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bioRxivMolecular BiologyPreprint

Proteomic characterization of sporadic clear cell renal cell carcinoma reveals a matrix-dense pseudocapsule and heterogeneous tumor subtypes

Tobias FeileniD, Grigor AndreeviD, Manuel RoggiD, Miguel Cosenza‐ContrerasiD, Niko PinteriD, Max ZirngibliD, Antonia Elsässer, Pitter F. HuesgeniD, Markus Tobias GrabbertiD, Christoph SchelliD, Oliver SchillingiD

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Abstract

Clear cell renal cell carcinoma (ccRCC) frequently develops a fibrotic pseudocapsule (PC) at the tumor boundary, yet its implications in the disease remain incompletely defined. We performed DIA-based proteomics on formalin-fixed, paraffin-embedded (FFPE) specimens of sporadic ccRCC from 153 patients, comprising 142 tumor, 123 pseudocapsule (PC), and 121 non-malignant adjacent tissue (NAT) samples. The PC displayed a distinctive proteomic signature characterized by matrix accumulation, enhanced remodeling and ECM-cell signaling, consistent with a signaling-competent boundary and active growth factor sequestration rather than a passive fibrotic barrier. Within the tumor, we observed canonical metabolic reprogramming with upregulated glycolysis and hypoxia markers and suppression of aerobic metabolism, accompanied by strong cell-cycle and pro-angiogenic signatures. Immune programs included increased antigen processing and presentation, together with elevated inflammasome and pyroptosis signatures. T-cell markers were enriched in the PC, indicating an immune-active boundary. Among tumors, we identified five distinct proteomic subtypes (C1-C5) spanning proliferative/mitotic, metabolic, immune/EMT/mTOR, and ECM phenotypes. Semi-specific peptide analysis indicated elevated endogenous proteolysis in the tumor and varied across clusters, with C1 and C5 showing the greatest proteolytic burden. Mechanotransduction features showed only modest PC elevation but were dominated by inter-patient variation. The present study defines a matrix-rich, signaling-active pseudocapsule and a classification of heterogeneous tumor subtypes in sporadic ccRCC. The framework provides a compartment-resolved, cluster-informed approach, and it highlights actionable axes of potential clinical relevance.

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