Pediatric papillary thyroid carcinoma

Pediatric patients with papillary thyroid carcinoma currently lack widely adopted risk stratification criteria, which leads to a lack of individualized treatment and prognosis assessments. In this retrospective study, 83 pediatric benign (PB), 85 pediatric malignant (PM) and 66 adult malignant (AM) thyroid nodules were evaluated, and 10,426 proteins were quantified by mass spectrometry. We found 243 significantly dysregulated proteins from the PM vs. PB and 122 from PM vs. AM. Function and pathway analyses showed the enhanced activation of the inflammatory and immune system in pediatric malignant patients compared with the others. Nineteen proteins were selected to predict recurrence using a machine learning model with an accuracy of 88.24%. Our study generated a protein-based personalized prognostic prediction model that can stratify pediatric patients with papillary thyroid carcinoma into high or low recurrence risk groups, providing a reference for clinical decision-making and individualized treatment.