Different Approaches to Study Molecular Blueprint and Biological Behavior of Brain Tumors: Editorial to the Special Issue "Advances in Molecular Genetics of Brain Tumors".

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TRIM28 Selective Nanobody Reduces Glioblastoma Stem Cell Invasion.

Glioblastoma (GB), is the most common and aggressive malignant primary brain tumour in adults. Intra- and inter-tumour heterogeneity, infiltrative GB cell invasion and presence of therapy-resistant GB stem cells (GSCs) represent major obstacles to favourable prognosis and poor therapy response. Identifying the biomarkers of the most aggressive tumour cells and their more efficient targeting strategies are; therefore, crucial. Recently, transcription factor TRIM28 has been identified as a GB biomarker and, in this study, we have shown high expression of TRIM28 in GB and in low grade gliomas as well as higher expression in GSCs vs. differentiated GB cells, although in both cases not significant. We demonstrated significant in vitro inhibition of GB cells and GSCs invasiveness and spread in zebrafish brains in vivo by anti-TRIM28 selective nanobody NB237. TRIM28 was also enriched in GB (tumour) core and associated with the expression of stem cell genes, but was not prognostic for overall survival. However, based on the above results, we conclude that TRIM28 nanobody NB237 offers a new opportunity as a GB therapeutic tool.

Analysis of miR-9-5p, miR-124-3p, miR-21-5p, miR-138-5p, and miR-1-3p in Glioblastoma Cell Lines and Extracellular Vesicles.

Glioblastoma (GBM), the most common primary brain tumor, is a complex and extremely aggressive disease. Despite recent advances in molecular biology, there is a lack of biomarkers, which would improve GBM's diagnosis, prognosis, and therapy. Here, we analyzed by qPCR the expression levels of a set of miRNAs in GBM and lower-grade glioma human tissue samples and performed a survival analysis in silico. We then determined the expression of same miRNAs and their selected target mRNAs in small extracellular vesicles (sEVs) of GBM cell lines. We showed that the expression of miR-21-5p was significantly increased in GBM tissue compared to lower-grade glioma and reference brain tissue, while miR-124-3p and miR-138-5p were overexpressed in reference brain tissue compared to GBM. We also demonstrated that miR-9-5p and miR-124-3p were overexpressed in the sEVs of GBM stem cell lines (NCH421k or NCH644, respectively) compared to the sEVs of all other GBM cell lines and astrocytes. VIM mRNA, a target of miR-124-3p and miR-138-5p, was overexpressed in the sEVs of U251 and U87 GBM cell lines compared to the sEVs of GBM stem cell line and also astrocytes. Our results suggest VIM mRNA, miR-9-5p miRNA, and miR-124-3p miRNA could serve as biomarkers of the sEVs of GBM cells.

Nanomedicine and Immunotherapy: A Step Further towards Precision Medicine for Glioblastoma.

Owing to the advancement of technology combined with our deeper knowledge of human nature and diseases, we are able to move towards precision medicine, where patients are treated at the individual level in concordance with their genetic profiles. Lately, the integration of nanoparticles in biotechnology and their applications in medicine has allowed us to diagnose and treat disease better and more precisely. As a model disease, we used a grade IV malignant brain tumor (glioblastoma). Significant improvements in diagnosis were achieved with the application of fluorescent nanoparticles for intraoperative magnetic resonance imaging (MRI), allowing for improved tumor cell visibility and increasing the extent of the surgical resection, leading to better patient response. Fluorescent probes can be engineered to be activated through different molecular pathways, which will open the path to individualized glioblastoma diagnosis, monitoring, and treatment. Nanoparticles are also extensively studied as nanovehicles for targeted delivery and more controlled medication release, and some nanomedicines are already in early phases of clinical trials. Moreover, sampling biological fluids will give new insights into glioblastoma pathogenesis due to the presence of extracellular vesicles, circulating tumor cells, and circulating tumor DNA. As current glioblastoma therapy does not provide good quality of life for patients, other approaches such as immunotherapy are explored. To conclude, we reason that development of personalized therapies based on a patient's genetic signature combined with pharmacogenomics and immunogenomic information will significantly change the outcome of glioblastoma patients.

