Differential expression of microRNAs as predictors of glioblastoma phenotypes

abstract: Background Glioblastoma is the most aggressive primary central nervous tumor and carries a very poor prognosis. Invasion precludes effective treatment and virtually assures tumor recurrence. In the current study, we applied analytical and bioinformatics approaches to identify a set of microRNAs (miRs) from several different human glioblastoma cell lines that exhibit significant differential expression between migratory (edge) and migration-restricted (core) cell populations. The hypothesis of the study is that differential expression of miRs provides an epigenetic mechanism to drive cell migration and invasion. Results Our research data comprise gene expression values for a set of 805 human miRs collected from matched pairs of migratory and migration-restricted cell populations from seven different glioblastoma cell lines. We identified 62 down-regulated and 2 up-regulated miRs that exhibit significant differential expression in the migratory (edge) cell population compared to matched migration-restricted (core) cells. We then conducted target prediction and pathway enrichment analysis with these miRs to investigate potential associated gene and pathway targets. Several miRs in the list appear to directly target apoptosis related genes. The analysis identifies a set of genes that are predicted by 3 different algorithms, further emphasizing the potential validity of these miRs to promote glioblastoma. Conclusions The results of this study identify a set of miRs with potential for decreased expression in invasive glioblastoma cells. The verification of these miRs and their associated targeted proteins provides new insights for further investigation into therapeutic interventions. The methodological approaches employed here could be applied to the study of other diseases to provide biomedical researchers and clinicians with increased opportunities for therapeutic interventions.The electronic version of this article is the complete one and can be found online at: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-2

Statistical Modeling of MicroRNA Expression with Human Cancers

MicroRNAs (miRNAs) are small non-coding RNAs (containing about 22 nucleotides) that regulate gene expression. MiRNAs are involved in many different biological processes such as cell proliferation, differentiation, apoptosis, fat metabolism, and human cancer genes; while miRNAs may function as candidates for diagnostic and prognostic biomarkers and predictors of drug response. This paper emphasizes the statistical methods in the analysis of the associations of miRNA gene expression with human cancers and related clinical phenotypes: 1) simple statistical methods include chi-square test, correlation analysis, t-test and one-way ANOVA; 2) regression models include linear and logistic regression; 3) survival analysis approaches such as non-parametric Kaplan-Meier method and log-rank test as well as semi-parametric Cox proportional hazards models have been used for time to event data; 4) multivariate method such as cluster analysis has been used for clustering samples and principal component analysis (PCA) has been used for data mining; 5) Bayesian statistical methods have recently made great inroads into many areas of science, including the assessment of association between miRNA expression and human cancers; and 6) multiple testing

Differential miRNA expression profiling reveals miR-205-3p to be a potential radiosensitizer for low- dose ionizing radiation in DLD-1 cells

Indexación: Scopus.Departamento de Oncología Básico-Clínica, Facultad de Medicina, Universidad de Chile, Santiago, Chile 2Comisión Chilena de Energía Nuclear, Santiago, Chile 3Center for Research and Applications in Plasma Physics and Pulsed Power, P4, Chile 4Departamento de Ciencias Físicas, Universidad Andres Bello, Santiago, Chile 5Centro de Investigación y Tratamiento del Cáncer, Facultad de Medicina, Universidad de Chile, Santiago, Chile 6Current Address: Center of Excellence in Precision Medicine, Pfizer, Chile. on IR responsive modeling. This work was supported by Anillo grant ACT1115 and ACT172101, PIA Program, CONICYT; the Chilean doctoral fellowship 21130246Enhanced radiosensitivity at low doses of ionizing radiation (IR) (0.2 to 0.6 Gy) has been reported in several cell lines. This phenomenon, known as low doses hyperradiosensitivity (LDHRS), appears as an opportunity to decrease toxicity of radiotherapy and to enhance the effects of chemotherapy. However, the effect of low single doses IR on cell death is subtle and the mechanism underlying LDHRS has not been clearly explained, limiting the utility of LDHRS for clinical applications. To understand the mechanisms responsible for cell death induced by low-dose IR, LDHRS was evaluated in DLD-1 human colorectal cancer cells and the expression of 80 microRNAs (miRNAs) was assessed by qPCR array. Our results show that DLD-1 cells display an early DNA damage response and apoptotic cell death when exposed to 0.6 Gy. miRNA expression profiling identified 3 over-expressed (miR-205-3p, miR-1 and miR-133b) and 2 downregulated miRNAs (miR-122-5p, and miR-134-5p) upon exposure to 0.6 Gy. This miRNA profile differed from the one in cells exposed to high-dose IR (12 Gy), supporting a distinct low-dose radiation-induced cell death mechanism. Expression of a mimetic miR- 205-3p, the most overexpressed miRNA in cells exposed to 0.6 Gy, induced apoptotic cell death and, more importantly, increased LDHRS in DLD-1 cells. Thus, we propose miR-205-3p as a potential radiosensitizer to low-dose IR. © Andaur et al.http://www.oncotarget.com/index.php?journal=oncotarget&page=article&op=view&path[]=25405&path[]=7956

