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Using single-cell RNA sequencing on 50 thyroid cancer samples, we built a comprehensive atlas of 400,000 cells. The study identifies a hub microenvironment formed by APOE+ macrophages, EMT-like cancer-associated fibroblasts, and RBP7+ endothelial cells that plays a key role in tumor progression.
To address the shortage of high-quality molecular data, we designed a graph neural network transfer-learning framework pretrained on large, lower-fidelity datasets. Across 28 million protein-ligand interaction records, it improved sparse-task performance eightfold while reducing high-fidelity training data needs by an order of magnitude.