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

Area of Science:

• Oncology
• Immunology
• Genomics

Background:

• Macrophages are crucial in cancer development, but their specific roles in renal cell carcinoma (RCC) are not fully understood.
• Characterizing macrophage functions is essential for understanding the RCC tumor microenvironment and developing effective treatments.

Purpose of the Study:

• To comprehensively analyze macrophage functions in RCC using integrated multi-omics data.
• To identify novel macrophage-associated gene signatures for prognostic prediction and therapeutic targeting in RCC.

Main Methods:

• Integrated analysis of bulk transcriptomics, single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics data.
• Development of a macrophage-associated gene classifier using SLC11A1 and IFI30.
• Validation of gene expression and functional impact in RCC cell lines and clinical samples.

Main Results:

• Identified SLC11A1 and IFI30 as significant macrophage-associated gene signatures in RCC.
• Developed a classifier based on these genes that accurately predicts RCC prognosis, drug sensitivity, and immune infiltration.
• Detailed characterization of macrophage metabolism, trajectory, and communication based on SLC11A1 and IFI30 expression.

Conclusions:

• Macrophages significantly influence the clinical and molecular diversity of RCC.
• The SLC11A1/IFI30 classifier provides valuable prognostic information and insights into therapeutic strategies for RCC.
• Understanding macrophage characteristics in RCC opens new avenues for targeted kidney cancer therapies.