Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for thyroid cancer

Abstract Background: Thyroid cancer (THCA) exhibits high molecular heterogeneity, posing challenges for precise prognosis and personalized therapy. Most existing models rely on single-omics data and limited algorithms, reducing robustness and clinical value. Methods: We integrated five omics layers from THCA patients using eleven clustering algorithms to identify molecular subtypes. Based on stable prognosis-related genes (SPRGs), we applied 99 combinations of ten machine learning methods to construct a robust prognostic model—Consensus Machine Learning-Driven Signature (CMLS). The model was validated across multiple