Sequencing the next generation of glioblastomas.

The most aggressive brain malignancy, glioblastoma, accounts for 60-70% of all gliomas and is uniformly fatal. According to the molecular signature, glioblastoma is divided into four subtypes (proneural, neural, classical, and mesenchymal), each with its own genetic background. The Cancer Genome Atlas project provides information about the most common genetic changes in glioblastoma. They involve mutations in TP53, TERT, and PTEN, and amplifications in EFGR, PDGFRA, CDK4, CDK6, MDM2, and MDM4. Recently, epigenetics was used to demonstrate the oncogenic roles of miR-124, miR-137, and miR-128. The most important findings so far are mutations in IDH1/2 and MGMT promoter methylation, which are routinely used as predictive biomarkers in patient care. Current clinical treatment leaves patients with only a 10% chance for 5-year survival. Attempts to define the mutational profile of glioblastoma to identify clinically relevant changes have not yet yielded significant results. This can be attributed to inter- and intra-tumor heterogeneity that is present in most glioblastomas, as well as hypermutation that appears as a consequence of chemotherapy. The evolving field of radiogenomics aims to classify glioblastoma using a combination of magnetic resonance imaging and genomic information. In the era of genomic medicine, next-generation sequencing is extensively used in glioblastoma research because it can detect multiple changes in a single biological sample; its potential in detecting circulating cell-free DNA has been tested in cerebrospinal fluid and plasma, and it shows promise in the examination of the cellular content of extracellular vesicles as a potential source of biomarkers. Next-generation sequencing is making its way into glioblastoma diagnostics. Gene panels like GlioSeq, which includes the most commonly mutated genes, are currently being tested on snap frozen and formalin fixed paraffin embedded tissues. This new methodology is helping to define the "next generation of glioblastomas" - clinically defined and better understood, with greater potential to improve patient care. However, limitations of the necessary infrastructure, space for data storage, technical expertise, and data ownership need to be considered carefully.

Meta-Analysis and Experimental Validation Identified FREM2 and SPRY1 as New Glioblastoma Marker Candidates.

Glioblastoma (GB) is the most aggressive brain malignancy. Although some potential glioblastoma biomarkers have already been identified, there is a lack of cell membrane-bound biomarkers capable of distinguishing brain tissue from glioblastoma and/or glioblastoma stem cells (GSC), which are responsible for the rapid post-operative tumor reoccurrence. In order to find new GB/GSC marker candidates that would be cell surface proteins (CSP), we have performed meta-analysis of genome-scale mRNA expression data from three data repositories (GEO, ArrayExpress and GLIOMASdb). The search yielded ten appropriate datasets, and three (GSE4290/GDS1962, GSE23806/GDS3885, and GLIOMASdb) were used for selection of new GB/GSC marker candidates, while the other seven (GSE4412/GDS1975, GSE4412/GDS1976, E-GEOD-52009, E-GEOD-68848, E-GEOD-16011, E-GEOD-4536, and E-GEOD-74571) were used for bioinformatic validation. The selection identified four new CSP-encoding candidate genes—CD276, FREM2, SPRY1, and SLC47A1—and the bioinformatic validation confirmed these findings. A review of the literature revealed that CD276 is not a novel candidate, while SLC47A1 had lower validation test scores than the other new candidates and was therefore not considered for experimental validation. This validation revealed that the expression of FREM2—but not SPRY1—is higher in glioblastoma cell lines when compared to non-malignant astrocytes. In addition, FREM2 gene and protein expression levels are higher in GB stem-like cell lines than in conventional glioblastoma cell lines. FREM2 is thus proposed as a novel GB biomarker and a putative biomarker of glioblastoma stem cells. Both FREM2 and SPRY1 are expressed on the surface of the GB cells, while SPRY1 alone was found overexpressed in the cytosol of non-malignant astrocytes.

