肾细胞癌的多组学框架揭示肿瘤异质性并预测治疗靶点

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 UBE2C+ cancer cells linked to poor outcomes, metabolic reprogramming and low differentiation, and PLK1+ NK cells, plasma cells, and CDC20 + 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 VEGF , GDF , PTN and IL16 pathways in the remodeling of the tumor microenvironment. Gene regulatory network analysis highlighted RAD21 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 UBE2C+ cancer cells as progression-associated tumor cell population, and nominates critical regulators and compounds as therapeutic targets.