The EphB4 receptor tyrosine kinase is overexpressed in many cancers including prostate cancer. The molecular mechanisms by which this ephrin receptor influences cancer progression are complex as there are tumor-promoting ligand-independent mechanisms in place as well as ligand-dependent tumor suppressive pathways.
We employed transient knockdown of EPHB4 in prostate cancer cells, coupled with gene microarray analysis, to identify genes that were regulated by EPHB4 and may represent linked tumor-promoting factors. We validated target genes using qRT-PCR and employed functional assays to determine their role in prostate cancer migration and invasion.
We discovered that over 500 genes were deregulated upon EPHB4 siRNA knockdown, with integrin β8 (ITGB8) being the top hit (29-fold down-regulated compared to negative non-silencing siRNA). Gene ontology analysis found that the process of cell adhesion was highly deregulated and two other integrin genes, ITGA3 and ITGA10, were also differentially expressed. In parallel, we also discovered that over-expression of EPHB4 led to a concomitant increase in ITGB8 expression. In silico analysis of a prostate cancer progression microarray publically available in the Oncomine database showed that both EPHB4 and ITGB8 are highly expressed in prostatic intraepithelial neoplasia, the precursor to prostate cancer. Knockdown of ITGB8 in PC-3 and 22Rv1 prostate cancer cells in vitro resulted in significant reduction of cell migration and invasion.
These results reveal that EphB4 regulates integrin β8 expression and that integrin β8 plays a hitherto unrecognized role in the motility of prostate cancer cells and thus targeting integrin β8 may be a new treatment strategy for prostate cancer.
Electronic supplementary material
The online version of this article (doi:10.1186/s12885-015-1164-6) contains supplementary material, which is available to authorized users.
EphB4; Prostate cancer; Integrin-β8
Strand specific RNAseq data is now more common in RNAseq projects. Visualizing RNAseq data has become an important matter in Analysis of sequencing data. The most widely used visualization tool is the UCSC genome browser that introduced the custom track concept that enabled researchers to simultaneously visualize gene expression at a particular locus from multiple experiments. Our objective of the software tool is to provide friendly interface for visualization of RNAseq datasets.
This paper introduces a visualization tool (RNASeqBrowser) that incorporates and extends the functionality of the UCSC genome browser. For example, RNASeqBrowser simultaneously displays read coverage, SNPs, InDels and raw read tracks with other BED and wiggle tracks -- all being dynamically built from the BAM file. Paired reads are also connected in the browser to enable easier identification of novel exon/intron borders and chimaeric transcripts. Strand specific RNAseq data is also supported by RNASeqBrowser that displays reads above (positive strand transcript) or below (negative strand transcripts) a central line. Finally, RNASeqBrowser was designed for ease of use for users with few bioinformatic skills, and incorporates the features of many genome browsers into one platform.
The features of RNASeqBrowser: (1) RNASeqBrowser integrates UCSC genome browser and NGS visualization tools such as IGV. It extends the functionality of the UCSC genome browser by adding several new types of tracks to show NGS data such as individual raw reads, SNPs and InDels. (2) RNASeqBrowser can dynamically generate RNA secondary structure. It is useful for identifying non-coding RNA such as miRNA. (3) Overlaying NGS wiggle data is helpful in displaying differential expression and is simple to implement in RNASeqBrowser. (4) NGS data accumulates a lot of raw reads. Thus, RNASeqBrowser collapses exact duplicate reads to reduce visualization space. Normal PC’s can show many windows of NGS individual raw reads without much delay. (5) Multiple popup windows of individual raw reads provide users with more viewing space. This avoids existing approaches (such as IGV) which squeeze all raw reads into one window. This will be helpful for visualizing multiple datasets simultaneously.
RNASeqBrowser and its manual are freely available at http://www.australianprostatecentre.org/research/software/rnaseqbrowser or http://sourceforge.net/projects/rnaseqbrowser/
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-015-1346-2) contains supplementary material, which is available to authorized users.
