Differentially expressed genes from the glioblastoma cell line SHG-44 treated with all-trans retinoic acid in vitro

Current Opportunities for Targeting Dysregulated Neurodevelopmental Signaling Pathways in Glioblastoma

Glioblastoma (GBM) is the most common and highly lethal type of brain tumor, with poor survival despite advances in understanding its complexity. After current standard therapeutic treatment, including tumor resection, radiotherapy and concomitant chemotherapy with temozolomide, the median overall survival of patients with this type of tumor is less than 15 months. Thus, there is an urgent need for new insights into GBM molecular characteristics and progress in targeted therapy in order to improve clinical outcomes. The literature data revealed that a number of different signaling pathways are dysregulated in GBM. In this review, we intended to summarize and discuss current literature data and therapeutic modalities focused on targeting dysregulated signaling pathways in GBM. A better understanding of opportunities for targeting signaling pathways that influences malignant behavior of GBM cells might open the way for the development of novel GBM-targeted therapies.

A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma

Differential co-expression network analyses have recently become an important step in the investigation of cellular differentiation and dysfunctional gene-regulation in cell and tissue disease-states. The resulting networks have been analyzed to identify and understand pathways associated with disorders, or to infer molecular interactions. However, existing methods for differential co-expression network analysis are unable to distinguish between various forms of differential co-expression. To close this gap, here we define the three different kinds (conserved, specific, and differentiated) of differential co-expression and present a systematic framework, CSD, for differential co-expression network analysis that incorporates these interactions on an equal footing. In addition, our method includes a subsampling strategy to estimate the variance of co-expressions. Our framework is applicable to a wide variety of cases, such as the study of differential co-expression networks between healthy and disease states, before and after treatments, or between species. Applying the CSD approach to a published gene-expression data set of cerebral cortex and basal ganglia samples from healthy individuals, we find that the resulting CSD network is enriched in genes associated with cognitive function, signaling pathways involving compounds with well-known roles in the central nervous system, as well as certain neurological diseases. From the CSD analysis, we identify a set of prominent hubs of differential co-expression, whose neighborhood contains a substantial number of genes associated with glioblastoma. The resulting gene-sets identified by our CSD analysis also contain many genes that so far have not been recognized as having a role in glioblastoma, but are good candidates for further studies. CSD may thus aid in hypothesis-generation for functional disease-associations.

Integrative bayesian network analysis of genomic data

Rapid development of genome-wide profiling technologies has made it possible to conduct integrative analysis on genomic data from multiple platforms. In this study, we develop a novel integrative Bayesian network approach to investigate the relationships between genetic and epigenetic alterations as well as how these mutations affect a patient's clinical outcome. We take a Bayesian network approach that admits a convenient decomposition of the joint distribution into local distributions. Exploiting the prior biological knowledge about regulatory mechanisms, we model each local distribution as linear regressions. This allows us to analyze multi-platform genome-wide data in a computationally efficient manner. We illustrate the performance of our approach through simulation studies. Our methods are motivated by and applied to a multi-platform glioblastoma dataset, from which we reveal several biologically relevant relationships that have been validated in the literature as well as new genes that could potentially be novel biomarkers for cancer progression.

Polypeptide N-acetylgalactosaminyltransferase 2 regulates cellular metastasis-associated behavior in gastric cancer

