Prognostic gene screening and experimental validation in renal clear cell carcinoma based on spatial transcriptomics and single-cell sequencing

Background: Clear cell renal cell carcinoma (ccRCC) is characterized by high recurrence and metastatic potential, leading to poor clinical outcomes. There is a critical need to identify reliable prognostic biomarkers and therapeutic targets to improve patient stratification and personalized treatment. Methods: This study integrated single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data to identify prognostic genes and therapeutic targets. Prognostic modeling and validation were performed using The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. In addition, functional analyses were conducted to explore the biological roles of candidate genes. Results: Seven prognostic genes (CYFIP2, MPPED2, HHLA2, ADAM8, ATP1A1, ARC, and MXD3) were identified and used to construct a risk model that stratified patients into high- and low-risk groups. The high-risk group exhibited significantly poorer survival, a finding validated in both TCGA and ICGC datasets. A nomogram incorporating risk score and age improved survival prediction accuracy, with Area Under the Curve (AUC) values of 0.79, 0.75, and 0.78 at 1, 3, and 5 years, respectively. ATP1A1 was highly expressed in endothelial cells and was significantly associated with M1 macrophages; thus, it was selected as a potential therapeutic target. Functional analyses revealed its role in angiogenesis inhibition and M1 macrophage polarization. Discussion: The risk model and nomogram demonstrate strong prognostic value and may aid in clinical risk stratification for ccRCC. ATP1A1 emerges as a potential therapeutic target, with functional implications in angiogenesis and immune modulation. These findings highlight the clinical relevance of the identified gene signatures and support the development of personalized treatment strategies for ccRCC patients.