-
[show abstract]
[hide abstract]
ABSTRACT: According to the cancer stem cell (CSC) model, higher CD133 expression in tumor tissue is associated with metastasis and poor prognosis in colon cancer. As such, the CD133-positive (CD133(+)) subpopulation of cancer cells is believed to play a central role in tumor development and metastatic progression. Although CD133(+) cells are believed to display more CSC-like behavior and be solely responsible for tumor colonization, recent research indicates that CD133(-) cells from metastatic colon tumors not only also possess colonization capacity but also promote the growth of larger tumors in a mouse model than CD133(+) cells, suggesting that an alternative mechanism of metastasis exists. This study investigated this possibility by examining the cell viability, tumorigenicity, and proliferation and growth capacity of the CD133(+) and CD133(-) subpopulations of the SW620 cell line, a human metastatic colon cancer cell line, in both an in vitro cell model and an in vivo mouse model. While both SW620 (CD133-) and SW620(CD133+) cells were found to engage in bidirectional cell-type switching in reaction to exposure to environmental stressors, including hypoxia, a cell adhesion-free environment, and extracellular matrix stimulation, both in vitro and in vivo, CD133(-) cells were found to have a growth advantage during early colonization due to their greater resistance to proliferation inhibition. Based on these findings, a hypothetical model in which colon cancer cells engage in cell-type switching in reaction to exposure to environmental stressors is proposed. Such switching may provide a survival advantage during early colonization, as well as that explain previous conflicting observations.
PLoS ONE 01/2013; 8(4):e61133. · 4.09 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: By regulating the amount of protein receptors on the cell membrane and the metabolisms of receptor-bound ligands, endocytosis represents one of the fundamental biological activities that regulate how cells respond to the environment. We report here that a Fab1-YotB-Vac1p-EEA1 (FYVE) domain-containing lipid associated protein, called Phafin2, is preferentially expressed in the human hepatocellular carcinoma (HCC) and is involved in the biogenesis of endosomes. Over-expression of Phafin2 or its FYVE domain results in the formation of enlarged endosomes that are still functional for endocytosis; the biogenesis of such abnormal organelles is mediated by phosphoinositide 3-kinases (PI3K) and Rab5 signaling. Using fluorescence resonance energy transfer measured by fluorescence lifetime imaging microscopy (FLIM-FRET), we further demonstrate in live cells that Phafin2 can directly activate Rab5. By modulating the receptor internalization/recycling and Rab5 activation, Phafin2 affects the density of membranous insulin receptors, and regulates the transcriptional activity of AP-1 that is downstream of the insulin signaling pathway. These results provide a vivid example that an endosome modulator, such as Phafin2, may control the cells' responses to the extracellular cues.
Biochemical and Biophysical Research Communications 12/2009; 391(1):1043-8. · 2.48 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Analyzing gene expression data by assessing the significance of pre-defined gene sets, rather than individual genes, has become a main approach in microarray data analysis and this has promisingly derive new biological interpretations of microarray data. However, the detection power of conventional gene list or gene set-based approaches is limited on highly heterogeneous samples, such as tumors.
We developed a novel method, the regulatory event-based Gene Set Analysis (eGSA), which considers not only the consistently changed genes but also every gene regulation (event) of each sample to overcome the detection limit. In comparison with conventional methods, eGSA can detect functional changes in heterogeneous samples more precisely and robustly. Furthermore, by utilizing eGSA, we successfully revealed novel functional characteristics and potential mechanisms of very early hepatocellular carcinoma (HCC).
Our study creates a novel scheme to directly target the major cellular functional changes in heterogeneous samples. All potential regulatory routines of a functional change can be further analyzed by the regulatory event frequency. We also provide a case study on early HCCs and reveal a novel insight at the initial stage of hepatocarcinogenesis. eGSA therefore accelerates and refines the interpretation of heterogeneous genomic data sets in the absence of gene-phenotype correlations.
BMC Genomics 02/2009; 10:26. · 4.07 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The identification of specific gene expression signature for distinguishing sample groups is a dominant field in cancer research. Although a number of tools have been developed to identify optimal gene expression signatures, the number of signature genes obtained is often overly large to be applied clinically. Furthermore, experimental verification is sometimes limited by the availability of wet-lab materials such as antibodies and reagents. A tool to evaluate the discrimination power of candidate genes is therefore in high demand by clinical researchers.
Signature Evaluation Tool (SET) is a Java-based tool adopting the Golub's weighted voting algorithm as well as incorporating the visual presentation of prediction strength for each array sample. SET provides a flexible and easy-to-follow platform to evaluate the discrimination power of a gene signature. Here, we demonstrated the application of SET for several purposes: (1) for signatures consisting of a large number of genes, SET offers the ability to rapidly narrow down the number of genes; (2) for a given signature (from third party analyses or user-defined), SET can re-evaluate and re-adjust its discrimination power by selecting/de-selecting genes repeatedly; (3) for multiple microarray datasets, SET can evaluate the classification capability of a signature among datasets; and (4) by providing a module to visualize the prediction strength for each sample, SET allows users to re-evaluate the discrimination power on mis-grouped or less-certain samples. Information obtained from the above applications could be useful in prognostic analyses or clinical management decisions.
Here we present SET to evaluate and visualize the sample-discrimination ability of a given gene expression signature. This tool provides a filtration function for signature identification and lies between clinical analyses and class prediction (or feature selection) tools. The simplicity, flexibility and brevity of SET could make it an invaluable tool for marker identification in clinical research.
BMC Bioinformatics 02/2008; 9:58. · 2.75 Impact Factor