Exosomal MicroRNA Signatures in Central Nervous System Diseases

During the last decade there has been a growing interest in studying extracellular vesicles, in particular exosomes and their miRNA contents. Exosomes are released by almost all cell types. They are packed with specific information, stable against degradation processes, are small and flexible enough to cross the blood-brain barrier (BBB), and are readily found in biological fluids including blood. MicroRNAs (miRNAs) are involved in nearly every cellular process and play a regulatory role in central nervous system (CNS) associated diseases. Accordingly, exosomal miRNAs could be ideal biomarkers to measure CNS disease activity and treatment response. In this thesis, the aim was to establish a robust protocol to investigate whether the differential expression of serum exosomal miRNA can be used as a biomarker for the accurate diagnosis of the CNS diseases multiple sclerosis (MS) and glioblastoma multiforme (GBM), as well as for the monitoring of disease progression and treatment response. Exosomes were purified from serum and their RNA contents profiled using highthroughput sequencing. In my first study, I profiled exosome–associated miRNAs in serum samples from MS patients and identified distinct biomarkers for the diagnosis of MS and identification of the disease subtype. In my second study, I investigated the effect of treatment in MS patients. I hypothesised that the deregulation of serum exosomal miRNAs is associated with the efficacy of therapy and is predictive of MS activity phases. Finally, I studied serum exosomal miRNA profiles to discover diagnostic biomarkers for GBM, and to demonstrate the applicability of my protocol to other neurological diseases. Taken together, my results demonstrate the exceptional utility of serum exosomal miRNA profiles as a blood-based biomarker to diagnose the CNS associated diseases, using a robust and easily reproducible protocol

\u3ci\u3e β3-adrenergic agonists mimic eustress response and reduce leptin-mediated proliferation in a GBM cell line \u3c/i\u3e

A great deal of mental stress, depression, and anxiety often overwhelm cancer patients; those diagnosed with glioblastoma multiforme (GBM) are no exception. Different types of stress invariably impact what has been termed “the brain-adipocyte BDNF/leptin axis” (Dr. Cao and colleagues of the Comprehensive Cancer Center at The Ohio State University). For example, eustress (good stress) and distress (bad stress) both lead to increased sympathetic activity and adrenal gland stimulation, yet eustress reduces leptin levels and attenuates tumor growth while distress increases leptin levels and augments tumor growth. Complicating matters in GBM is that leptin and its receptor are expressed at much higher levels than in normal glial cells and provides a potential autocrine signaling pathway. In this study, we confirm that 200 ng/mL of leptin-conditioned media increases cell proliferation of the established GBM cell line T98G. We hypothesized that elevated sympathoadrenal activity would increase cell proliferation and be additive to leptin\u27s effects. To the contrary, adding 300 pg/mL of epinephrine to leptin-conditioned media blocked leptin-mediated proliferation. Because beta3-adrenergic receptor stimulation suppresses leptin gene expression and release in adipocytes, we hypothesized that a beta3-adrenergic agonist would counteract leptin\u27s effects on T98G cell proliferation. Use of the beta3-adrenergic agonist BRL 37344 did not only counteract leptin\u27s effects but also significantly reduced T98G cell proliferation in unconditioned media. This has immediate translational value in that treating GBM with a beta3-adrenergic agonist may reduce tumor proliferation through receptor activation and by blocking the leptin-leptin receptor autocrine loop. Moreover, recent reports indicate that beta3-adrenergic agonists capable of crossing the blood-brain barrier (like SR 58611A) may be beneficial for anxiety and depression, further improving the quality of life for brain tumor patients

Glioblastoma stem cells (GSCs) epigenetic plasticity and interconversion between differentiated non-GSCs and GSCs

