Integration of transcriptome and proteome profiles in glioblastoma: looking for the missing link

Peritumoral Brain Zone in Astrocytoma: Morphology, Molecular Aspects, and Clinical Manifestations (Review)

A peritumoral brain zone is an area between a tumor and nontumorous brain tissue with tumor cell infiltration. The identification of this area is sufficiently difficult due to the lack of clear morphological or some other criteria. Besides, its dimensions may vary considerably. In the present review, we have analyzed the available data on the morphological structure and metabolism of peritumoral zone in astrocytomes, and considered the main molecular and genetic aspects and clinical manifestations. Exploration of the peritumoral zone is of great importance for determining the extent of resection to prevent recurrence and to reveal the causes and mechanisms of continued tumor growth.

Sample-Wise and Gene-Wise Comparisons Confirm a Greater Similarity of RNA and Protein Expression Data at the Level of Molecular Pathways and Suggest an Approach for the Data Quality Check in High-Throughput Expression Databases

Identification of genes and molecular pathways with congruent profiles in the proteomic and transcriptomic datasets may result in the discovery of promising transcriptomic biomarkers that would be more relevant to phenotypic changes. In this study, we conducted comparative analysis of 943 paired RNA and proteomic profiles obtained for the same samples of seven human cancer types from The Cancer Genome Atlas (TCGA) and NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) [two major open human cancer proteomic and transcriptomic databases] that included 15,112 protein-coding genes and 1611 molecular pathways. Overall, our findings demonstrated statistically significant improvement of the congruence between RNA and proteomic profiles when performing analysis at the level of molecular pathways rather than at the level of individual gene products. Transition to the molecular pathway level of data analysis increased the correlation to 0.19-0.57 (Pearson) and 0.14-057 (Spearman), or 2-3-fold for some cancer types. Evaluating the gain of the correlation upon transition to the data analysis the pathway level can be used to refine the omics data by identifying outliers that can be excluded from the comparison of RNA and proteomic profiles. We suggest using sample- and gene-wise correlations for individual genes and molecular pathways as a measure of quality of RNA/protein paired molecular data. We also provide a database of human genes, molecular pathways, and samples related to the correlation between RNA and protein products to facilitate an exploration of new cancer transcriptomic biomarkers and molecular mechanisms at different levels of human gene expression.

Bibliometric analysis of research on neurodegenerative diseases and single-cell RNA sequencing: Opportunities and challenges

Neurodegeneration, characterized by the progressive deterioration in neuronal structure or function, presents an elusive mechanism. The use of single-cell RNA sequencing (scRNA-seq) technology in the clinic is becoming increasingly prevalent in recent decades. This technology offers unparalleled cell-level insights into neurodegenerative diseases, establishing itself as a potent tool for elucidating these diseases underlying mechanisms. Here, we made a deep investigation for scRNA-seq research in neurodegenerative diseases using bibliometric analysis from 2009 to 2022. We observed a robust upward trajectory in the number of publications on this subject. The United States stood out as the principal contributor to this expanding field. Specifically, the University of California System exhibited notable research prowess in this field. Alzheimer disease and Parkinson disease were the diseases most frequently investigated. Key research hotspots include the creation of a molecular brain atlas and identification of vulnerable neuronal subpopulations and potential therapeutic targets at the transcriptomic level.

An overview of human single-cell RNA sequencing studies in neurobiological disease

Neurobiological disorders are highly prevalent medical conditions that contribute to significant morbidity and mortality. Single-cell RNA sequencing (scRNA-seq) is a technique that measures gene expression in individual cells. In this review, we survey scRNA-seq studies of tissues from patients suffering from neurobiological disease. This includes postmortem human brains and organoids derived from peripheral cells. We highlight a range of conditions, including epilepsy, cognitive disorders, substance use disorders, and mood disorders. These findings provide new insights into neurobiological disease in multiple ways, including discovering novel cell types or subtypes involved in disease, proposing new pathophysiological mechanisms, uncovering novel drug targets, or identifying potential biomarkers. We discuss the quality of these findings and suggest potential future directions and areas open for additional research, including studies of non-cortical brain regions and additional conditions such as anxiety disorders, mood disorders, and sleeping disorders. We argue that additional scRNA-seq of tissues from patients suffering from neurobiological disease could advance our understanding and treatment of these conditions.

