A Multi-Omics Framework Reveals Tumor Heterogeneity and Predicts Therapeutic Targets in Renal Cell Carcinoma

Tumor cell heterogeneity and multicellular interactions critically influence drug resistance, recurrence, and prognosis. Here, CPcellsubpopulation, a computational framework integrating scRNA-seq, bulk RNA-seq, and clinical data was developed to identify cancer progression-associated cell subpopulations. Then, the integrated analyses of scRNA-seq and spatial transcriptomics were performed to predict potential interactions, identify critical transcription factors, and predict candidate anticancer drugs. Across nine cancers, we detected cancer progression-associated cell subpopulations significantly linked to prognosis, with consistent patterns across cancer types. In renal cell carcinoma (RCC), we identified conserved metabolic^high<i>UBE2C+</i> cancer cells linked to poor outcomes, metabolic reprogramming and low differentiation, and <i>PLK1+</i> NK cells, plasma cells, and <i>CDC20</i>+ macrophages associated with advanced stages and unfavorable prognosis. Spatial mapping revealed spatial association of RCC progression-associated cancer and immune cell subpopulations, suggesting the potential role of the <i>VEGF</i>, <i>GDF</i>, <i>PTN</i> and <i>IL16</i> pathways in the remodeling of the tumor microenvironment. Gene regulatory network analysis highlighted <i>RAD21</i> as a key regulator linking metabolism and therapy resistance. This study provides a systematic pipeline to delineate cancer progression-associated cell subpopulations, uncovers metabolic^high<i>UBE2C+</i> cancer cells as progression-associated tumor cell population, and nominates critical regulators and compounds as therapeutic targets.