Aberrant glycosylation of cell surface glycoprotein due to specific alterations of glycosyltransferase activity is usually associated with invasion and metastasis of cancer, particularly of gastric carcinomas. Polypeptide N-acetylgalactosaminyltransferase 2 (ppGalNAc-T2), which catalyzes initiation of mucin-type O-glycosylation, is also involved in tumor migration and invasion. However, a comprehensive understanding of how ppGalNAc-T2 correlates with the metastasic potential of human gastric cancer is not currently available. In the present study, ppGalNAc-T2 was detected in a variety of human poorly differentiated tumor cells, and expression appeared to be higher in SGC7901 gastric cancer cells. In addition, we investigated the potential effects of ppGalNAc-T2 on growth and metastasis-associated behavior in SGC7901 cells after stable transfection with ppGalNAc-T2 sense and antisense vectors. We found that cell proliferation, adhesion and invasion were decreased in ppGalNAc-T2 overexpressed cells but increased in ppGalNAc-T2 downregulated cells. Therefore, we attempted to clarify the mechanisms underlying the anti-metastatic activities of ppGalNAc-T2. Further investigation indicated that overexpression of ppGalNAc-T2 is involved in the inhibition of matrix metalloproteinase (MMP)-2 expression at both the protein and mRNA levels, which may be associated with ppGalNAc-T2 suppressing the expression of transforming growth factor (TGF)-β1. However, it did not exhibit any apparent correlation with MMP-14 expression levels. Our data show the effect of ppGalNAc-T2 on proliferation, adhesion or invasion of SGC7901 gastric cancer cells, suggesting that ppGalNAc-T2 may exert anti-proliferative and anti-metastatic activity through the decrease of MMP-2 and TGF-β1. These results indicate that ppGalNAc‑T2 may be used as a novel therapeutic target for human gastric cancer treatment.

Cell cycle and aging, morphogenesis, and response to stimuli genes are individualized biomarkers of glioblastoma progression and survival

BACKGROUND

Glioblastoma is a complex multifactorial disorder that has swift and devastating consequences. Few genes have been consistently identified as prognostic biomarkers of glioblastoma survival. The goal of this study was to identify general and clinical-dependent biomarker genes and biological processes of three complementary events: lifetime, overall and progression-free glioblastoma survival.

METHODS

A novel analytical strategy was developed to identify general associations between the biomarkers and glioblastoma, and associations that depend on cohort groups, such as race, gender, and therapy. Gene network inference, cross-validation and functional analyses further supported the identified biomarkers.

RESULTS

A total of 61, 47 and 60 gene expression profiles were significantly associated with lifetime, overall, and progression-free survival, respectively. The vast majority of these genes have been previously reported to be associated with glioblastoma (35, 24, and 35 genes, respectively) or with other cancers (10, 19, and 15 genes, respectively) and the rest (16, 4, and 10 genes, respectively) are novel associations. Pik3r1, E2f3, Akr1c3, Csf1, Jag2, Plcg1, Rpl37a, Sod2, Topors, Hras, Mdm2, Camk2g, Fstl1, Il13ra1, Mtap and Tp53 were associated with multiple survival events.Most genes (from 90 to 96%) were associated with survival in a general or cohort-independent manner and thus the same trend is observed across all clinical levels studied. The most extreme associations between profiles and survival were observed for Syne1, Pdcd4, Ighg1, Tgfa, Pla2g7, and Paics. Several genes were found to have a cohort-dependent association with survival and these associations are the basis for individualized prognostic and gene-based therapies. C2, Egfr, Prkcb, Igf2bp3, and Gdf10 had gender-dependent associations; Sox10, Rps20, Rab31, and Vav3 had race-dependent associations; Chi3l1, Prkcb, Polr2d, and Apool had therapy-dependent associations. Biological processes associated glioblastoma survival included morphogenesis, cell cycle, aging, response to stimuli, and programmed cell death.

CONCLUSIONS

Known biomarkers of glioblastoma survival were confirmed, and new general and clinical-dependent gene profiles were uncovered. The comparison of biomarkers across glioblastoma phases and functional analyses offered insights into the role of genes. These findings support the development of more accurate and personalized prognostic tools and gene-based therapies that improve the survival and quality of life of individuals afflicted by glioblastoma multiforme.

