Urine supernatant reveals a signature that predicts survival in clear‐cell renal cell carcinoma

Abstract

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 MethodsSamples 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. ResultsUnsupervised 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. ConclusionWe 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.

Advances in aqueous humor proteomics for biomarker discovery and disease mechanisms exploration: a spotlight on primary open angle glaucoma

“…Novel affinity proteomics discovery platforms, such as SomaScan 11k and Olink Explore HT, enable the analysis of thousands of protein targets in a semi-quantitative manner—without any sample pre-processing. While being largely developed for the analysis of blood-derived samples, the platforms offer access to sample matrices beyond those, including saliva ( Scholtz et al, 2020 ), urine ( Daza et al, 2023 ), ascites ( Finkernagel et al, 2019 ) and many other bodily fluids including AH and vitreous humor (VH) ( Lamy et al, 2020 ; Peng et al, 2023 ; Wolf et al, 2023 ).…”

Round up

“…This study aimed to identify biomarkers to predict the prognosis of patients with clear cell renal cell carcinoma (ccRCC) using proteomic and transcriptomic data. [ 2 ] The researchers measured the concentrations of 92 inflammation-related proteins in cell-free urine supernatant and plasma from 17 patients of ccRCC. They performed a targeted mRNA analysis of genes encoding these proteins using the Cancer Genome Atlas.…”

Landscaping of Urine Proteome: Unlocking Diagnostic Potential and Overcoming Unique Challenges

“…(12) Recent advancements in protein measurements, particularly with innovations like the Proximity Extension Assay, coupled with advanced AI algorithms, hold the transformative potential to reshape the landscape of urine-based diagnostics. These innovations not only enable the detection of low-abundance biomarkers in urine with remarkable sensitivity and specificity (13)(14)(15)(16)(17)(18)(19) but also open the door to a more comprehensive diagnostic approach. Rather than concentrating solely on individual biomarkers, we are now poised to identify the unique patterns associated with each disease within urine samples.…”