Anti-metastatic Immunotherapy of Advanced Tumors Based on EDTA Intercalated Zinc-aluminum Layered Double Hydroxide
Source abstract
Abstract
Systemic metastasis of cancer cells is currently the main cause of death for patients with advanced cancer.Due to the rapid proliferation of tumor cells and abnormal deposition of extracellular matrix, the large-volume tumor tissue in advanced cancer is dense and stiff, which brings great difficulties to the treatment of advanced solid tumors: conventional drugs having difficulty in penetrating and immune cells facing challenges to infiltrate into their interior.Meanwhile, tumor cells on hard matrices have stronger invasive ability, which is prone to cause systemic metastasis ofJournal of Inorganic Materials * tumors.To solve this problem, this study prepared ethylenediaminetetraacetic acid (EDTA) intercalated zinc-aluminum layered double hydroxide nanomaterials (EDTA/LDH).Based on two parallel Ca 2+ deprivation mechanisms, the antimetastasis immunotherapy of EDTA/LDH material system for advanced large-volume solid tumors was studied.In the slightly acidic tumor microenvironment, the material system adheres to the tumor cell membrane through electrostatic force, releases EDTA to chelate Ca 2+ in cell adhesion proteins, cuts off part of the cell connections, reduces the stiffness of large tumors, and promotes the infiltration of immune cells into the tumor tissue.In addition, the material system is phagocytosed by macrophages as a "foreign body", causing calcium store-operated calcium influx, activating the antitumor immune effect of macrophages, and inhibiting the tumor-promoting invasion of polymorphonuclear myeloidderived suppressor cells (PMN-MDSCs) and regulatory T cells (Tregs).This study will provide reference ideas and methods for the anti-metastasis treatment of advanced malignant solid tumors.
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Research context
OpenAlex topics: Nanoplatforms for cancer theranostics · Immune cells in cancer · Peptidase Inhibition and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (28)
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