Data from Biomarker Identification and Risk Prediction Model Development for Differentiated Thyroid Carcinoma Lung Metastasis Based on Primary Lesion Proteomics

• DOI: 10.1158/1078-0432.c.7348775
• Publication Date: 2024-07-15T07:23:24Z
• Authors: Xiaoqi Peng; Hongbo Zhao; Lijuan Ye; Fei Hou; Zihan Yi; Yanxin Ren; Lin Lu; Fukun Chen; Juan Lv; Yinghui Wang; Haolin Cai; Xihua Zheng; Qing Yang; Ting Chen
• Keywords: Nomogram

<div>AbstractPurpose:<p>The rising global high incidence of differentiated thyroid carcinoma (DTC) has led to a significant increase in patients presenting with lung metastasis DTC (LMDTC). This population poses challenge clinical practice, necessitating the urgent development effective risk stratification methods and predictive tools for metastasis.</p>Experimental Design:<p>Through proteomic analysis large samples primary lesion dual validation employing parallel reaction monitoring IHC, we identified eight hub proteins as potential biomarkers. By expanding sample size conducting statistical on features protein expression, constructed three prediction models.</p>Results:<p>This study proteins—SUCLG1/2, DLAT, IDH3B, ACSF2, ACO2, CYCS, VDAC2—as biomarkers predicting LMDTC risk. We developed internally validated models incorporating both characteristics expression. Our findings demonstrated that combined model exhibited optimal performance, highest discrimination (AUC: 0.986) calibration (Brier score: 0.043). Application within specific threshold (0–0.97) yielded maximal benefit. Finally, nomogram based model.</p>Conclusions:<p>As research, identification through proteomics integrating offer valuable insights establishing personalized treatment strategies.</p></div>