RNA-seq; Genome browser; RNA secondary structure; SNP
Weak cell-surface adhesion of cell lines to tissue culture surfaces is a common problem and presents technical limitations to the design of experiments. To overcome this problem, various surface coating protocols have been developed. However, a comparative and precise real-time measurement of their impact on cell behavior has not been conducted. The prostate cancer cell line LNCaP, derived from a patient lymph node metastasis, is a commonly used model system in prostate cancer research. However, the cells’ characteristically weak attachment to the surface of tissue culture vessels and cover slips has impeded their manipulation and analysis and use in high throughput screening. To improve the adherence of LNCaP cells to the culture surface, we compared different coating reagents (poly-l-lysine, poly-l-ornithine, collagen type IV, fibronectin, and laminin) and culturing conditions and analyzed their impact on cell proliferation, adhesion, morphology, mobility and gene expression using real-time technologies. The results showed that fibronectin, poly-l-lysine and poly-l-ornithine improved LNCaP cells adherence and provoked cell morphology alterations, such as increase of nuclear and cellular area. These coating reagents also induced a higher expression of F-actin and reduced cell mobility. In contrast, laminin and collagen type IV did not improve adherence but promoted cell aggregation and affected cell morphology. Cells cultured in the presence of laminin displayed higher mobility than control cells. All the coating conditions significantly affected cell viability; however, they did not affect the expression of androgen receptor-regulated genes. Our comparative findings provide important insight for the selection of the ideal coating reagent and culture conditions for the cancer cell lines with respect to their effect on proliferation rate, attachment, morphology, migration, transcriptional response and cellular cytoskeleton arrangement.
Chitinase 3-like 1 (CHI3L1 or YKL40) is a secreted glycoprotein highly expressed in tumours from patients with advanced stage cancers, including prostate cancer (PCa). The exact function of YKL40 is poorly understood, but it has been shown to play an important role in promoting tumour angiogenesis and metastasis. The therapeutic value and biological function of YKL40 are unknown in PCa. The objective of this study was to examine the expression and function of YKL40 in PCa. Gene expression analysis demonstrated that YKL40 was highly expressed in metastatic PCa cells when compared with less invasive and normal prostate epithelial cell lines. In addition, the expression was primarily limited to androgen receptor-positive cell lines. Evaluation of YKL40 tissue expression in PCa patients showed a progressive increase in patients with aggressive disease when compared with those with less aggressive cancers and normal controls. Treatment of LNCaP and C4-2B cells with androgens increased YKL40 expression, whereas treatment with an anti-androgen agent decreased the gene expression of YKL40 in androgen-sensitive LNCaP cells. Furthermore, knockdown of YKL40 significantly decreased invasion and migration of PCa cells, whereas overexpression rendered them more invasive and migratory, which was commensurate with an enhancement in the anchorage-independent growth of cells. To our knowledge, this study characterises the role of YKL40 for the first time in PCa. Together, these results suggest that YKL40 plays an important role in PCa progression and thus inhibition of YKL40 may be a potential therapeutic strategy for the treatment of PCa.
YKL40; prostate cancer; cell migration; cell invasion; metastasis; targeted therapy
Ascidians are marine invertebrates that have been a source of numerous cytotoxic compounds. Of the first six marine-derived drugs that made anticancer clinical trials, three originated from ascidian specimens. In order to identify new anti-neoplastic compounds, an ascidian extract library (143 samples) was generated and screened in MDA-MB-231 breast cancer cells using a real-time cell analyzer (RTCA). This resulted in 143 time-dependent cell response profiles (TCRP), which are read-outs of changes to the growth rate, morphology, and adhesive characteristics of the cell culture. Twenty-one extracts affected the TCRP of MDA-MB-231 cells and were further investigated regarding toxicity and specificity, as well as their effects on cell morphology and cell cycle. The results of these studies were used to prioritize extracts for bioassay-guided fractionation, which led to the isolation of the previously identified marine natural product, eusynstyelamide B (1). This bis-indole alkaloid was shown to display an IC50 of 5 µM in MDA-MB-231 cells. Moreover, 1 caused a strong cell cycle arrest in G2/M and induced apoptosis after 72 h treatment, making this molecule an attractive candidate for further mechanism of action studies.
