (DCA), respectively.Unsupervised clustering analysis revealed inflammatory signatures in cell-free urine supernatant of ccRCC patients. Backward stepwise regressions using TCGA data identified transcriptomic risk factors and risk groups associated with OS. A nomogram to predict 2-year and 5-year OS was developed using these risk factors. The DCA revealed that our model was associated with a net benefit improvement compared to the treat all/none strategies CONCLUSION: We defined four novel biomarkers using proteomic and transcriptomic data that distinguish severity of prognosis in ccRCC. We show that these biomarkers can be used in a model to predict 2-year and 5-year OS in ccRCC across different tumor stages. This type of analysis, if validated in the future, provides non-invasive prognostic information that could inform either management or surveillance strategies for patients.This article is protected by copyright. All rights reserved.