1280-Tumor Heterogeneity in Hepatocellular Carcinoma

This research utilizes single-cell RNA sequencing and machine learning to map the complex cellular landscape of hepatocellular carcinoma (HCC). The authors identified five distinct malignant cell subpopulations, discovering that specific clusters associated with advanced-stage tumors drive progression through unique signaling pathways and metabolic shifts. A central finding highlights PGAM2 as a pivotal transcriptional regulator linked to sialylation, a chemical modification that helps cancer cells evade the immune system. By integrating these molecular insights, the study established a prognostic model that accurately predicts patient survival outcomes and therapeutic responses. Furthermore, functional experiments validated AGRN as a key driver of tumor proliferation and invasion. Collectively, the findings provide a robust framework for improving risk stratification and identifying new therapeutic targets for liver cancer.References:Tang K, Han L, Li J, et al. Machine learning-driven comprehensive profiling of tumor heterogeneity and sialylation in hepatocellular carcinoma[J]. NPJ Precision Oncology, 2025.前往小宇宙评论区与主播互动