Abstract Introduction 20%) was observed in 41. 3% of metastatic samples and was strongly correlated with shorter metastasis-free survival (median 22. 6 vs. 46. 8 months, P 0. 001). Multivariate analysis identified high HIF-1α, VEGF, and PD-L1 expression, along with tumor stage ≥T3 and Fuhrman grade ≥3, as independent predictors of lung metastasis (P 0. 05). The final predictive model combining these five factors achieved an AUC of 0. 92, with sensitivity 88. 4%, specificity 83. 7%, and overall accuracy 85. 5% in distinguishing patients with high risk of pulmonary metastasis. Internal cross-validation confirmed model robustness (C-index = 0. 90). Conclusions By integrating immunohistochemical and clinicopathological features from 69 RCC patients with lung metastases, we identified key molecular alterations—particularly in the HIF-1α/VEGF/PD-L1 axis—that drive metastatic potential. The predictive model demonstrates strong discriminatory power for identifying patients at high risk for pulmonary metastasis, providing a valuable tool for individualized prognosis and early intervention strategies in RCC management. Citation Format: Chuandong Wang, Kan Gong. Comprehensive pathological analysis of 1, 369 renal cell carcinoma patients reveals novel biomarkers and establishes a predictive model for clinical outcomes abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Kidney Cancer Research: From Molecular Insights to Therapeutic Breakthroughs; 2026 Mar 13-16; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86 (5Suppl₂): Abstract nr B008.
Wang et al. (Fri,) studied this question.