Abstract
INTRODUCTION: Tumor drug resistance, particularly immune-mediated resistance, poses a significant challenge in cancer therapy, especially in clear-cell renal cell carcinoma (ccRCC), the most common and aggressive subtype of renal cancer. Tumor-infiltrating immune cells (TIICs) within the tumor microenvironment (TME) play pivotal roles in tumor progression, immune evasion, and therapy resistance. This study explores the prognostic and therapeutic implications of TIICs in ccRCC, aiming to uncover molecular underpinnings and potential strategies to counter drug resistance. METHODS: Integrative analyses of transcriptomic and single-cell RNA sequencing data from multiple cohorts were employed to characterize immune and metabolic landscapes in ccRCC. Machine learning algorithms were utilized to identify key TIIC-related RNAs (TIIC-RNAs) associated with prognosis and therapeutic response. The constructed prognostic model was validated across independent datasets. Additionally, the correlation between TIIC score and immune checkpoint expression, metabolic alterations, and genomic mutations was investigated. RESULTS: The TIIC-based model demonstrated superior predictive performance for patient outcomes compared to 53 published models. High TIIC feature score correlated with increased immune infiltration, inflammatory responses, and poor survival. In contrast, low score was associated with enhanced responses to immune checkpoint inhibitors. Significant metabolic reprogramming, including lipid and sulfur metabolism, and distinct genomic alterations, such as BAP1 mutations, were linked to TIIC score. CONCLUSION: Our findings underscore the pivotal role of TIIC-RNAs in mediating drug resistance in ccRCC. The prognostic model provides valuable insights into immune and metabolic mechanisms underlying therapy resistance, offering a foundation for developing precision therapeutics targeting the TME.