Construction of a combined random forest and artificial neural network diagnosis model to screening potential biomarker for hepatoblastoma

Study combines random forest and artificial neural network models to identify five candidate biomarkers for hepatoblastoma diagnosis, with ARHGEF2 emerging as a key regulator of cell cycle pathways. Analysis reveals significant differences in tumor-infiltrating immune cells between HB and normal samples, suggesting memory B cells play an important role in disease pathogenesis.