Proteomic Characterization of Papillary Thyroid Carcinoma with Distant Metastasis

Introduction

Papillary thyroid carcinoma (PTC) generally has a favorable prognosis, but distant metastases can occur in approximately 6% of patients. The disease-specific survival rates for PTC patients with distant metastases are only 35% at five years and 25% at ten years. Identifying biomarkers and understanding the mechanisms underlying distant metastasis in PTC are particularly important. In our study, we conducted analyses of exome and proteome in PTC patients with and without distant metastases to identify key biomarkers and pathways that promote tumor invasion and metastasis.

Methods

We retrospectively collected FFPE tissues and clinical data from 34 PTC patients with distant metastases, treated at the First Hospital of China Medical University between 2011 and 2016. Additionally, we collected data from 35 PTC patients matched by clinical and pathological characteristics. Exome sequencing was performed on 52 of these patients, comparing gene expression profiles with those from the TCGA database to observe changes specific to Asian patients. Proteomic data from all samples were collected and analyzed using data-independent acquisition (DIA) mode on the Thermo Scientific Orbitrap Exploris 480.

Preliminary Data

Exome sequencing did not identify significant differences between the two groups genes. Based on proteomics, a total of 52,169 precursors, 44,838 peptides, and 7,551 proteins were identified. Using limma and Welch's t-test, we found 207 consistently differentially expressed proteins (DEPs) in PTC patients with distant metastases (40 upregulated and 167 downregulated, log Fold Change > 0.5, adjusted P < 0.05). Functional and pathway enrichment analysis revealed significant clustering in mitochondrial dysfunction. By focusing on proteins localized to the mitochondria and with known drug targets, we identified 33 significantly different proteins, among which only LRP2 was upregulated. We further validated LRP2 expression in tissues through immunohistochemical staining across all samples. Next, data were randomly divided into a training set and a test set (2:1), and using the top ten proteins ranked by the Gini coefficient from the DEPs, we built a random forest model for classification. The model achieved an accuracy of 88.1% for identifying patients with distant metastasis.

Novel Aspect

By leveraging exome sequencing and proteomic analysis, this study offers unique insights into the molecular mechanisms of metastatic PTC in Asian patients. It highlights mitochondrial dysfunction as a crucial factor and identifies LRP2 as a potential biomarker. Additionally, a highly accurate random forest model based on differentially expressed proteins predicts distant metastasis in PTC, paving the way for personalized treatment strategies.