Assessment of Papillary Thyroid Carcinoma by Profiling Multiple Matrix Metalloproteinase Activities Using a Machine Learning-Assisted Peptide Microarray Sensing Platform

Although significant progress has been achieved in diagnosing papillary thyroid carcinoma (PTC), it remains a challenge to diagnose thyroid nodules (TNs) with ambiguous ultrasound features. Matrix metalloproteinases (MMPs), key mediators of tumor invasion and metastasis, are highly promising biomarkers for PTC. In this manuscript, a machine learning (ML)-assisted peptide microarray sensing platform (MLPM) is developed for accurate diagnosis of PTC through the combination of the high-throughput ability of peptide microarray and the powerful predictive performance of the gradient boosting (GB) algorithm. The peptide microarray enables the simultaneous detection of seven MMP activities with low limits of detection (LODs) (down to the pg mL –1 level) and wide dynamic ranges (more than 3 orders of magnitude) across diverse sample types, including cells, tissues, and plasma. The GB model with MMP activity profiles exhibits exceptional diagnostic performance, achieving area under the receiver operating characteristic curve (AUC) values of 0.99 for discriminating benign from malignant nodules and 0.97 for predicting lymph node metastasis status in PTC. Crucially, in an independent blind validation, the model maintains high accuracy of 96.0% and 94.1% for these two critical clinical tasks, confirming its robustness and generalizability.