Non-animal glioblastoma models for personalized treatment.

Glioblastoma is an extremely lethal cancer characterized by great heterogeneity at different molecular and cellular levels. As a result, treatment options have moved far from systemic and universal therapies toward targeted treatments and personalized medicine. However, for successful translation from preclinical studies to clinical trials, experiments must be performed on reliable disease models. Numerous experimental models have been developed for glioblastoma, ranging from simple 2D cell cultures to study the nature of the disease to complex 3D models such as neurospheres, organoids, tissue-slice cultures, bioprinted models, and tumor on chip, as perfect prototypes to evaluate the therapeutic potential of different drugs. The presence of multiple research models is consistent with the complexity and molecular diversity of glioblastoma. The advantage of such models is the recapitulation of the tumor environment, and in some cases the preservation of immune system components as well as the creation of simple vessels. There are also two case studies translating in vitro studies on glioblastoma organoids to patients as well as four ongoing clinical trials using glioblastoma models, indicating high clinical potential of glioblastoma models.

Sonication is a suitable method for loading nanobody into glioblastoma small extracellular vesicles.

Glioblastoma is one of the deadliest cancers, therefore novel efficient therapeutic approaches are urgently required. One of such are nanobodies, prospective nano-sized bio-drugs with advantageous characteristics. Nanobodies can target intracellular proteins, but to increase their efficiency, the delivery system should be applied. Here, we examined small extracellular vesicles as a delivery system for anti-vimentin nanobody Nb79. Nb79 was loaded in small extracellular vesicles either by incubation with glioblastoma cells, by passive loading into isolated small extracellular vesicles or by sonication of isolated small extracellular vesicles. Small extracellular vesicles secreted by glioblastoma cells were isolated by ultracentrifugation on sucrose cushion. The size distribution and average size of sonicated and non-sonicated small extracellular vesicles were determined by nanoparticle tracking analysis method. The loading of Nb79 into small extracellular vesicles by incubation with cells, passive loading or sonication was confirmed by Western blot and electron microscopy. The effect of small extracellular vesicles on cell survival was determined by WST-1 reagent. Loading of small extracellular vesicles by incubation of cells with Nb79 was unsuccessful and resulted in substantial cell death. On the other hand, as confirmed by Western blot and electron microscopy, sonication is a successful method for obtaining Nb79-loaded small extracellular vesicles. Small extracellular vesicles also had an effect on cell viability. Small extracellular vesicles without Nb79 increased survival of U251 and NCH644 cells for 20-25%, while the Nb79-loaded small extracellular vesicles decreased survival of NCH421k by 11%. We demonstrated that sonication is a suitable method to load nanobodies into exosome, and these small extracellular vesicles could in turn reduce cell survival. The method could be translated also to other applications, such as targeted delivery system of other protein-based drugs.

Anti-Vimentin Nanobody Decreases Glioblastoma Cell Invasion In Vitro and In Vivo.

PURPOSE: Glioblastoma (GBM) is the most common primary brain tumour and one of the deadliest cancers. In addition to late diagnosis and inadequate treatment, the extremely low survival rate is also due to the lack of appropriate therapeutic biomarkers and corresponding therapeutic agents. One of the potential therapeutic biomarkers is the intermediate filament vimentin, which is associated with epithelial-mesenchymal transition (EMT). The purpose of this study was to analyse the effect of the anti-vimentin nanobody Nb79 on cell invasion in vitro and in vivo. To further our understanding of the mechanism of action, we investigated the association between Nb79 and EMT in GBM and GBM stem cells by analysing the expression levels of key EMT-related proteins. METHODS: The expression of vimentin in glioma tissues and cells was determined by RT-qPCR. An invasion assay was performed on differentiated glioblastoma cell line U-87 MG and stem cell line NCH421k in vitro as well as in vivo in zebrafish embryos. The effect of Nb79 on expression of EMT biomarkers beta-catenin, vimentin, ZEB-1 and ZO1 was determined by Western blot and immunocytochemistry. RESULTS: Our study shows that vimentin is upregulated in glioblastoma tissue compared to lower grade glioma and non-tumour brain tissue. We demonstrated that treatment with Nb79 reduced glioblastoma cell invasion by up to 64% in vitro and up to 21% in vivo. In addition, we found that the tight junction protein ZO-1 had higher expression on the cell membrane, when treated with inhibitory anti-vimentin Nb79 compared to control. CONCLUSION: In conclusion, our results suggest that anti-vimentin nanobody Nb79 is a promising tool to target glioblastoma cell invasion.