ascidian; eusynstyelamide B; cytotoxic; MDA-MB-231; RTCA; G2/M arrest
Inhibition of FASN has emerged as a promising therapeutic target in cancer, and numerous inhibitors have been investigated. However, severe pharmacological limitations have challenged their clinical testing. The synthetic FASN inhibitor triclosan, which was initially developed as a topical antibacterial agent, is merely affected by these pharmacological limitations. Yet, little is known about its mechanism in inhibiting the growth of cancer cells. Here we compared the cellular and molecular effects of triclosan in a panel of eight malignant and non-malignant prostate cell lines to the well-known FASN inhibitors C75 and orlistat, which target different partial catalytic activities of FASN. Triclosan displayed a superior cytotoxic profile with a several-fold lower IC50 than C75 or orlistat. Structure-function analysis revealed that alcohol functionality of the parent phenol is critical for inhibitory action. Rescue experiments confirmed that end product starvation was a major cause of cytotoxicity. Importantly, triclosan, C75 and orlistat induced distinct changes to morphology, cell cycle, lipid content and the expression of key enzymes of lipid metabolism, demonstrating that inhibition of different partial catalytic activities of FASN activates different metabolic pathways. These finding combined with its well-documented pharmacological safety profile make triclosan a promising drug candidate for the treatment of prostate cancer.
Triclosan; fatty acid synthase; AMPK; lipid metabolism; C75; orlistat; prostate cancer
The mainstay therapeutic strategy for metastatic castrate-resistant prostate cancer (CRPC) continues to be androgen deprivation therapy usually in combination with chemotherapy or androgen receptor targeting therapy in either sequence, or recently approved novel agents such as Radium 223. However, immunotherapy has also emerged as an option for the treatment of this disease following the approval of sipuleucel-T by the FDA in 2010. Immunotherapy is a rational approach for prostate cancer based on a body of evidence suggesting these cancers are inherently immunogenic and, most importantly, that immunological interventions can induce protective antitumour responses. Various forms of immunotherapy are currently being explored clinically, with the most common being cancer vaccines (dendritic-cell, viral, and whole tumour cell-based) and immune checkpoint inhibition. This review will discuss recent clinical developments of immune-based therapies for prostate cancer that have reached the phase III clinical trial stage. A perspective of how immunotherapy could be best employed within current treatment regimes to achieve most clinical benefits is also provided.
Epigenetic silencing mediated by CpG methylation is a common feature of many cancers. Characterizing aberrant DNA methylation changes associated with tumor progression may identify potential prognostic markers for prostate cancer (PCa). We treated two PCa cell lines, 22Rv1 and DU-145 with the demethylating agent 5-Aza 2’–deoxycitidine (DAC) and global methylation status was analyzed by performing methylation-sensitive restriction enzyme based differential methylation hybridization strategy followed by genome-wide CpG methylation array profiling. In addition, we examined gene expression changes using a custom microarray. Gene Set Enrichment Analysis (GSEA) identified the most significantly dysregulated pathways. In addition, we assessed methylation status of candidate genes that showed reduced CpG methylation and increased gene expression after DAC treatment, in Gleason score (GS) 8 vs. GS6 patients using three independent cohorts of patients; the publically available The Cancer Genome Atlas (TCGA) dataset, and two separate patient cohorts. Our analysis, by integrating methylation and gene expression in PCa cell lines, combined with patient tumor data, identified novel potential biomarkers for PCa patients. These markers may help elucidate the pathogenesis of PCa and represent potential prognostic markers for PCa patients.
Prostate cancer; tumor markers; biomarkers; epigenetics; DNA methylation; expression array; DAC treatment; demethylating agent treatment
Small RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeep’s probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction.
We have developed a user-friendly plant miRNA prediction tool called miRPlant. We show using 16 plant miRNA datasets from four different plant species that miRPlant has at least a 10% improvement in accuracy compared to miRDeep-P, which is the most popular plant miRNA prediction tool. Furthermore, miRPlant uses a Graphical User Interface for data input and output, and identified miRNA are shown with all RNAseq reads in a hairpin diagram.