The peritumoral brain zone in glioblastoma: where we are and where we are going

Glioblastoma (GBM) is the most aggressive and invasive primary brain tumor. Current therapies are not curative, and patients' outcomes remain poor with an overall survival of 20.9 months after surgery. The typical growing pattern of GBM develops by infiltrating the surrounding apparent normal brain tissue within which the recurrence is expected to appear in the majority of cases. Thus, in the last decades, an increased interest has developed to investigate the cellular and molecular interactions between GBM and the peritumoral brain zone (PBZ) bordering the tumor tissue. The aim of this review is to provide up-to-date knowledge about the oncogenic properties of the PBZ to highlight possible druggable targets for more effective treatment of GBM by limiting the formation of recurrence, which is almost inevitable in the majority of patients. Starting from the description of the cellular components, passing through the illustration of the molecular profiles, we finally focused on more clinical aspects, represented by imaging and radiological details. The complete picture that emerges from this review could provide new input for future investigations aimed at identifying new effective strategies to eradicate this still incurable tumor.

Alternative RNA splicing modulates ribosomal composition and determines the spatial phenotype of glioblastoma cells

Glioblastoma (GBM) is characterized by exceptionally high intratumoral heterogeneity. However, the molecular mechanisms underlying the origin of different GBM cell populations remain unclear. Here, we found that the compositions of ribosomes of GBM cells in the tumour core and edge differ due to alternative RNA splicing. The acidic pH in the core switches before messenger RNA splicing of the ribosomal gene RPL22L1 towards the RPL22L1b isoform. This allows cells to survive acidosis, increases stemness and correlates with worse patient outcome. Mechanistically, RPL22L1b promotes RNA splicing by interacting with lncMALAT1 in the nucleus and inducing its degradation. Contrarily, in the tumour edge region, RPL22L1a interacts with ribosomes in the cytoplasm and upregulates the translation of multiple messenger RNAs including TP53. We found that the RPL22L1 isoform switch is regulated by SRSF4 and identified a compound that inhibits this process and decreases tumour growth. These findings demonstrate how distinct GBM cell populations arise during tumour growth. Targeting this mechanism may decrease GBM heterogeneity and facilitate therapy.

HYGIEIA: HYpothesizing the Genesis of Infectious Diseases and Epidemics through an Integrated Systems Biology Approach

More than two years on, the COVID-19 pandemic continues to wreak havoc around the world and has battle-tested the pandemic-situation responses of all major global governments. Two key areas of investigation that are still unclear are: the molecular mechanisms that lead to heterogenic patient outcomes, and the causes of Post COVID condition (AKA Long-COVID). In this paper, we introduce the HYGIEIA project, designed to respond to the enormous challenges of the COVID-19 pandemic through a multi-omic approach supported by network medicine. It is hoped that in addition to investigating COVID-19, the logistics deployed within this project will be applicable to other infectious agents, pandemic-type situations, and also other complex, non-infectious diseases. Here, we first look at previous research into COVID-19 in the context of the proteome, metabolome, transcriptome, microbiome, host genome, and viral genome. We then discuss a proposed methodology for a large-scale multi-omic longitudinal study to investigate the aforementioned biological strata through high-throughput sequencing (HTS) and mass-spectrometry (MS) technologies. Lastly, we discuss how a network medicine approach can be used to analyze the data and make meaningful discoveries, with the final aim being the translation of these discoveries into the clinics to improve patient care.

