Revealing the Molecular Mechanism of Gastric Cancer Marker Annexin A4 in Cancer Cell Proliferation Using Exon Arrays

<div><p>Gastric cancer is a malignant disease that arises from the gastric epithelium. A potential biomarker for gastric cancer is the protein annexin A4 (ANXA4), an intracellular Ca²⁺ sensor. ANXA4 is primarily found in epithelial cells, and is known to be involved in various biological processes, including apoptosis, cell cycling and anticoagulation. In respect to cancer, ANXA4-overexpression has been observed in cancers of various origins, including gastric tumors associated with <em>Helicobacter pylori</em> infection. <em>H. pylori</em> induces ANXA4 expression and intracellular [Ca²⁺]_i elevation, and is an important risk factor for carcinogenesis that results in gastric cancer. Despite this correlation, the role of ANXA4 in the progression of gastric tumors remains unclear. In this study, we have investigated whether ANXA4 can mediate the rate of cell growth and whether ANXA4 downstream signals are involved in tumorigenesis. After observing the rate of cell growth in real-time, we determined that ANXA4 promotes cell proliferation. The transcription gene profile of ANXA4-overexpressing cells was measured and analyzed by human exon arrays. From this transcriptional gene data, we show that overexpression of ANXA4 regulates genes that are known to be related to cancer, for example the activation of hyaluronan mediated motility receptor (RHAMM), AKT, and cyclin-dependent kinase 1 (CDK1) as well as the suppression of p21. The regulation of these genes further induces cancer cell proliferation. We also found Ca²⁺ could regulate the transmission of downstream signals by ANXA4. We suggest that ANXA4 triggers a signaling cascade, leading to increased epithelial cell proliferation, ultimately promoting carcinogenesis. These results might therefore provide a new insight for gastric cancer therapy, specifically through the modification of ANXA4 activity.</p> </div

Ca²⁺ mediates the expression of RHAMM, phospho-AKT and p21.

<p>(A) Cells were treated with ionomycin to increase intracellular Ca²⁺ levels, and the expression levels of ANXA4, LAMP2, RHAMM, phospho-AKT (Ser473), p21, and phospho-CDK1 (Thr161) were showed by immunoblotting. (B) The histogram shows the related levels of (A). The relative expressions of RHAMM (<i>P</i><0.01), phospho-AKT (Ser473) (<i>P</i><0.05) and p21 (<i>P</i><0.01) were significantly different. Data are taken from three independent experiments (mean ± SD). *<i>P</i><0.05, **<i>P</i><0.01 vs. control treatment values.</p

MeInfoText: associated gene methylation and cancer information from text mining-0

<p><b>Copyright information:</b></p><p>Taken from "MeInfoText: associated gene methylation and cancer information from text mining"</p><p>http://www.biomedcentral.com/1471-2105/9/22</p><p>BMC Bioinformatics 2008;9():22-22.</p><p>Published online 14 Jan 2008</p><p>PMCID:PMC2258285.</p><p></p>gories, (1) search for associations among gene, methylation and cancer, (2) multiple searches for gene methylation associations, (3) multiple searches for the profile of gene methylation across human cancer types and (4) search for gene methylation of a specific cancer type. The major search results are shown around. Literature evidences with highlighted keywords could also be retrieved

MeInfoText: associated gene methylation and cancer information from text mining-1

<p><b>Copyright information:</b></p><p>Taken from "MeInfoText: associated gene methylation and cancer information from text mining"</p><p>http://www.biomedcentral.com/1471-2105/9/22</p><p>BMC Bioinformatics 2008;9():22-22.</p><p>Published online 14 Jan 2008</p><p>PMCID:PMC2258285.</p><p></p>gories, (1) search for associations among gene, methylation and cancer, (2) multiple searches for gene methylation associations, (3) multiple searches for the profile of gene methylation across human cancer types and (4) search for gene methylation of a specific cancer type. The major search results are shown around. Literature evidences with highlighted keywords could also be retrieved

Schematic representation of the molecular mechanism is induced by ANXA4.

