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Metastatic tumor cells - DOAJ

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Last Updated: 23 October 2021

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Intercellular transfer of mitochondrial DNA carrying metastasis-enhancing pathogenic mutations from high- to low-metastatic tumor cells and stromal cells via extracellular vesicles

Abstract Background Mitochondrial DNA carrying particular pathogenic mutations or solitary nucleotide versions boosts the invasion and transition of tumor cells, and several of these mutations are homoplasmic in tumor cells and in tumor cells. It stays uncertain whether metastasis-enhancing pathogenic mutant mtDNA in tumor cells is intercellularly moved in between tumor cells and stromal cells. In this study, we examined whether mtDNA with the NADH dehydrogenase subunit 6 G13997A pathogenic mutation in extremely metastatic cells can be horizontally moved to low-metastatic cells and stromal cells in the tumor microenvironment. Outcomes When MitoTracker Deep Red-labeled high-metastatic Lewis lung carcinoma A11 cells carrying the ND6 G13997A mtDNA mutation were cocultured with CellLight mitochondria-GFP-labeled low-metastatic P29 cells nurturing wild-type mtDNA, bidirectional transfer of red- and green-colored vesicles, most likely mitochondria-related EVs, was observed in a time-dependent fashion. Intercellular transfer of mitochondria-related EVs happened between A11 cells and α-smooth muscular tissue actin -positive cancer-associated fibroblasts, macrophages and cytotoxic T cells. In syngeneic mouse subcutaneous growths formed by a mix of A11 and P29 cells, the mitochondria-related EVs released from A11 cells reached distantly positioned P29 cells and CAFs. Final thoughts These results suggest that metastasis-enhancing pathogenic mtDNA stemmed from metastatic tumor cells is transferred to low-metastatic tumor cells and stromal cells by means of S-EVs in vitro and in the tumor microenvironment, inferring a novel mechanism of enhancement of metastatic potential during tumor progression.

Source link: https://doi.org/10.1186/s12860-021-00391-5


Detection of Metastatic Tumor Cells in the Bone Marrow Aspirate Smears by Artificial Intelligence (AI)-Based Morphogo System

IntroductionMetastatic cancers of bone marrow are identified as lumps of non-hematopoietic origin infecting the bone marrow through blood or lymphatic circulation. Nonetheless, the recognition of metastatic cancer cells on bone marrow ambition smears is practically tough by conventional tiny screening. ObjectiveThe purpose of this study is to create an automatic recognition system using deep learning formulas applied to bone marrow cells image analysis. High resolution digital bone marrow aspirate smear images were created and immediately examined by Morphogo AI based system. The location under the contour for Morphogo to identify the cancer cells cell collections was 0. 865. ConclusionIn patients with clinical background of cancer, the Morphogo system was validated as a helpful testing device in the recognition of metastatic cancer cells in the bone marrow aspirate smears.

Source link: https://doi.org/10.3389/fonc.2021.742395

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions