Keyao Chen, Huabo Wang, Bingwei Ma, Jessica Knapp, Colin Henchy, Jie Lu, Taylor Stevens, Sarangarajan Ranganathan, Edward V Prochownik
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引用次数: 0
Abstract
Hepatoblastoma (HB), the most common pediatric liver cancer, often dysregulates the Wnt/β-catenin, Hippo and/or NFE2L2/NRF2 pathways. Pairwise combinations of oncogenically active forms of the terminal transcription factors of these pathways, namely β-catenin (B), YAP (Y) and NRF2 (N) generate HBs in mice, with the triple combination (B+Y+N) being particularly potent. Each tumor group alters the expression of thousands of B-,Y- and N-driven unique and common target genes. Identifying those most responsible for transformation is thus of paramount importance as it might reveal new mechanistic insights and therapeutic opportunities. Transcriptional profiling of >60 murine HBs has previously revealed a common set of 22 "BYN genes" that are similarly deregulated in all cases. Most are associated with multiple "Cancer Hallmarks" and their expression levels often correlate with survival in human HBs, hepatocellular carcinomas and other cancers. Among the most down-regulated of these is Gas1, which encodes a glycosylphosphatidylinositol (GPI)-linked outer membrane protein. We show here that restoring Gas1 expression impairs B+Y+N-driven HB tumor growth in vivo and in HB-derived immortalized BY and BYN cell lines in vitro in a manner than requires membrane anchoring of the protein via its GPI moiety. Our findings implicate Gas1 as a proximal mediator of HB pathogenesis and validate the BYN gene set as deserving of closer additional scrutiny in future studies.
期刊介绍:
The American Journal of Pathology, official journal of the American Society for Investigative Pathology, published by Elsevier, Inc., seeks high-quality original research reports, reviews, and commentaries related to the molecular and cellular basis of disease. The editors will consider basic, translational, and clinical investigations that directly address mechanisms of pathogenesis or provide a foundation for future mechanistic inquiries. Examples of such foundational investigations include data mining, identification of biomarkers, molecular pathology, and discovery research. Foundational studies that incorporate deep learning and artificial intelligence are also welcome. High priority is given to studies of human disease and relevant experimental models using molecular, cellular, and organismal approaches.