We have developed miRPlant which extends miRDeep* to various plant species by adopting suitable strategies to identify hairpin excision regions and hairpin structure filtering for plants. miRPlant does not require any third party tools such as mapping or RNA secondary structure prediction tools. miRPlant is also the first plant miRNA prediction tool that dynamically plots miRNA hairpin structure with small reads for identified novel miRNAs. This feature will enable biologists to visualize novel pre-miRNA structure and the location of small RNA reads relative to the hairpin. Moreover, miRPlant can be easily used by biologists with limited bioinformatics skills.
miRPlant and its manual are freely available at http://www.australianprostatecentre.org/research/software/mirplant or http://sourceforge.net/projects/mirplant/.
Electronic supplementary material
The online version of this article (doi:10.1186/1471-2105-15-275) contains supplementary material, which is available to authorized users.
RNA-seq; miRNA; Plant small RNA; RNA secondary structure
Androgens regulate biological pathways to promote proliferation, differentiation, and survival of benign and malignant prostate tissue. Androgen receptor (AR) targeted therapies exploit this dependence and are used in advanced prostate cancer to control disease progression. Contemporary treatment regimens involve sequential use of inhibitors of androgen synthesis or AR function. Although targeting the androgen axis has clear therapeutic benefit, its effectiveness is temporary, as prostate tumor cells adapt to survive and grow. The removal of androgens (androgen deprivation) has been shown to activate both epithelial-to-mesenchymal transition (EMT) and neuroendocrine transdifferentiation (NEtD) programs. EMT has established roles in promoting biological phenotypes associated with tumor progression (migration/invasion, tumor cell survival, cancer stem cell-like properties, resistance to radiation and chemotherapy) in multiple human cancer types. NEtD in prostate cancer is associated with resistance to therapy, visceral metastasis, and aggressive disease. Thus, activation of these programs via inhibition of the androgen axis provides a mechanism by which tumor cells can adapt to promote disease recurrence and progression. Brachyury, Axl, MEK, and Aurora kinase A are molecular drivers of these programs, and inhibitors are currently in clinical trials to determine therapeutic applications. Understanding tumor cell plasticity will be important in further defining the rational use of androgen-targeted therapies clinically and provides an opportunity for intervention to prolong survival of men with metastatic prostate cancer.
prostate cancer; epithelial-to-mesenchymal transition; neuroendocrine; androgen deprivation therapy; castrate resistant; tumor cell plasticity; brachyury; Axl
Exosomes have been shown to act as mediators for cell to cell communication and as a potential source of biomarkers for many diseases, including prostate cancer. Exosomes are nanosized vesicles secreted by cells and consist of proteins normally found in multivesicular bodies, RNA, DNA and lipids. As a potential source of biomarkers, exosomes have attracted considerable attention, as their protein content resembles that of their cells of origin, even though it is noted that the proteins, miRNAs and lipids found in the exosomes are not a reflective stoichiometric sampling of the contents from the parent cells. While the biogenesis of exosomes in dendritic cells and platelets has been extensively characterized, much less is known about the biogenesis of exosomes in cancer cells. An understanding of the processes involved in prostate cancer will help to further elucidate the role of exosomes and other extracellular vesicles in prostate cancer progression and metastasis. There are few methodologies available for general isolation of exosomes, however validation of those methodologies is necessary to study the role of exosomal-derived biomarkers in various diseases. In this review, we discuss “exosomes” as a member of the family of extracellular vesicles and their potential to provide candidate biomarkers for prostate cancer.
exosomes; exosome biogenesis; extracellular vesicle; prostate cancer; biomarker; androgen receptor
Obesity and type 2 diabetes are recognised risk factors for the development of some cancers and, increasingly, predict more aggressive disease, treatment failure, and cancer-specific mortality. Many factors may contribute to this clinical observation. Hyperinsulinaemia, dyslipidaemia, hypoxia, ER stress, and inflammation associated with expanded adipose tissue are thought to be among the main culprits driving malignant growth and cancer advancement. This observation has led to the proposal of the potential utility of “old players” for the treatment of type 2 diabetes and metabolic syndrome as new cancer adjuvant therapeutics. Androgen-regulated pathways drive proliferation, differentiation, and survival of benign and malignant prostate tissue. Androgen deprivation therapy (ADT) exploits this dependence to systemically treat advanced prostate cancer resulting in anticancer response and improvement of cancer symptoms. However, the initial therapeutic response from ADT eventually progresses to castrate resistant prostate cancer (CRPC) which is currently incurable. ADT rapidly induces hyperinsulinaemia which is associated with more rapid treatment failure. We discuss current observations of cancer in the context of obesity, diabetes, and insulin-lowering medication. We provide an update on current treatments for advanced prostate cancer and discuss whether metabolic dysfunction, developed during ADT, provides a unique therapeutic window for rapid translation of insulin-sensitising medication as combination therapy with antiandrogen targeting agents for the management of advanced prostate cancer.
