An individualized protein-based prognostic model to stratify pediatric patients with papillary thyroid carcinoma

Abstract

Pediatric papillary thyroid carcinomas (PPTCs) are with high inter-tumor heterogeneity and currently lack widely adopted recurrence risk stratification criteria. Hence, we propose a machine learning-based objective method to individually predict their recurrence risk. We retrospectively collected and evaluated the clinical factors and proteomes of 83 pediatric benign (PB), 85 pediatric malignant (PM) and 66 adult malignant (AM) nodules, and quantified 10,426 proteins by mass spectrometry. We found 243 and 121 significantly dysregulated proteins from PM vs. PB and PM vs. AM, respectively. Function and pathway analyses showed the enhanced activation of the inflammatory and immune system in PM 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 PPTC patients into high- or low-recurrence risk groups, providing a reference for clinical decision-making and individualized treatment.