Transcriptomic profiles of invasive and non-invasive pituitary neuroendocrine tumors and normal pituitary glands

This study aims to uncover the biomarkers and molecular mechanisms that distinguish invasive from non-invasive pituitary neuroendocrine tumors (PitNETs) by integrating deep proteomic and transcriptomic profiling. Despite recent advances in tumor classification, invasive PitNETs remain clinically challenging due to a lack of effective diagnostic and therapeutic strategies. Using data-independent acquisition mass spectrometry (DIA-MS) on 53 PitNET tumors and 4 control pituitary glands, we identified distinct biomarker panels and signaling pathways associated with tumor invasiveness, including those involved in cell adhesion, neurogenesis, and hormone response. Machine learning algorithms were employed to classify tumor subtypes and prioritize invasion-related biomarkers with strong diagnostic potential. Further integration with drug-target databases revealed actionable proteins linked to FDA-approved drugs, offering new therapeutic directions. These findings provide mechanistic insight into PitNET progression and support the development of precision diagnostics and therapies for invasive PitNETs.