PitNETs are among the most common intracranial tumors, consisting of various hormone-secreting cells with complex subtypes and clinical manifestations. Existing classifications can differentiate the three major lineages of pituitary tumors but often fail to capture their heterogeneity. This study explores alternative splicing features for precise diagnostic classification.

Pediatric AML is a highly aggressive malignancy, with around 30% of patients experiencing chemoresistance or relapses, often due to unknown mechanisms. While about 30% of adult AML patients have splicing factor (SF) mutations linked to splicing dysregulation, such mutations are rare in pediatric AML, leaving the role of splicing function unclear.

Systematic identification of alternative splicing events largely relies on computational methods, making it challenging to filter out true key abnormal splicing events with oncogenic potential from the vast number of computationally predicted events.