Abstract B008: Comprehensive pathological analysis of 1,369 renal cell carcinoma patients reveals novel biomarkers and establishes a predictive model for clinical outcomes

Introduction & Objectives Pulmonary metastasis is one of the most common and life-threatening events in renal cell carcinoma (RCC), yet the molecular mechanisms and predictive markers remain unclear. This study aimed to identify immunohistochemical biomarkers associated with lung metastasis and to construct a predictive model integrating pathological and clinical parameters. Materials & Methods We collected tumor specimens from 69 RCC patients with histologically confirmed pulmonary metastases. Paired primary renal lesions and lung metastatic tissues were analyzed by immunohistochemical (IHC) staining for a panel of markers including CAIX, VHL, HIF-1α, VEGF, Ki-67, and PD-L1. Staining intensity and proportion scores were quantified using digital pathology software. Clinicopathological data (age, sex, tumor size, stage, grade, and metastasis-free interval) were integrated with IHC data to establish a predictive model for lung metastasis using logistic regression and ROC curve analysis. Results Among the 69 patients, the mean age was 58.4 ± 9.7 years, with 47 males (68.1%) and 22 females (31.9%).Compared with primary lesions, lung metastatic sites showed significantly higher expression of HIF-1α (72.5% vs. 43.5%, P = 0.004), VEGF (68.1% vs. 40.6%, P = 0.006), and PD-L1 (31.9% vs. 14.5%, P = 0.018), while VHL expression was reduced (29.0% vs. 61.0%, P = 0.002). High Ki-67 index (>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(5_Suppl_2):Abstract nr B008.