Renal cell carcinoma (RCC) accounts for 90% of adult renal cancer cases and is characterized by significant heterogeneity within its tumor microenvironment. This study tests the hypothesis that tumor-associated macrophages (TAMs) influence RCC progression and patient response to treatment by investigating the prognostic implications of TAM signatures. Utilizing independent single-cell RNA sequencing data from RCC patients, we developed eight distinct TAM signatures reflective of TAM presence. A LASSO Cox regression model was constructed to predict survival outcomes, evaluated using the TCGA dataset, and independently validated across multiple RCC cohorts. Model performance was assessed through Kaplan-Meier survival plots, receiver operating characteristic (ROC) curves, principal component analysis. Survival analysis demonstrated that specific TAM signature gene expressions serve as significant prognostic markers, identifying TAM signatures positively correlated with patient survival and macrophage infiltration. A 27-gene TAM risk model was established, successfully stratifying patients into risk categories, with low-risk patients showing improved overall survival. These findings provide insights into the role of TAMs in modulating the RCC tumor immune microenvironment and their impact on patient prognosis, suggesting that TAM-based signatures may serve as useful prognostic markers and potential targets to enhance RCC treatment strategies.

Renal cell carcinoma (RCC) accounts for 90% of adult renal cancer cases and is characterized by significant heterogeneity within its tumor microenvironment. This study tests the hypothesis that tumor-associated macrophages (TAMs) influence RCC progression and patient response to treatment by investigating the prognostic implications of TAM signatures. Utilizing independent single-cell RNA sequencing data from RCC patients, we developed eight distinct TAM signatures reflective of TAM presence. A LASSO Cox regression model was constructed to predict survival outcomes, evaluated using the TCGA dataset, and validated across independent RCC cohorts. Model performance was assessed through Kaplan–Meier survival plots, receiver operating characteristic (ROC) curves, and principal component analysis. Survival analysis demonstrated that specific TAM signature gene expressions serve as significant prognostic markers, identifying TAM signatures positively correlated with patient survival and macrophage infiltration. A 27-gene TAM risk model was established, successfully stratifying patients into risk categories, with low-risk patients showing improved overall survival. These findings provide insights into the role of TAMs in modulating the RCC tumor immune microenvironment and their impact on patient prognosis, suggesting that TAM-based signatures may serve as useful prognostic markers and potential targets to enhance RCC treatment strategies.