Predicting missing proteomics values using machine learning: Filling the gap using transcriptomics and other biological features

Proteins are often considered the main biological element in charge of the different functions and structures of a cell. However, proteomics, the global study of all expressed proteins, often performed by mass spectrometry, is limited by its stochastic sampling and can only quantify a limited amount of protein per sample. Transcriptomics, which allows an exhaustive analysis of all expressed transcripts, is often used as a surrogate. However, the transcript level does not present a high level of correlation with the corresponding protein level, notably due to the existence of several post-transcriptional regulatory mechanisms. In this publication, we hypothesize that the missing protein values in proteomics could be predicted using machine learning regression methods, trained with many features extracted from transcriptomics, including known translational regulatory elements such as microRNAs and circular RNAs. After considering different machine learning algorithms applied on two different splitting strategies, we report that random forest can predict proteins in new samples out of transcriptomics data with good accuracy. The proposed pre-processing and model building scripts can be accessed on GitHub: https://github.com/jochotecoa/ml_proteomics.

In-Depth Matrisome and Glycoproteomic Analysis of Human Brain Glioblastoma Versus Control Tissue

Glioblastoma (GBM) is the most common and malignant primary brain tumor. The extracellular matrix, also known as the matrisome, helps determine glioma invasion, adhesion, and growth. Little attention, however, has been paid to glycosylation of the extracellular matrix components that constitute the majority of glycosylated protein mass and presumed biological properties. To acquire a comprehensive understanding of the biological functions of the matrisome and its components, including proteoglycans (PGs) and glycosaminoglycans (GAGs), in GBM tumorigenesis, and to identify potential biomarker candidates, we studied the alterations of GAGs, including heparan sulfate (HS) and chondroitin sulfate (CS), the core proteins of PGs, and other glycosylated matrisomal proteins in GBM subtypes versus control human brain tissue samples. We scrutinized the proteomics data to acquire in-depth site-specific glycoproteomic profiles of the GBM subtypes that will assist in identifying specific glycosylation changes in GBM. We observed an increase in CS 6-O sulfation and a decrease in HS 6-O sulfation, accompanied by an increase in unsulfated CS and HS disaccharides in GBM versus control samples. Several core matrisome proteins, including PGs (decorin, biglycan, agrin, prolargin, glypican-1, and chondroitin sulfate proteoglycan 4), tenascin, fibronectin, hyaluronan link protein 1 and 2, laminins, and collagens, were differentially regulated in GBM versus controls. Interestingly, a higher degree of collagen hydroxyprolination was also observed for GBM versus controls. Further, two PGs, chondroitin sulfate proteoglycan 4 and agrin, were significantly lower, about 6-fold for isocitrate dehydrogenase-mutant, compared to the WT GBM samples. Differential regulation of O-glycopeptides for PGs, including brevican, neurocan, and versican, was observed for GBM subtypes versus controls. Moreover, an increase in levels of glycosyltransferase and glycosidase enzymes was observed for GBM when compared to control samples. We also report distinct protein, peptide, and glycopeptide features for GBM subtypes comparisons. Taken together, our study informs understanding of the alterations to key matrisomal molecules that occur during GBM development. (Data are available via ProteomeXchange with identifier PXD028931, and the peaks project file is available at Zenodo with DOI 10.5281/zenodo.5911810).

SETMAR Shorter Isoform: A New Prognostic Factor in Glioblastoma

Recent evidence suggests that the chimeric protein SETMAR is a factor of interest in cancer, especially in glioblastoma. However, little is known about the expression of this protein in glioblastoma tissues, and no study has been done to assess if SETMAR could be a prognostic and/or diagnostic marker of glioblastoma. We analyzed protein extracts of 47 glioblastoma samples coming from a local and a national cohort of patients. From the local cohort, we obtained localized biopsies from the central necrosis area, the tumor, and the perilesional brain. From the French Glioblastoma Biobank (FGB), we obtained three types of samples: from the same tumors before and after treatment, from long survivors, and from very short survivors. We studied the correlations between SETMAR amounts, clinical profiles of patients and other associated proteins (PTN, snRNP70 and OLIG2). In glioblastoma tissues, the shorter isoform of SETMAR (S-SETMAR) was predominant over the full-length isoform (FL-SETMAR), and the expression of both SETMAR variants was higher in the tumor compared to the perilesional tissues. Data from the FGB showed that SETMAR amounts were not different between the initial tumors and tumor relapses after treatment. These data also showed a trend toward higher amounts of S-SETMAR in long survivors. In localized biopsies, we found a positive correlation between good prognosis and large amounts of S-SETMAR in the perilesional area. This is the main result presented here: survival in Glioblastoma is correlated with amounts of S-SETMAR in perilesional brain, which should be considered as a new relevant prognosis marker.