AbstractCancer stem cells (CSCs) or cancer initiating cells (CICs) maintain self-renewal and multilineage differentiation properties of various tumors, as well as the cellular heterogeneity consisting of several subpopulations within tumors. CSCs display the malignant phenotype, self-renewal ability, altered genomic stability, specific epigenetic signature, and most of the time can be phenotyped by cell surface markers (e.g., CD133, CD24, and CD44). Numerous studies support the concept that non-stem cancer cells (non-CSCs) are sensitive to cancer therapy while CSCs are relatively resistant to treatment. In glioblastoma stem cells (GSCs), there is clonal heterogeneity at the genetic level with distinct tumorigenic potential, and defined GSC marker expression resulting from clonal evolution which is likely to influence disease progression and response to treatment. Another level of complexity in glioblastoma multiforme (GBM) tumors is the dynamic equilibrium between GSCs and differentiated non-GSCs, and the potential for non-GSCs to revert (dedifferentiate) to GSCs due to epigenetic alteration which confers phenotypic plasticity to the tumor cell population. Moreover, exposure of the differentiated GBM cells to therapeutic doses of temozolomide (TMZ) or ionizing radiation (IR) increases the GSC pool both in vitro and in vivo. This review describes various subtypes of GBM, discusses the evolution of CSC models and epigenetic plasticity, as well as interconversion between GSCs and differentiated non-GSCs, and offers strategies to potentially eliminate GSCs

Neuronally enriched microvesicle RNAs are differentially expressed in the serums of Parkinson’s patients

BackgroundCirculating small RNAs (smRNAs) originate from diverse tissues and organs. Previous studies investigating smRNAs as potential biomarkers for Parkinson’s disease (PD) have yielded inconsistent results. We investigated whether smRNA profiles from neuronally-enriched serum exosomes and microvesicles are altered in PD patients and discriminate PD subjects from controls.MethodsDemographic, clinical, and serum samples were obtained from 60 PD subjects and 40 age- and sex-matched controls. Exosomes and microvesicles were extracted and isolated using a validated neuronal membrane marker (CD171). Sequencing and bioinformatics analyses were used to identify differentially expressed smRNAs in PD and control samples. SmRNAs also were tested for association with clinical metrics. Logistic regression and random forest classification models evaluated the discriminative value of the smRNAs.ResultsIn serum CD171 enriched exosomes and microvesicles, a panel of 29 smRNAs was expressed differentially between PD and controls (false discovery rate (FDR) < 0.05). Among the smRNAs, 23 were upregulated and 6 were downregulated in PD patients. Pathway analysis revealed links to cellular proliferation regulation and signaling. Least absolute shrinkage and selection operator adjusted for the multicollinearity of these smRNAs and association tests to clinical parameters via linear regression did not yield significant results. Univariate logistic regression models showed that four smRNAs achieved an AUC ≥ 0.74 to discriminate PD subjects from controls. The random forest model had an AUC of 0.942 for the 29 smRNA panel.ConclusionCD171-enriched exosomes and microvesicles contain the differential expression of smRNAs between PD and controls. Future studies are warranted to follow up on the findings and understand the scientific and clinical relevance

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Identification of MicroRNAs as Potential Prognostic Markers in Ependymoma

INTRODUCTION: We have examined expression of microRNAs (miRNAs) in ependymomas to identify molecular markers of value for clinical management. miRNAs are non-coding RNAs that can block mRNA translation and affect mRNA stability. Changes in the expression of miRNAs have been correlated with many human cancers. MATERIALS AND METHODS: We have utilized TaqMan Low Density Arrays to evaluate the expression of 365 miRNAs in ependymomas and normal brain tissue. We first demonstrated the similarity of expression profiles of paired frozen tissue (FT) and paraffin-embedded specimens (FFPE). We compared the miRNA expression profiles of 34 FFPE ependymoma samples with 8 microdissected normal brain tissue specimens enriched for ependymal cells. miRNA expression profiles were then correlated with tumor location, histology and other clinicopathological features. RESULTS: We have identified miRNAs that are over-expressed in ependymomas, such as miR-135a and miR-17-5p, and down-regulated, such as miR-383 and miR-485-5p. We have also uncovered associations between expression of specific miRNAs which portend a worse prognosis. For example, we have identified a cluster of miRNAs on human chromosome 14q32 that is associated with time to relapse. We also found that miR-203 is an independent marker for relapse compared to the parameters that are currently used. Additionally, we have identified three miRNAs (let-7d, miR-596 and miR-367) that strongly correlate to overall survival. CONCLUSION: We have identified miRNAs that are differentially expressed in ependymomas compared with normal ependymal tissue. We have also uncovered significant associations of miRNAs with clinical behavior. This is the first report of clinically relevant miRNAs in ependymomas