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The ubiquitous protein kinase CK2 has been demonstrated to be overexpressed in a number of human tumours. This enzyme is composed of two catalytic α or α' subunits and a dimer of β regulatory subunits whose expression levels are probably implicated in CK2 regulation. Several recent papers reported that unbalanced expression of CK2 subunits is sufficient to drive epithelial to mesenchymal transition, a process involved in cancer invasion and metastasis. Herein, through transcriptomic and miRNA analysis together with comparison of cellular properties between wild type and CKβ-knock-down MCF10A cells, we show that down-regulation of CKβ subunit in mammary epithelial cells induces the acquisition of stem cell-like properties associated with perturbed polarity, CD44high/CD24low antigenic phenotype and the ability to grow under anchorage-independent conditions. These data demonstrate that a CK2β level establishes a critical cell fate threshold in the control of epithelial cell plasticity. Thus, this regulatory subunit functions as a nodal protein to maintain an epithelial phenotype and its depletion drives breast cell stemness. [ABSTRACT FROM AUTHOR]

Simple Summary: Osteosarcoma is the most common primary bone tumor and has a poor prognosis. Therefore, it is important to understand the mechanism of the development of osteosarcoma to overcome therapy resistance. Several mathematical models have been developed to study the initiation and progression of many cancer types. However, there are currently no mathematical models for the progression of osteosarcoma, to the best of our knowledge. In this work, we develop a data-driven mathematical model to analyze the impact of the immune cell interactions on the growth of osteosarcoma tumors that have distinct immune patterns. Our model provides a foundation for investigating the effect of various treatments on the dynamics of key players in the primary tumor, including immune cells and cytokines, and ultimately the whole tumor. As the immune system has a significant role in tumor progression, in this paper, we develop a data-driven mathematical model to study the interactions between immune cells and the osteosarcoma microenvironment. Osteosarcoma tumors are divided into three clusters based on their relative abundance of immune cells as estimated from their gene expression profiles. We then analyze the tumor progression and effects of the immune system on cancer growth in each cluster. Cluster 3, which had approximately the same number of naive and M2 macrophages, had the slowest tumor growth, and cluster 2, with the highest population of naive macrophages, had the highest cancer population at the steady states. We also found that the fastest growth of cancer occurred when the anti-tumor immune cells and cytokines, including dendritic cells, helper T cells, cytotoxic cells, and IFN- γ , switched from increasing to decreasing, while the dynamics of regulatory T cells switched from decreasing to increasing. Importantly, the most impactful immune parameters on the number of cancer and total cells were the activation and decay rates of the macrophages and regulatory T cells for all clusters. This work presents the first osteosarcoma progression model, which can be later extended to investigate the effectiveness of various osteosarcoma treatments. [ABSTRACT FROM AUTHOR]