Aldehyde dehydrogenase 1 positive glioblastoma cells show brain tumor stem cell capacity

Glioblastoma (GBM) is the most aggressive primary brain tumor and is resistant to all therapeutic regimens. Relapse occurs regularly and might be caused by a poorly characterized tumor stem cell (TSC) subpopulation escaping therapy. We suggest aldehyde dehydrogenase 1 (ALDH1) as a novel stem cell marker in human GBM. Using the neurosphere formation assay as a functional method to identify brain TSCs, we show that high protein levels of ALDH1 facilitate neurosphere formation in established GBM cell lines. Even single ALDH1 positive cells give rise to colonies and neurospheres. Consequently, the inhibition of ALDH1 in vitro decreases both the number of neurospheres and their size. Cell lines without expression of ALDH1 do not form tumor spheroids under the same culturing conditions. High levels of ALDH1 seem to keep tumor cells in an undifferentiated, stem cell-like state indicated by the low expression of beta-III-tubulin. In contrast, ALDH1 inhibition induces premature cellular differentiation and reduces clonogenic capacity. Primary cell cultures obtained from fresh tumor samples approve the established GBM cell line results.

De-repression of CTGF via the miR-17-92 cluster upon differentiation of human glioblastoma spheroid cultures

All-trans retinoic acid is a potent promoter of cellular differentiation processes, which is used in cancer therapy. Glioblastoma spheroid cultures are enriched in tumor-initiating cells, and provide a model to test new treatment options in vitro. We investigated the molecular mechanisms of response to exposure to differentiation-promoting conditions in such cultures. Microarray analyses of five independent cultures showed that after induction of differentiation, inhibitors of transforming growth factor-beta/bone morphogenetic protein, Wnt/beta-catenin and IGF signaling were upregulated, whereas expression of several microRNAs decreased, particularly that of the miR-17-92 cluster. In primary astrocytic gliomas (n=82), expression of several members of miR-17-92 was significantly higher relative to those of normal brain (n=8) and significantly increased with tumor grade progression (P<0.05). A high-level amplification of the miR-17-92 locus was detected in one glioblastoma specimen. Transfection of inhibitors of miR-17-92 induced increased apoptosis and decreased cell proliferation in glioblastoma spheroids. Mir-17-92 inhibition was also associated with increased messenger RNA (mRNA) and/or protein expression of CDKN1A, E2F1, PTEN and CTGF. The CTGF gene was shown to be a target of miR-17-92 in glioblastoma spheroids by luciferase reporter assays. Our results suggest that miR-17-92 and its target CTGF mediate effects of differentiation-promoting treatment on glioblastoma cells through multiple regulatory pathways.

Retinoid-responsive transcriptional changes in epidermal keratinocytes

Retinoids (RA) have been used as therapeutic agents for numerous skin diseases, from psoriasis to acne and wrinkles. While RA is known to inhibit keratinocyte differentiation, the molecular effects of RA in epidermis have not been comprehensively defined. To identify the transcriptional targets of RA in primary human epidermal keratinocytes, we compared the transcriptional profiles of cells grown in the presence or absence of all-trans retinoic acid for 1, 4, 24, 48, and 72 h, using large DNA microarrays. As expected, RA suppresses the protein markers of cornification; however the genes responsible for biosynthesis of epidermal lipids, long-chain fatty acids, cholesterol, and sphingolipids, are also suppressed. Importantly, the pathways of RA synthesis, esterification and metabolism are activated by RA; therefore, RA regulates its own bioavailability. Unexpectedly, RA regulates many genes associated with the cell cycle and programmed cell death. This led us to reveal novel effects of RA on keratinocyte proliferation and apoptosis. The response to RA is very fast: 315 genes were regulated already after 1 h. More than one-third of RA-regulated genes function in signal transduction and regulation of transcription. Using in silico analysis, we identified a set of over-represented transcription factor binding sites in the RA-regulated genes. Many psoriasis-related genes are regulated by RA, some induced, others suppressed. These results comprehensively document the transcriptional changes caused by RA in keratinocytes, add new insights into the molecular mechanism influenced by RA in the epidermis and demonstrate the hypothesis-generating power of DNA microarray analysis.