Dynamic Intercell Communication between Glioblastoma and Microenvironment through Extracellular Vesicles.

Glioblastoma is simultaneously the most common and most aggressive primary brain tumor in the central nervous system, with poor patient survival and scarce treatment options. Most primary glioblastomas reoccur and evolve radio- and chemoresistant properties which make them resistant to further treatments. Based on gene mutations and expression profiles, glioblastoma is relatively well classified; however, research shows that there is more to glioblastoma biology than that defined solely by its genetic component. Specifically, the overall malignancy of the tumor is also influenced by the dynamic communication to its immediate and distant environment, as important messengers to neighboring cells in the tumor microenvironment extracellular vesicles (EVs) have been identified. EVs and their cargo can modulate the immune microenvironment and other physiological processes, and can interact with the host immune system. They are involved in tumor cell survival and metabolism, tumor initiation, progression, and therapy resistance. However, on the other hand EVs are thought to become an effective treatment alternative, since they can cross the blood-brain barrier, are able of specific cell-targeting and can be loaded with various therapeutic molecules.

Nanobodies targeting ABCC3 for immunotargeted applications in glioblastoma.

The cancer "omics" reveal many clinically relevant alterations that are transforming the molecular characterization of glioblastomas. However, many of these findings are not yet translated into clinical practice due, in part, to the lack of non-invasive biomarkers and the limitations imposed by the blood-brain barrier. Nanobodies, camelid single-domain antibody fragments, emerge as a promising tool for immunotargeted applications for diagnosing and treating glioblastomas. Performing agnostic bioinformatic analysis from glioblastoma patient datasets, we identified ATP Binding Cassette subfamily C member 3 (ABCC3) as a suitable target for immunotargeted applications. The expression of ABCC3 is associated with poor survival and impaired response to temozolomide. Importantly, high expression of ABCC3 is restricted to glioblastoma, with negligible levels in healthy brain tissue, and further correlates with tumor grade and stemness markers. We identified three immunogenic epitopes of ABCC3 which were used to isolate nanobodies from a glioblastoma-specific phage-display nanobody library. Two nanobodies targeting ABCC3 (NbA42 and NbA213) were further characterized and demonstrated in vivo selective recognition of ABCC3 in glioblastoma xenograft mouse models upon systemic administration. We designate NbA42 and NbA213 as new candidates to implement immunotargeted applications guiding a more personalized and precise diagnosis, monitoring, and treatment of glioblastoma patients.

Neuroepigenetics of psychiatric disorders: Focus on lncRNA.

Understanding the pathology of psychiatric disorders is challenging due to their complexity and multifactorial origin. However, development of high-throughput technologies has allowed for better insight into their molecular signatures. Advancement of sequencing methodologies have made it possible to study not only the protein-coding but also the noncoding genome. It is now clear that besides the genetic component, different epigenetic mechanisms play major roles in the onset and development of psychiatric disorders. Among them, examining the role of long noncoding RNAs (lncRNAs) is a relatively new field. Here, we present an overview of what is currently known about the involvement of lncRNAs in schizophrenia, major depressive and bipolar disorders, as well as suicide. The diagnosis of psychiatric disorders mainly relies on clinical evaluation without using measurable biomarkers. In this regard, lncRNA may open new opportunities for development of molecular tests. However, so far only a small set of known lncRNAs have been characterized at molecular level, which means they have a long way to go before clinical implementation. Understanding how changes in lncRNAs affect the appearance and development of psychiatric disorders may lead to a more classified and objective diagnostic system, but also open up new therapeutic targets for these patients.