Cancer outlier profile analysis (COPA) has proven to be an effective approach to analyzing cancer expression data, leading to the discovery of the TMPRSS2 and ETS family gene fusion events in prostate cancer. However, the original COPA algorithm did not identify down-regulated outliers, and the currently available R package implementing the method is similarly restricted to the analysis of over-expressed outliers. Here we present a modified outlier detection method, mCOPA, which contains refinements to the outlier-detection algorithm, identifies both over- and under-expressed outliers, is freely available, and can be applied to any expression dataset.
We compare our method to other feature-selection approaches, and demonstrate that mCOPA frequently selects more-informative features than do differential expression or variance-based feature selection approaches, and is able to recover observed clinical subtypes more consistently. We demonstrate the application of mCOPA to prostate cancer expression data, and explore the use of outliers in clustering, pathway analysis, and the identification of tumour suppressors. We analyse the under-expressed outliers to identify known and novel prostate cancer tumour suppressor genes, validating these against data in Oncomine and the Cancer Gene Index. We also demonstrate how a combination of outlier analysis and pathway analysis can identify molecular mechanisms disrupted in individual tumours.
We demonstrate that mCOPA offers advantages, compared to differential expression or variance, in selecting outlier features, and that the features so selected are better able to assign samples to clinically annotated subtypes. Further, we show that the biology explored by outlier analysis differs from that uncovered in differential expression or variance analysis. mCOPA is an important new tool for the exploration of cancer datasets and the discovery of new cancer subtypes, and can be combined with pathway and functional analysis approaches to discover mechanisms underpinning heterogeneity in cancers.
Cancer; Outliers; Expression data; Expression profile; Cluster; Subtype; Heterogeneous; Bioinformatics; Percentile; Feature selection
miRDeep and its varieties are widely used to quantify known and novel micro RNA (miRNA) from small RNA sequencing (RNAseq). This article describes miRDeep*, our integrated miRNA identification tool, which is modeled off miRDeep, but the precision of detecting novel miRNAs is improved by introducing new strategies to identify precursor miRNAs. miRDeep* has a user-friendly graphic interface and accepts raw data in FastQ and Sequence Alignment Map (SAM) or the binary equivalent (BAM) format. Known and novel miRNA expression levels, as measured by the number of reads, are displayed in an interface, which shows each RNAseq read relative to the pre-miRNA hairpin. The secondary pre-miRNA structure and read locations for each predicted miRNA are shown and kept in a separate figure file. Moreover, the target genes of known and novel miRNAs are predicted using the TargetScan algorithm, and the targets are ranked according to the confidence score. miRDeep* is an integrated standalone application where sequence alignment, pre-miRNA secondary structure calculation and graphical display are purely Java coded. This application tool can be executed using a normal personal computer with 1.5 GB of memory. Further, we show that miRDeep* outperformed existing miRNA prediction tools using our LNCaP and other small RNAseq datasets. miRDeep* is freely available online at http://www.australianprostatecentre.org/research/software/mirdeep-star.
Effective management of chronic diseases such as prostate cancer is important. Research suggests a tendency to use self-care treatment options such as over-the-counter (OTC) complementary medications among prostate cancer patients. The current trend in patient-driven recording of health data in an online Personal Health Record (PHR) presents an opportunity to develop new data-driven approaches for improving prostate cancer patient care. However, the ability of current online solutions to share patients’ data for better decision support is limited. An informatics approach may improve online sharing of self-care interventions among these patients. It can also provide better evidence to support decisions made during their self-managed care.
