Aberrant glycosylation in hematologic malignancies: mechanisms, immune evasion, and therapeutic targeting.
Hematologic malignancies are a group of malignant diseases originating from hematopoietic stem cells or the lymphatic system, mainly including leukemia, lymphoma, myeloma and so on.
APA
Lu X, Song Z, et al. (2026). Aberrant glycosylation in hematologic malignancies: mechanisms, immune evasion, and therapeutic targeting.. Blood cancer journal. https://doi.org/10.1038/s41408-026-01493-z
MLA
Lu X, et al.. "Aberrant glycosylation in hematologic malignancies: mechanisms, immune evasion, and therapeutic targeting.." Blood cancer journal, 2026.
PMID
41963305
Abstract
Hematologic malignancies are a group of malignant diseases originating from hematopoietic stem cells or the lymphatic system, mainly including leukemia, lymphoma, myeloma and so on. These diseases are characterized by their high heterogeneity, rapid disease progression and poor prognosis. Glycosylation is one of the most common post-translational modifications. In recent years, it has been found that abnormal glycosylation plays an important role in the genesis, development and treatment of hematologic malignancies. Aberrant glycosylation has been demonstrated to exert a significant influence on the progression of disease, impacting various biological processes including tumor cell signaling, the tumor microenvironment, cellular recognition, and immune evasion. This review synthesizes recent advances in how dysregulated glycosylation drives the biology of leukemia, lymphoma, myeloma, and other types of malignancies. Additionally, it discusses the potential value of glycosylation products that can be used as biomarkers for disease diagnosis and prognosis.
같은 제1저자의 인용 많은 논문 (5)
- Activated hepatic stellate cell-derived exosomal miR-23a-3p promotes hepatocellular carcinogenesis by regulating DUSP5/ERK signaling.
- Current landscape and challenges in autologous breast reconstruction across China: A nationwide questionnaire-based survey of 198 hospitals.
- Integration of histopathological characteristics by machine learning improves the prediction of neoadjuvant immunochemotherapy response in triple-negative breast cancer.
- Histological Categorization of Desmoplastic Reaction in Triple-Negative Breast Cancer: Its Relevance to Neoadjuvant Chemoimmunotherapy Response and Tumor Biology.
- Secreted SULF1 protein modulates CD8 + T cell exhaustion by promoting TAM polarization in gastric cancer.