Wanwan Yi, Yida Huang, Jiayou Feng, Xiaowen Zhang, Yuheng Zhu, Kun Qian, Chang Lei, Zhongwei Lv, Chengzhong Yu
Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, and early diagnosis is critical for timely intervention and improved therapeutic outcomes. However, convenient and accurate PTC diagnosis is challenging due to the lack of reliable biomarkers. Herein, mesoporous carbon hollow spheres (MCHS) modified with gold nanoparticles and lauric acid (MCHS-Au-LA) are developed as a novel substrate to enable carbon nanostructure initiator mass spectrometry (MS) based sensitive metabolic profiling for PTC diagnosis. The designed carbon nanostructure incorporated with gold nanoparticles enhances both analyte ionization and desorption, thus significantly improving metabolite detection sensitivity. This platform has been applied to generate serum metabolic fingerprints (SMFs) from 131 PTC patients, 79 individuals with benign thyroid nodules (BTN), and 71 healthy controls (HC). Machine learning analysis of SMFs has revealed strong diagnostic performance with area-under-the-curve (AUC) values of 0.896-0.937. Moreover, a seven-metabolite biomarker panel is identified for differentiating PTC from controls, providing a reliable approach for early PTC diagnosis and outperforming conventional serum indicators such as thyroglobulin and thyroid-stimulating hormone (TSH). This study provides a convenient and precise diagnostic tool that can be applied in other clinical applications.