Cytoskeletal proteins as glioblastoma biomarkers and targets for therapy: A systematic review.

Glioblastoma, the most common primary brain malignancy, is an exceptionally fatal cancer. Lack of suitable biomarkers and efficient treatment largely contribute to the therapy failure. Cytoskeletal proteins are crucial proteins in glioblastoma pathogenesis and can potentially serve as biomarkers and therapeutic targets. Among them, GFAP, has gained most attention as potential diagnostic biomarker, while vimentin and microtubules are considered as prospective therapeutic targets. Microtubules represent one of the best anti-cancer targets due to their critical role in cell proliferation. Despite testing in clinical trials, the efficiency of taxanes, epothilones, vinca-domain binding drugs, colchicine-domain binding drugs and γ-tubulin binding drugs remains to be confirmed. Moreover, tumor treating field that disrupts microtubules draw attention because of its high efficiency and is called "the fourth cancer treatment modality". Thereby, because of the involvement of cytoskeleton in key physiological and pathological processes, its therapeutic potential in glioblastoma is currently extensively investigated.

OligoPrime: An Information System for Oligonucleotide Management.

With the increasing number of molecular biology techniques, large numbers of oligonucleotides are frequently involved in individual research projects. Thus, a dedicated electronic oligonucleotide management system is expected to provide several benefits such as increased oligonucleotide traceability, facilitated sharing of oligonucleotides between laboratories, and simplified (bulk) ordering of oligonucleotides. Herein, we describe OligoPrime, an information system for oligonucleotide management, which presents a computational support for all steps in an oligonucleotide lifecycle, namely, from its ordering and storage to its application, and disposal. OligoPrime is easy to use since it is accessible via a web browser and does not require any installation from the end user's perspective. It allows filtering and search of oligonucleotides by various parameters, which include the exact location of an oligonucleotide, its sequence, and availability. The oligonucleotide database behind the system is shared among the researchers working in the same laboratory or research group. Users might have different roles which define the access permissions and range from students to researchers and primary investigators. Furthermore, OligoPrime is easy to manage and install and is based on open-source software solutions. Its code is freely available at https://github.com/OligoPrime. Moreover, an implementation of OligoPrime, which can be used for testing is available at http://oligoprime.xyz/. To our knowledge, OligoPrime is the only software solution dedicated specifically to oligonucleotide management. We strongly believe that it has a large potential to enhance the transparency of use and to simplify the management of oligonucleotides in academic laboratories and research groups.

Current Technologies for RNA-Directed Liquid Diagnostics.

There is unequivocal acceptance of the variety of enormous potential liquid nucleic acid-based diagnostics seems to offer. However, the existing controversies and the increased awareness of RNA-based techniques in society during the current global COVID-19 pandemic have made the readiness of liquid nucleic acid-based diagnostics for routine use a matter of concern. In this regard-and in the context of oncology-our review presented and discussed the status quo of RNA-based liquid diagnostics. We summarized the technical background of the available assays and benchmarked their applicability against each other. Herein, we compared the technology readiness level in the clinical context, economic aspects, implementation as part of routine point-of-care testing as well as performance power. Since the preventive care market is the most promising application sector, we also investigated whether the developments predominantly occur in the context of early disease detection or surveillance of therapy success. In addition, we provided a careful view on the current biotechnology investment activities in this sector to indicate the most attractive strategies for future economic success. Taken together, our review shall serve as a current reference, at the interplay of technology, clinical use and economic potential, to guide the interested readers in this rapid developing sector of precision medicine.

Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas.