Integration of transcriptome and proteome profiles in placenta accreta reveals trophoblast over-migration as the underlying pathogenesis

Background

Placenta accreta (PA) is a major cause of maternal morbidity and mortality in modern obstetrics, few studies have explored the underlying molecular mechanisms.

Methods

In our study, transcriptome and proteome profiling were performed in placental tissues from ten participants including five cases each in the PA and control groups to clarify the pathogenesis of PA.

Results

We identified differential expression of 37,743 transcripts and 160 proteins between the PA and control groups with an overlap rate of 0.09%. The 33 most-significant transcripts and proteins were found and further screened and analyzed. Adhesion-related signature, chemotaxis related signatures and immune related signature were found in the PA group and played a certain role. Sum up two points, three significant indicators, methyl-CpG-binding domain protein 2 (MeCP2), podocin (PODN), and apolipoprotein D (ApoD), which participate in “negative regulation of cell migration”, were downregulated at the mRNA and protein levels in PA group. Furthermore, transwell migration and invasion assay of HTR-8/SVneo cell indicated the all of them impaired the migration and invasion of trophoblast.

Conclusion

A poor correlation was observed between the transcriptome and proteome data and MeCP2, PODN, and ApoD decreased in transcriptome and proteome profiling, resulting in increased migration of trophoblasts in the PA group, which clarify the mechanism of PA and might be the biomarkers or therapy targets in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-021-09336-8.

ENPP2 Methylation in Health and Cancer

Autotaxin (ATX) encoded by Ectonucleotide Pyrophosphatase/Phosphodiesterase 2 (ENPP2) is a key enzyme in Lysophosphatidic Acid (LPA) synthesis implicated in cancer. Although its aberrant expression has been reported, ENPP2 methylation profiles in health and malignancy are not described. We examined in silico the methylation of ENPP2 analyzing publicly available methylome datasets, to identify Differentially Methylated CpGs (DMCs) which were then correlated with expression at gene and isoform levels. Significance indication was set to be FDR corrected p-value < 0.05. Healthy tissues presented methylation in all gene body CGs and lower levels in Promoter Associated (PA) regions, whereas in the majority of the tumors examined (HCC, melanoma, CRC, LC and PC) the methylation pattern was reversed. DMCs identified in the promoter were located in sites recognized by multiple transcription factors, suggesting involvement in gene expression. Alterations in methylation were correlated to an aggressive phenotype in cancer cell lines. In prostate and lung adenocarcinomas, increased methylation of PA CGs was correlated to decreased ENPP2 mRNA expression and to poor prognosis parameters. Collectively, our results corroborate that methylation is an active level of ATX expression regulation in cancer. Our study provides an extended description of the methylation status of ENPP2 in health and cancer and points out specific DMCs of value as prognostic biomarkers.

Progesterone Receptor Together with PKCα Expression as Prognostic Factors for Astrocytomas Malignancy

INTRODUCTION

Astrocytomas are the most common and aggressive primary brain tumors, and they are classified according to the degree of malignancy on a scale of I to IV, in which grade I is the least malignant and grade IV the highest. Many factors are related to astrocytomas progression as progesterone receptor (PR), whose transcriptional activity could be regulated by phosphorylation by protein kinase C alpha (PKCα) at the residue Ser400. Our aim was to investigate if PR phosphorylation together with PKCα expression could be used as a prognostic factor for astrocytomas malignancy.

