Jaeyong Kim

MicroRNAs have added a new dimension to our understanding of tumorigenesis and associated processes like epithelial-to-mesenchymal transition (EMT). Here, we show that miR-375 is elevated in epithelial-like breast cancer cells, and ectopic miR-375 expression suppresses EMT in mesenchymal-like breast cancer cells. We identified short stature homeobox 2 (SHOX2) as a miR-375 target, and miR-375-mediated suppression in EMT was reversed by forced SHOX2 expression. Ectopic SHOX2 expression can induce

High abundance of c-Jun is detected in invasive breast cancer cells and aggressive breast tumor malignancies. Here, we demonstrate that a major cause of high c-Jun abundance in invasive breast cancer cells is prolonged c-Jun protein stability owing to poor poly-ubiquitination of c-Jun. Among the known c-Jun-targeting E3 ligases, we identified constitutive photomorphogenesis protein 1 (COP1) as an E3 ligase responsible for c-Jun degradation in less invasive breast cancer cells because depletion o

Cell-cell adhesion molecule cadherin-11(CDH11) is preferentially expressed in basal-like breast cancer cells and facilitates breast cancer cell migration by promoting small GTPase Rac activity. However, how the expression of CDH11 is regulated in breast cancer cells is not understood. Here, we show that CDH11 is transcriptionally controlled by homeobox C8 (HOXC8) in human breast cancer cells. HOXC8 serves as a CDH11-specific transcription factor and binds to the site of nucleotides -196 to -191

Abstract Triple-negative breast cancer (TNBC) cells are modulated in reaction to tumor-infiltrating lymphocytes. However, their specific responses to this immune pressure are unknown. In order to address this question, we first used mRNA sequencing to compare the immunophenotype of the TNBC cell line MDA-MB-231 and the luminal breast cancer cell line MCF7 after both were cocultured with activated human T cells. Despite similarities in the cytokine-induced immune signatures of the two cell lines,

Contemporary datasets can be immense and complex in nature. Thus, summarizing and extracting information frequently precedes any analysis. The summarizing techniques are many and varied and driven by underlying scientific questions of interest. One type of resulting datasets contains so-called histogram-valued observations. While such datasets are becoming more and more pervasive, methodologies to analyse them are still very inadequate. One area of interest falls under the rubric of cluster anal

In modern systems biology the modeling of longitudinal data, such as changes in mRNA concentrations, is often of interest. Fully parametric, ordinary differential equations (ODE)-based models are typically developed for the purpose, but their lack of fit in some examples indicates that more flexible Bayesian models may be beneficial, particularly when there are relatively few data points available. However, under such sparse data scenarios it is often difficult to identify the most suitable mode

One of the common issues in large dataset analyses is to detect and construct homogeneous groups of objects in those datasets. This is typically done by some form of clustering technique. In this study, we present a divisive hierarchical clustering method for two monothetic characteristics of histogram data. Unlike classical data points, a histogram has internal variation of itself as well as location information. However, to find the optimal bipartition, existing divisive monothetic clustering

Dynamic system consisting of ordinary differential equations (ODEs) is a well-known tool for describing dynamic nature of gene regulatory networks (GRNs), and the dynamic features of GRNs are usually captured through time-course gene expression data. Owing to high-throughput technologies, time-course gene expression data have complex structures such as heteroscedasticity, correlations between genes, and time dependence. Since gene experiments typically yield highly noisy data with small sample s