Decoding macrophage-driven tumor heterogeneity in renal cell carcinoma using spatial and single-cell transcriptomics.

Macrophages play critical roles in tumorigenesis and progression; however, their functions in renal cell carcinoma (RCC) remain insufficiently characterized. In this study, we leveraged multiple perspectives, including bulk transcriptomics, single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics, to conduct an integrated analysis of various databases. We identified two macrophage-associated gene signatures, SLC11A1 and IFI30, and established a classifier based on these genes that correlates with different RCC prognoses and molecular patterns. This classifier significantly predicts adverse outcomes for RCC patients and demonstrates marked differences in drug sensitivity analysis and immune infiltration. Furthermore, we conducted an in-depth analysis at the scRNA-seq and spatial transcriptomics levels to characterize the pseudotime trajectory, metabolism, and communication of macrophages expressing SLC11A1 or IFI30. We also validated the expression and functional impact of these two genes in tumor cell lines through clinical samples and in vitro experiments. This study emphasizes the significant association between macrophages and the diverse clinical features and molecular landscapes of RCC. Evaluating the characteristics of macrophages in RCC enhances our understanding of the tumor microenvironment and paves new avenues for targeted therapeutic strategies for RCC.