METHODS

By immunofluorescence, we detected the content of PKCα, PR and its phosphorylation at Ser400 in 46 biopsies from Mexican patients with different astrocytoma malignancy grades; by bioinformatic tools using TCGA data, we evaluated the expression of PR and PKCα mRNA according to astrocytoma malignancy grades. For all statistical analyses, significance was p<0.05.

RESULTS

We detected a positive correlation between the tumor grade and the content of PKCα, PR and its phosphorylation at Ser400, as well as the intracellular colocalization of these proteins. Interestingly, using an in silico assay, we found that the PR and PKCα expression at mRNA level has an inverse ratio with astrocytomas tumor grade.

DISCUSSION

These results indicate that PR and its phosphorylation at Ser400 site, as well as PKCα and their colocalization, could be considered as possible malignancy biomarkers for astrocytomas grades I-IV.

LPA1 Receptor Promotes Progesterone Receptor Phosphorylation through PKCα in Human Glioblastoma Cells

Lysophosphatidic acid (LPA) induces a wide range of cellular processes and its signaling is increased in several cancers including glioblastoma (GBM), a high-grade astrocytoma, which is the most common malignant brain tumor. LPA1 receptor is expressed in GBM cells and its signaling pathways activate protein kinases C (PKCs). A downstream target of PKC, involved in GBM progression, is the intracellular progesterone receptor (PR), which can be phosphorylated by this enzyme, increasing its transcriptional activity. Interestingly, in GBM cells, PKCα isotype translocates to the nucleus after LPA stimulation, resulting in an increase in PR phosphorylation. In this study, we determined that LPA1 receptor activation induces protein-protein interaction between PKCα and PR in human GBM cells; this interaction increased PR phosphorylation in serine400. Moreover, LPA treatment augmented VEGF transcription, a known PR target. This effect was blocked by the PR selective modulator RU486; also, the activation of LPA1/PR signaling promoted migration of GBM cells. Interestingly, using TCGA data base, we found that mRNA expression of LPAR1 increases according to tumor malignancy and correlates with a lower survival in grade III astrocytomas. These results suggest that LPA1/PR pathway regulates GBM progression.

Putting Proteomics Into Immunotherapy for Glioblastoma

In glioblastoma, the most aggressive brain cancer, a complex microenvironment of heterogeneity and immunosuppression, are considerable hurdles to classify the subtypes and promote treatment progression. Treatments for glioblastoma are similar to standard therapies for many other cancers and do not effectively prolong the survival of patients, due to the unique location and heterogeneous characteristics of glioblastoma. Immunotherapy has shown a promising effect for many other tumors, but its application for glioma still has some challenges. The recent breakthrough of high-throughput liquid chromatography-mass spectrometry (LC-MS/MS) systems has allowed researchers to update their strategy for identifying and quantifying thousands of proteins in a much shorter time with lesser effort. The protein maps can contribute to generating a complete map of regulatory systems to elucidate tumor mechanisms. In particular, newly developed unicellular proteomics could be used to determine the microenvironment and heterogeneity. In addition, a large scale of differentiated proteins provides more ways to precisely classify tumor subtypes and construct a larger library for biomarkers and biotargets, especially for immunotherapy. A series of advanced proteomic studies have been devoted to the different aspects of immunotherapy for glioma, including monoclonal antibodies, oncolytic viruses, dendritic cell (DC) vaccines, and chimeric antigen receptor (CAR) T cells. Thus, the application of proteomics in immunotherapy may accelerate research on the treatment of glioblastoma. In this review, we evaluate the frontline applications of proteomics strategies for immunotherapy in glioblastoma research.