To identify requirements for an online system and describe a new case-based reasoning (CBR) method for improving self-care of advanced prostate cancer patients in an online PHR environment.
A non-identifying online survey was conducted to understand self-care patterns among prostate cancer patients and to identify requirements for an online information system. The pilot study was carried out between August 2010 and December 2010. A case-base of 52 patients was developed.
The data analysis showed self-care patterns among the prostate cancer patients. Selenium (55%) was the common complementary supplement used by the patients. Paracetamol (about 45%) was the commonly used OTC by the patients.
The results of this study specified requirements for an online case-based reasoning information system. The outcomes of this study are being incorporated in design of the proposed Artificial Intelligence (Al) driven patient journey browser system. A basic version of the proposed system is currently being considered for implementation.
Advanced Prostate Cancer; Self-care; Patient Journey; PHR; Case-based reasoning
Biophysical and biochemical properties of the microenvironment regulate cellular responses such as growth, differentiation, morphogenesis and migration in normal and cancer cells. Since two-dimensional (2D) cultures lack the essential characteristics of the native cellular microenvironment, three-dimensional (3D) cultures have been developed to better mimic the natural extracellular matrix. To date, 3D culture systems have relied mostly on collagen and Matrigel™ hydrogels, allowing only limited control over matrix stiffness, proteolytic degradability, and ligand density. In contrast, bioengineered hydrogels allow us to independently tune and systematically investigate the influence of these parameters on cell growth and differentiation. In this study, polyethylene glycol (PEG) hydrogels, functionalized with the Arginine-glycine-aspartic acid (RGD) motifs, common cell-binding motifs in extracellular matrix proteins, and matrix metalloproteinase (MMP) cleavage sites, were characterized regarding their stiffness, diffusive properties, and ability to support growth of androgen-dependent LNCaP prostate cancer cells. We found that the mechanical properties modulated the growth kinetics of LNCaP cells in the PEG hydrogel. At culture periods of 28 days, LNCaP cells underwent morphogenic changes, forming tumor-like structures in 3D culture, with hypoxic and apoptotic cores. We further compared protein and gene expression levels between 3D and 2D cultures upon stimulation with the synthetic androgen R1881. Interestingly, the kinetics of R1881 stimulated androgen receptor (AR) nuclear translocation differed between 2D and 3D cultures when observed by immunofluorescent staining. Furthermore, microarray studies revealed that changes in expression levels of androgen responsive genes upon R1881 treatment differed greatly between 2D and 3D cultures. Taken together, culturing LNCaP cells in the tunable PEG hydrogels reveals differences in the cellular responses to androgen stimulation between the 2D and 3D environments. Therefore, we suggest that the presented 3D culture system represents a powerful tool for high throughput prostate cancer drug testing that recapitulates tumor microenvironment.
Accumulated biological research outcomes show that biological functions do not depend on individual genes, but on complex gene networks. Microarray data are widely used to cluster genes according to their expression levels across experimental conditions. However, functionally related genes generally do not show coherent expression across all conditions since any given cellular process is active only under a subset of conditions. Biclustering finds gene clusters that have similar expression levels across a subset of conditions. This paper proposes a seed-based algorithm that identifies coherent genes in an exhaustive, but efficient manner.
In order to find the biclusters in a gene expression dataset, we exhaustively select combinations of genes and conditions as seeds to create candidate bicluster tables. The tables have two columns (a) a gene set, and (b) the conditions on which the gene set have dissimilar expression levels to the seed. First, the genes with less than the maximum number of dissimilar conditions are identified and a table of these genes is created. Second, the rows that have the same dissimilar conditions are grouped together. Third, the table is sorted in ascending order based on the number of dissimilar conditions. Finally, beginning with the first row of the table, a test is run repeatedly to determine whether the cardinality of the gene set in the row is greater than the minimum threshold number of genes in a bicluster. If so, a bicluster is outputted and the corresponding row is removed from the table. Repeating this process, all biclusters in the table are systematically identified until the table becomes empty.
This paper presents a novel biclustering algorithm for the identification of additive biclusters. Since it involves exhaustively testing combinations of genes and conditions, the additive biclusters can be found more readily.
