GBP1 as a machine learning-prioritized biomarker and therapeutic target for Epstein–Barr virus-induced clear cell renal cell carcinoma: multi-omics causal validation

BACKGROUND: This study aims to explore the oncogenic mechanisms of Epstein-Barr virus (EBV) in clear cell renal cell carcinoma (ccRCC) and to identify actionable biomarkers. METHODS: Mendelian randomization (MR) was employed to analyze the causal effects of EBV on ccRCC and to explore the mediating role of immune cells. Single-cell RNA sequencing (scRNA-seq) data of ccRCC were combined with EBV bulk-mRNA data to screen candidate genes for machine learning model construction. The SHapley Additive exPlanations (SHAP) framework was introduced to interpret feature contributions. High-confidence identification and validation of core targets were achieved through multi-omics MR, Summary-data-based MR (SMR), colocalization, drug prediction, and molecular docking. RESULTS: MR analysis demonstrated that regulatory T cells (Tregs) and B cells mediated EBV-specific antibody-driven ccRCC risk elevation. Through machine learning, we prioritized seven key genes (GBP1, IFI16, RECQL, GBP5, STK39, TAP2, and IL12RB1) from 24 EBV-ccRCC related Treg&B cell co-expressed genes. SHAP and multi-omics validation highlighted GBP1 as the core target (SHAP value = 0.191), with MR and colocalization (PP.H4 > 0.80) corroborating its causal involvement. Drug prediction revealed that finasteride exerts an inhibitory effect on GBP1, and molecular docking provided strong evidence of binding affinity (-7.6 kcal/mol). CONCLUSION: This work reveals a causal relationship between EBV infection and ccRCC pathogenesis, establishing GBP1 as a top-priority candidate molecule through a multi-level, multi-dimensional evidence framework. Drug prediction and molecular docking suggest finasteride as a potential inhibitor of GBP1, offering new strategies for the precise prevention and treatment of ccRCC.