Strategies for Functional Interrogation of Big Cancer Data Using Drosophila Cancer Models

Rapid development of high throughput genome analysis technologies accompanied by significant reduction in costs has led to the accumulation of an incredible amount of data during the last decade. The emergence of big data has had a particularly significant impact in biomedical research by providing unprecedented, systems-level access to many disease states including cancer, and has created promising opportunities as well as new challenges. Arguably, the most significant challenge cancer research currently faces is finding effective ways to use big data to improve our understanding of molecular mechanisms underlying tumorigenesis and developing effective new therapies. Functional exploration of these datasets and testing predictions from computational approaches using experimental models to interrogate their biological relevance is a key step towards achieving this goal. Given the daunting scale and complexity of the big data available, experimental systems like Drosophila that allow large-scale functional studies and complex genetic manipulations in a rapid, cost-effective manner will be of particular importance for this purpose. Findings from these large-scale exploratory functional studies can then be used to formulate more specific hypotheses to be explored in mammalian models. Here, I will discuss several strategies for functional exploration of big cancer data using Drosophila cancer models.

Vitamin D metabolites influence expression of genes concerning cellular viability and function in insulin producing β-cells (INS1E)

BACKGROUND

Studies have shown that vitamin D can enhance glucose-stimulated insulin secretion (GSIS) and change the expression of genes in pancreatic β-cells. Still the mechanisms linking vitamin D and GSIS are unknown.

MATERIAL AND METHODS

We used an established β-cell line, INS1E. INS1E cells were pre-treated with 10 nM 1,25(OH)₂vitamin D or 10 nM 25(OH)vitamin D for 72 h and stimulated with 22 mM glucose for 60 min. RNA was extracted for gene expression analysis.

RESULTS

Expression of genes affecting viability, apoptosis and GSIS changed after pre-treatment with both 1,25(OH)₂vitamin D and 25(OH)vitamin D in INS1E cells. Stimulation with glucose after pre-treatment of INS1E cells with 1,25(OH)₂vitamin D resulted in 181 differentially expressed genes, whereas 526 genes were differentially expressed after pre-treatment with 25(OH)vitamin D.

CONCLUSION

Vitamin D metabolites may affect pancreatic β-cells and GSIS through changed gene expression for genes involved in β-cell function and viability.

Single Eye mRNA-Seq Reveals Normalisation of the Retinal Microglial Transcriptome Following Acute Inflammation

Background: Whether retinal microglia can maintain or restore immune homeostasis during and after inflammation is unclear. We performed single-eye mRNA-sequencing on microglia at different timepoints following a single inflammatory stimulus to characterise their transcriptome during and after resolution of endotoxin-induced uveitis (EIU). Experimental Approach: Cx3cr1 ^CreER :R26-tdTomato (C57BL/6) male heterozygotes were administered tamoxifen via different regimes at 4-5 weeks of age. Four weeks post-tamoxifen, mice were injected intravitreally with 10 ng lipopolysaccharide (endotoxin induced uveitis, EIU). Six-hundred retinal microglia were obtained by FACS from individual naïve retinas and at 4 h, 18 h, and 2 weeks following EIU induction. Samples were sequenced to a depth of up to 16.7 million reads using the SMART-Seq v4 Ultra Low Input RNA kit. The data was analysed using Partek software and Ingenuity Pathway Analysis. Genes were considered differentially-expressed (DEG) if the FDR step-up p-value was ≤0.05 and the fold-change was ≥±2. Results: Flow cytometric analysis indicates that the Cx3cr1 ^CreER :R26-tdTomato strain is both sensitive (>95% tagging) and specific (>95% specificity) for microglia when tamoxifen is administered topically to the eye for 3 days. During "early" activation, 613 DEGs were identified. In contrast, 537 DEGs were observed during peak cellular infiltrate and none at 2 weeks, compared to baseline controls (1,069 total unique DEGs). Key marker changes were validated by qPCR, flow cytometry, and fluorescence microscopy. C5AR1 was identified and validated as a robust marker of differentiating microglial subsets during an LPS response. Conclusion: Using EIU to provide a single defined inflammatory stimulus, mRNA-Seq identified acute transcriptional changes in retinal microglia which returned to their original transcriptome after 2 weeks. Yolk-sac derived microglia are capable of restoring their homeostatic state after acute inflammation.