An association between the metabolic syndrome and reduced testosterone levels has been identified, and a specific inverse relationship between insulin and testosterone levels suggests that an important metabolic crosstalk exists between these two hormonal axes; however, the mechanisms by which insulin and androgens may be reciprocally regulated are not well described. Androgen-dependant gene pathways regulate the growth and maintenance of both normal and malignant prostate tissue, and androgen-deprivation therapy (ADT) in patients exploits this dependence when used to treat recurrent and metastatic prostate cancer resulting in tumour regression. A major systemic side effect of ADT includes induction of key features of the metabolic syndrome and the consistent feature of hyperinsulinaemia. Recent studies have specifically identified a correlation between elevated insulin and high-grade PCa and more rapid progression to castrate resistant disease. This paper examines the relationship between insulin and androgens in the context of prostate cancer progression. Prostate cancer patients present a promising cohort for the exploration of insulin stabilising agents as adjunct treatments for hormone deprivation or enhancers of chemosensitivity for treatment of advanced prostate cancer.
Secretory clusterin (sCLU) is a stress-activated, cytoprotective chaperone that confers broad-spectrum cancer treatment resistance and its targeted inhibitor (OGX-011) is currently in Phase II trials for prostate, lung, and breast cancer. However, molecular mechanisms by which sCLU inhibits treatment-induced apoptosis in prostate cancer remain incompletely defined. We report that sCLU increases NF-κB nuclear translocation and transcriptional activity by serving as a ubiquitin binding protein that enhances COMMD1 and I-κB proteasomal degradation by interacting with members of the SCF-βTrCP E3 ligase family. Knockdown of sCLU in prostate cancer cells stabilizes COMMD1 and I-κB, thereby sequestrating NF-κB in the cytoplasm and decreasing NF-κB transcriptional activity. Comparative microarray profiling of sCLU over-expressing and knockdown prostate cancer cells confirmed that the expression of many NF-κB regulated genes positively correlate with sCLU levels. We propose that elevated levels of sCLU promote prostate cancer cell survival by facilitating degradation of COMMD1 and I-κB, thereby activating the canonical NF-κB pathway.
prostate cancer; clusterin; COMMD1; I-κB; NF-κB
Cell-cell and cell-matrix interactions play a major role in tumor morphogenesis and cancer metastasis. Therefore, it is crucial to create a model with a biomimetic microenvironment that allows such interactions to fully represent the pathophysiology of a disease for an in vitro study. This is achievable by using three-dimensional (3D) models instead of conventional two-dimensional (2D) cultures with the aid of tissue engineering technology. We are now able to better address the complex intercellular interactions underlying prostate cancer (CaP) bone metastasis through such models. In this study, we assessed the interaction of CaP cells and human osteoblasts (hOBs) within a tissue engineered bone (TEB) construct. Consistent with other in vivo studies, our findings show that intercellular and CaP cell-bone matrix interactions lead to elevated levels of matrix metalloproteinases, steroidogenic enzymes and the CaP biomarker, prostate specific antigen (PSA); all associated with CaP metastasis. Hence, it highlights the physiological relevance of this model. We believe that this model will provide new insights for understanding of the previously poorly understood molecular mechanisms of bone metastasis, which will foster further translational studies, and ultimately offer a potential tool for drug screening.
3D in vitro model; bone metastasis; co-culture; tissue engineering; prostate cancer; osteoblasts
Time–course microarray data consist of mRNA expression from a common set of genes collected at different time points. Such data are thought to reflect underlying biological processes developing over time. In this article we propose a model that allows us to examine differential expression and gene network relationships using time course microarray data. We model each gene expression profile as a random functional transformation of the scale, amplitude and phase of a common curve. Inferences about the gene–specific amplitude parameters allow us to examine differential gene expression. Inferences about measures of functional similarity based on estimated time transformation functions allow us to examine gene networks while accounting for features of the gene expression profiles. We discuss applications to simulated data as well as to microarray data on prostate cancer progression.
Bayesian hierarchical model; differential expression; functional data; functional similarity; gene networks; time–course microarray data; time transformation