<p>ANXA4 binds to the plasma membrane in a Ca²⁺-dependent manner and induces downstream signaling transduction. ANXA4 up-regulates LAMP2, a lysosomal marker involved in exocytosis, and RHAMM. Previous reports have showed that RHAMM activates RAS and PI3K, which subsequently leads to the induction of AKT. ANXA4 up-regulates AKT and CDK1 activation, PBK gene expression and down-regulates p21. Ca²⁺ also up-regulates RHAMM and phospho-AKT, and down-regulates p21. This signal cascade might eventually lead to cell hyperproliferation. Solid lines with arrows and blue circles indicate confirmed regulation; dashed lines with arrows and purple circles indicate references or unconfirmed interactions.</p

MeInfoText: associated gene methylation and cancer information from text mining-3

<p><b>Copyright information:</b></p><p>Taken from "MeInfoText: associated gene methylation and cancer information from text mining"</p><p>http://www.biomedcentral.com/1471-2105/9/22</p><p>BMC Bioinformatics 2008;9():22-22.</p><p>Published online 14 Jan 2008</p><p>PMCID:PMC2258285.</p><p></p>tated with gene symbols. The most recent 100 gene-annotated abstracts are manually checked to reduce false named entity recognitions and enhance dictionary coverage. The gene-annotated documents are indexed with Plucene module and then mined according to the frequencies of co-occurrences of entities. Various association, protein-protein interaction and pathway information are stored in the relational database, MeInfoText. Users can search the database via the web interface. Thick arrow indicates the basic workflow of MeInfoText

ANXA4 induces downstream signal transduction.

<p>(A<i>–</i>B) Protein levels of p21, phospho-AKT (Ser473) and phospho-CDK1 (Thr161) in AGS cells, as determined by immunoblotting analysis. (A) Cells were transfected with empty vector or full-length <i>ANXA4</i>. (B) Cells were transfected with siControl or si<i>ANXA4</i>. Representative data from three independent experiments are presented as mean ± SD. α-tubulin was used as an internal control. *<i>P</i><0.05, **<i>P</i><0.01 vs. control treatment values.</p

ANXA4 induces cell proliferation.

<p>To measure cell proliferation, AGS cells were cultured in a 16-well microtiter E-plate. After incubation for 24 h, the cell growth rate of (A) cells overexpressing ANXA4, and (B) cells containing <i>ANXA4</i>-specific siRNA were measured. It was observed that ANXA4 regulated the cell index in a time-dependent manner. (A and B) Data were normalized from measurements taken at 24 h, which was when transfection was initiated. The detection time from three independent experiments is represented as mean ± SD, n = 3. <i>P</i> values were calculated using the two-sample Kolmogorov-Smirnov test.</p

ANXA4 up-regulates RHAMM and LAMP2 expression.

<p>(A) AGS cells were transfected with either an empty vector (pcDNA3.1), full-length <i>ANXA4</i> (pcDNA3.1/<i>ANXA4</i>), control siRNA (siControl), or <i>ANXA4</i> siRNA (si<i>ANXA4</i>). (B–C) RHAMM and LAMP2 expressions were measured in ANXA4-overexpressing cells or cells containing <i>ANXA4</i>-specific siRNA by immunoblotting analysis. (B) The expression of RHAMM was significantly up-regulated (<i>P</i><0.05) after overexpressing ANXA4 and significantly down-regulated (<i>P</i><0.01) after silencing ANXA4 expression. (C) The expression of LAMP2 was up-regulated after overexpressing ANXA4 and significantly down-regulated (<i>P</i><0.05) after silencing ANXA4 expression. Data are represented as mean ± SD, n = 3. *<i>P</i><0.05 vs. control treatment values.</p

MeInfoText: associated gene methylation and cancer information from text mining-2

<p><b>Copyright information:</b></p><p>Taken from "MeInfoText: associated gene methylation and cancer information from text mining"</p><p>http://www.biomedcentral.com/1471-2105/9/22</p><p>BMC Bioinformatics 2008;9():22-22.</p><p>Published online 14 Jan 2008</p><p>PMCID:PMC2258285.</p><p></p>y oval-shape. For instance, each human gene may contribute to one or more cancers due to abnormal methylation, have many interacting partners and involve several signaling pathways. Each association between gene methylation and cancer could be referred to one or more evidences