22 research outputs found

    Prostate Stromal Cells Express the Progesterone Receptor to Control Cancer Cell Mobility

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    <div><p>Background</p><p>Reciprocal interactions between epithelium and stroma play vital roles for prostate cancer development and progression. Enhanced secretions of cytokines and growth factors by cancer associated fibroblasts in prostate tumors create a favorable microenvironment for cancer cells to grow and metastasize. Our previous work showed that the progesterone receptor (PR) was expressed specifically in prostate stromal fibroblasts and smooth muscle cells. However, the expression levels of PR and its impact to tumor microenvironment in prostate tumors are poorly understood.</p><p>Methods</p><p>Immunohistochemistry assays are applied to human prostate tissue biopsies. Cell migration, invasion and proliferation assays are performed using human prostate cells. Real-time PCR and ELISA are applied to measure gene expression at molecular levels.</p><p>Results</p><p>Immunohistochemistry assays showed that PR protein levels were decreased in cancer associated stroma when compared with paired normal prostate stroma. Using <i>in vitro</i> prostate stromal cell models, we showed that conditioned media collected from PR positive stromal cells inhibited prostate cancer cell migration and invasion, but had minor suppressive impacts on cancer cell proliferation. PR suppressed the secretion of stromal derived factor-1 (SDF-1) and interlukin-6 (IL-6) by stromal cells independent to PR ligands. Blocking PR expression by siRNA or supplementation of exogenous SDF-1 or IL-6 to conditioned media from PR positive stromal cells counteracted the inhibitory effects of PR to cancer cell migration and invasion.</p><p>Conclusions</p><p>Decreased expression of the PR in cancer associated stroma may contribute to the elevated SDF-1 and IL-6 levels in prostate tumors and enhance prostate tumor progression.</p></div

    PR negatively regulates prostate cancer migration through a paracrine pathway.

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    <p>Conditioned media (CM) were collected from parental hCAFs, WPMY-1 or their derived cell lines expressing mock, PRA or PRB in the presence of vehicle or 10 nM P4. PC-3 cells were seeded in 6 well plates and incubated with CM from hCAFs (<b>A</b>) or from WPMY-1 (<b>B</b>) cells for 24 hours in wound healing assays. Representative images after 24 hour CM treatment were captured by an inverted microscope. WPMY-1 and its derived cell lines expressing mock, PRA or PRB were treated with 0, 10 nM and 100 nM of P4 for 24 hours (<b>C and E</b>) or with vehicle, 10 nM of P4 and/or 10 uM of RU486 for 24 hours (<b>D and F</b>). CM were then collected and incubated with PC-3 cells (<b>C–D</b>) and C4-2B (<b>E–F</b>) in cell migration assays as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092714#s2" target="_blank">material and methods</a> section. One-way ANOVA and paired student's t-test calculate the statistical significance set at P<0.05 as * and P<0.001 as ***.</p

    Visual representations of how the three conceptual trade-offs (as identified by Mouchet <i>et al</i>. (23)) may appear across the seven outcomes assessed in our study.

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    <p>Each example radar plot (A,B,C) shows all five focal outcomes (ecosystem health, migratory species, fishery resources, well-being of user groups (e.g., fishers), and well-being of users of the ecosystem (e.g., coastal residents, tourists), with the inner-most band representing a decline and the outside line representing an increase (indicated with ‘worst’ to ‘best’ on the radar plot). Key outcome trade-offs have been circled to aid understanding of the trade-off typology and how it applies to our data. Outcome abbreviations used in radar plot: Eco = ecosystem health change; WB_Eco = well-being change of the user of the ecosystem health indicator; WB_Fish = well-being change of the user of the fisheries indicator; Mig = migratory species change; Fish = fisheries change. <b>A</b>: Supply trade-off: ecosystem health improving, but fisheries declining (or vice versa; conservation versus use). <b>B</b>: Supply-demand trade-off: fisheries improving, but well-being of a user (fisher) declining (or vice versa). <b>C</b>: Demand trade-off: differentiated impacts in the well-being of different users, with a well-being decline of a user dependent on fisheries, and a well-being improvement of a user dependent on ecosystem health (e.g. tourism) (or vice versa).</p

    Diversity of microbial communities in healthy, non-severe and severe asthmatics.

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    <p>A. Boxplot of alpha diversity measures Species Richness and Shannon’s Diversity Index, which are not significantly different between groups. B Non metric multidimensional scaling of Bray Curtis distance split by group (stress 0.22). Here PERMANOVA (Adonis) indicates that community structure is significantly associated with asthma classification (P = 0.008) and this variable explains 6% of the variance.</p

    Heat map of operational taxonomic units (OTUs) found in the sputum of asthmatics and non-asthmatic subjects.

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    <p>The phylogenetic tree for the principal (>1% of total) OTUs is shown at the left. On the right, increasing depth of colour indicates relative abundance of the OTU in an individual sample. Major phyla are shown between the phylogenetic tree and the heat map. Proteo = Proteobacteria; Fuso = Fusobacteria.</p

    Characteristics of the subjects<sup>1</sup>.

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    <p>Characteristics of the subjects<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0152724#t001fn001" target="_blank"><sup>1</sup></a>.</p
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