Integration of transcriptomics, proteomics and metabolomics data to reveal the biological mechanisms of abrin injury in human lung epithelial cells

BACKGROUND

Abrin toxin (AT) is a potent plant toxin that belongs to the type Ⅱ ribosome inactivating protein family and is recognized as an important toxin agent for potential bioweapons. Exposure to AT by way of aerosol is the most lethal route, but the mechanism of injury requires further investigation.

MATERIALS AND METHODS

In the present study, we performed a comprehensive analysis of transcriptomics, proteomics and metabolomics on the potential mechanism of abrin injury in human lung epithelial cells.

RESULTS

In total, 6838 genes, 314 proteins and 178 metabolites showed significant changes in human lung epithelial cells after AT treatment. Using molecular function, pathway, and network analysis, the genes and proteins regulated in AT-treated cells were mainly attributed to amino acid metabolism, lipid metabolism, and genetic information processing. Furthermore, a comprehensive analysis of the transcripts, proteins, and metabolites was performed. The results revealed that the correlated genes, proteins, and metabolism pathways regulated in AT-treated human lung epithelial cells were involved in tryptophan metabolism, biosynthesis of amino acids, and protein digestion and absorption.

CONCLUSION

This study provides large-scale omics data to develop new strategies for the prevention, rapid diagnosis, and treatment of AT poisoning, especially AT from aerosol.

Essential role of JunD in cell proliferation is mediated via MYC signaling in prostate cancer cells

JunD, a member of the AP-1 family, is essential for cell proliferation in prostate cancer (PCa) cells. We recently demonstrated that JunD knock-down (KD) in PCa cells results in cell cycle arrest in G1-phase concomitant with a decrease in cyclin D1, Ki67, and c-MYC, but an increase in p21 levels. Furthermore, the over-expression of JunD significantly increased proliferation suggesting JunD regulation of genes required for cell cycle progression. Here, employing gene expression profiling, quantitative proteomics, and validation approaches, we demonstrate that JunD KD is associated with distinct gene and protein expression patterns. Comparative integrative analysis by Ingenuity Pathway Analysis (IPA) identified 1) cell cycle control/regulation as the top canonical pathway whose members exhibited a significant decrease in their expression following JunD KD including PRDX3, PEA15, KIF2C, and CDK2, and 2) JunD dependent genes are associated with cell proliferation, with MYC as the critical downstream regulator. Conversely, JunD over-expression induced the expression of the above genes including c-MYC. We conclude that JunD is a crucial regulator of cell cycle progression and inhibiting its target genes may be an effective approach to block prostate carcinogenesis.

The French glioblastoma biobank (FGB): a national clinicobiological database

BACKGROUND

Glioblastomas (GB) are the most common and lethal primary brain tumors. Significant progress has been made toward identifying potential risk factors for GB and diagnostic and prognostic biomarkers. However, the current standard of care for newly diagnosed GB, the Stupp protocol, has remained unchanged for over a decade. Large-scale translational programs based on a large clinicobiological database are required to improve our understanding of GB biology, potentially facilitating the development of personalized and specifically targeted therapies. With this goal in mind, a well-annotated clinicobiological database housing data and samples from GB patients has been set up in France: the French GB biobank (FGB).

METHODS

The biobank contains data and samples from adult GB patients from 24 centers in France providing written informed consent. Clinical and biomaterial data are stored in anonymized certified electronic case report forms. Biological samples (including frozen and formalin-fixed paraffin-embedded tumor tissues, blood samples, and hair) are conserved in certified biological resource centers or tumor tissue banks at each participating center.

RESULTS

Clinical data and biological materials have been collected for 1087 GB patients. A complete set of samples (tumor, blood and hair) is available for 66%, and at least one frozen tumor sample is available for 88% of the GB patients.

CONCLUSIONS

This large biobank is unique in Europe and can support the large-scale translational projects required to improve GB care. Additional biological materials, such as peritumoral brain zone and fecal samples, will be collected in the future, to respond to research needs.