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Prognostic Value of M1-like and M2-like Tumor-Associated Macrophage Signatures and Their Ratio in Renal Cell Carcinoma

ORAL

Abstract

Renal cell carcinoma (RCC) represents 90% of adult renal cancers and features significant heterogeneity within its tumor microenvironment. We hypothesized that tumor-associated macrophages (TAMs) and their subtype ratios influence RCC progression and patient treatment responses. This study examined the prognostic implications of TAM signatures on RCC survival, using immunomics to analyze TAM-related changes in the tumor immune microenvironment (TIME). Through single-cell RNA sequencing data from RCC patients, we identified eight distinct TAM signatures. An AI machine learning model was developed to predict survival, tested against The Cancer Genome Atlas (TCGA) dataset, and validated across multiple RCC cohorts. Performance was assessed via Kaplan-Meier survival analyses, ROC curves, PCA, and t-SNE. Two M1-like TAM signatures, associated with positive patient survival outcomes and macrophage infiltration, were identified, as well as three M2-like TAM signatures. The 32-gene M1-like and 18-gene M2-like TAM risk models stratified patients into risk categories, with low-risk patients showing improved overall survival. Patients with a high M1-like/M2-like TAM ratio achieved a better prognosis and showed enrichment of anti-tumor-related immune cells and active pathways. These findings highlight TAM interactions within TIME, offering prognostic markers and potential therapeutic targets for RCC treatment.

Presenters

  • Chongming Jiang

    Terasaki Institute for Biomedical Innovation

Authors

  • Chongming Jiang

    Terasaki Institute for Biomedical Innovation

  • Youngsoo Han

    University of California, Los Angeles

  • Aidan Shen

    Terasaki Institute for Biomedical Innovation

  • Olamide Adefioye

    Terasaki Institute for Biomedical Innovation

  • Yangzhi Zhu

    Terasaki Institute for Biomedical Innovation

  • Xiuying Li

    Terasaki Institute for Biomedical Innovation

  • Zhaohui Wang

    Terasaki Institute for Biomedical Innovation