Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
BJU Int. 2023 Jul;132(1):75-83. doi: 10.1111/bju.15989. Epub 2023 Mar 7.
To profile the cell-free urine supernatant and plasma of a small cohort of clear-cell renal cell carcinoma (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).
SUBJECTS/PATIENTS AND METHODS: 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, respectively.
Unsupervised clustering analysis revealed inflammatory signatures in the 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 decision curve analysis showed that our model was associated with a net benefit improvement compared to the treat-all/none strategies.
We defined four novel biomarkers using proteomic and transcriptomic data that distinguish severity of prognosis in ccRCC. We showed that these biomarkers can be used in a model to predict 2-year and 5-year OS in ccRCC across different tumour 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.
通过测量 92 种与炎症相关的蛋白质的相对浓度，描绘一小部分透明细胞肾细胞癌 (ccRCC) 患者的无细胞尿液上清液和血浆特征。我们使用癌症基因组图谱 (TCGA) 对编码上述蛋白质的基因进行了靶向 mRNA 分析，并定义了它们对总生存期 (OS) 的影响。
对象/患者和方法：前瞻性收集 ccRCC 患者的样本。使用多重邻近延伸测定法测量无细胞尿液上清液和血浆中 92 种炎症相关蛋白的浓度。从 TCGA 获得 ccRCC 患者的转录组学和临床信息。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 对显著差异表达蛋白的编码基因进行靶向 mRNA 分析。使用向后逐步回归分析构建列线图。通过区分度和校准评估列线图和临床获益的性能，并分别进行决策曲线分析。
无监督聚类分析显示 ccRCC 患者无细胞尿液上清液中存在炎症特征。使用 TCGA 数据进行的向后逐步回归确定了与 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 的列线图。决策曲线分析表明，与“治疗所有/无”策略相比，我们的模型与净效益改善相关。
我们使用蛋白质组学和转录组学数据定义了四个新的生物标志物，可区分 ccRCC 预后严重程度。我们表明，这些生物标志物可用于模型中预测 ccRCC 不同肿瘤分期的 2 年和 5 年 OS。如果在未来得到验证，这种分析提供了非侵入性的预后信息，可以为患者的管理或监测策略提供信息。
