Clinically Interpretable Extracellular Vesicle Gene Model for Non-Invasive Liver Cancer Diagnosis

Hepatocellular carcinoma (HCC) is a major cause of cancer death worldwide, underscoring the need for early non-invasive diagnostics. This study developed an interpretable model using extracellular vesicle (EV)-derived RNA signatures from exoRBase 2.0. Six machine learning algorithms were compared, with the Deep Neural Network (DNN) achieving superior performance (AUC = 0.8877). Ten diagnostic mRNAs (MTRNR2L8, HBB, PF4, FTL, MTRNR2L12, TMSB4X, PPBP, OST4, ACTB, and S100A9) were identified, among which MTRNR2L8 was the most significant predictor. SHAP and Kolmogorov-Arnold Networks (KAN) revealed nonlinear feature–outcome relationships and potential biomarkers. An online platform was created for real-time clinical use. This tool offers a robust, interpretable, and non-invasive method for early HCC detection, potentially improving diagnostic timeliness and decision-making. Further validation in prospective cohorts is warranted.