Clear cell renal cell carcinoma (ccRCC) is the most lethal urological malignancy, with 30% of patients presenting with de novo metastasis in the clinic and an additional 30% further developing recurrent metastasis during disease progression. Current diagnostic and prognostic methods rely primarily on histopathological characterization, which limits early detection and intervention for recurrent metastasis.

This study employs liquid biopsy to enhance prognostication and clinical management of ccRCC patients across two sub-cohorts to identify biomarkers for localized and metastatic ccRCC, utilizing longitudinal plasma proteomics. We collected plasma samples from ccRCC patients before surgery (PRE-OP), after surgery (POST-OP), and at longitudinal follow-up time points. Using data-independent mass spectrometry, we analyzed the depleted plasma samples to identify biomarker candidates. Next, we employed network analysis to identify protein modules potentially contributing to the disease phenotype. Finally, we assessed the utility of our candidate proteins as biomarkers.

Our network analysis approach identified protein modules enriched in matrix remodeling and metabolic dysregulation pathways, perpetuating the ccRCC phenotype. First, we identified five proteins involved in angiogenic and immune responses as biomarker candidates for localized ccRCC. These proteins demonstrated high diagnostic performance, discriminating pre-operative samples from post-operative samples in our localized cohort. Next, we identified six proteins previously implicated in cancer metastasis as biomarker candidates for metastatic ccRCC. These candidates effectively distinguished metastatic from high-grade non-metastatic ccRCC patient plasma samples in our cohort.

These findings underscore the utility of plasma proteomics in biomarker discovery. Further validation of these signatures could inform clinical decision-making, enabling early detection and real-time longitudinal monitoring for ccRCC patients.