Machine learning-derived mast cell-associated angiogenesis features can serve as prognostic targets for clear cell renal cell carcinoma.

Background: In clear cell renal cell carcinoma (ccRCC), mast cell activation and angiogenesis are crucial for disease progression, with interactions occurring between these processes. The involvement of mast cell-related angiogenic characteristics in ccRCC is not yet fully elucidated. To address this gap, this study aims to clarify the biological role and prognostic significance of mast cell-mediated angiogenesis in ccRCC, and to examine its links to the tumor microenvironment and disease progression. Methods: We utilized bioinformatics techniques to integrate and analyze single-cell and bulk transcriptomics data. We developed prognostic models using ten classical machine learning algorithms and conducted intergroup differential gene extraction, functional pathway enrichment, immune infiltration, and somatic mutation analyses. Finally, the expression levels of the model genes were verified by quantitative real-time polymerase chain reaction (qRT-PCR). Results: TNF-α signaling is significantly upregulated in mast cells within the ccRCC microenvironment, shaping an immunosuppressive microenvironment through receptor-ligand interactions such as SPP1-CD44 and CLEC2C-KLRB1. We developed a mast cell-associated angiogenesis score that demonstrates satisfactory accuracy in assessing prognosis for ccRCC patients. Patients in the high-risk group exhibited activation of oncogenic signaling pathways including JAK-STAT3, accompanied by immunosuppressive status and elevated genomic instability. Furthermore, we identified the core oncogene TIMP1 and the protective gene EMCN. Conclusions: Mast cell-associated angiogenesis features aid in prognostic assessment for ccRCC patients, with TIMP1 and EMCN representing potential therapeutic targets.