Gliomas are the most common malignant brain tumors with high mortality rates. Recently we showed that the FREM2 gene has a role in glioblastoma progression. Here we reconstructed the FREM2 molecular pathway using the human interactome model. We assessed the biomarker capacity of FREM2 expression and its pathway as the overall survival (OS) and progression-free survival (PFS) biomarkers. To this end, we used three literature and one experimental RNA sequencing datasets collectively covering 566 glioblastomas (GBM) and 1097 low-grade gliomas (LGG). The activation level of deduced FREM2 pathway showed strong biomarker characteristics and significantly outperformed the FREM2 expression level itself. For all relevant datasets, it could robustly discriminate GBM and LGG (p < 1.63 × 10-13, AUC > 0.74). High FREM2 pathway activation level was associated with poor OS in LGG (p < 0.001), and low PFS in LGG (p < 0.001) and GBM (p < 0.05). FREM2 pathway activation level was poor prognosis biomarker for OS (p < 0.05) and PFS (p < 0.05) in LGG with IDH mutation, for PFS in LGG with wild type IDH (p < 0.001) and mutant IDH with 1p/19q codeletion(p < 0.05), in GBM with unmethylated MGMT (p < 0.05), and in GBM with wild type IDH (p < 0.05). Thus, we conclude that the activation level of the FREM2 pathway is a potent new-generation diagnostic and prognostic biomarker for multiple molecular subtypes of GBM and LGG.

Large-Scale Transcriptomics-Driven Approach Revealed Overexpression of CRNDE as a Poor Survival Prognosis Biomarker in Glioblastoma.

Glioblastoma is the most common and malignant brain malignancy worldwide, with a 10-year survival of only 0.7%. Aggressive multimodal treatment is not enough to increase life expectancy and provide good quality of life for glioblastoma patients. In addition, despite decades of research, there are no established biomarkers for early disease diagnosis and monitoring of patient response to treatment. High throughput sequencing technologies allow for the identification of unique molecules from large clinically annotated datasets. Thus, the aim of our study was to identify significant molecular changes between short- and long-term glioblastoma survivors by transcriptome RNA sequencing profiling, followed by differential pathway-activation-level analysis. We used data from the publicly available repositories The Cancer Genome Atlas (TCGA; number of annotated cases = 135) and Chinese Glioma Genome Atlas (CGGA; number of annotated cases = 218), and experimental clinically annotated glioblastoma tissue samples from the Institute of Pathology, Faculty of Medicine in Ljubljana corresponding to 2-58 months overall survival (n = 16). We found one differential gene for long noncoding RNA CRNDE whose overexpression showed correlation to poor patient OS. Moreover, we identified overlapping sets of congruently regulated differential genes involved in cell growth, division, and migration, structure and dynamics of extracellular matrix, DNA methylation, and regulation through noncoding RNAs. Gene ontology analysis can provide additional information about the function of protein- and nonprotein-coding genes of interest and the processes in which they are involved. In the future, this can shape the design of more targeted therapeutic approaches.

Next Generation Sequencing and Machine Learning Technologies Are Painting the Epigenetic Portrait of Glioblastoma.

Even with a rare occurrence of only 1.35% of cancer cases in the United States of America, brain tumors are considered as one of the most lethal malignancies. The most aggressive and invasive type of brain tumor, glioblastoma, accounts for 60-70% of all gliomas and presents with life expectancy of only 12-18 months. Despite trimodal treatment and advances in diagnostic and therapeutic methods, there are no significant changes in patient outcome. Our understanding of glioblastoma was significantly improved with the introduction of next generation sequencing technologies. This led to the identification of different genetic and molecular subtypes, which greatly improve glioblastoma diagnosis. Still, because of the poor life expectancy, novel diagnostic, and treatment methods are broadly explored. Epigenetic modifications like methylation and changes in histone acetylation are such examples. Recently, in addition to genetic and molecular characteristics, epigenetic profiling of glioblastomas is also used for sample classification. Further advancement of next generation sequencing technologies is expected to identify in detail the epigenetic signature of glioblastoma that can open up new therapeutic opportunities for glioblastoma patients. This should be complemented with the use of computational power i.e., machine and deep learning algorithms for objective diagnostics and design of individualized therapies. Using a combination of phenotypic, genotypic, and epigenetic parameters in glioblastoma diagnostics will bring us closer to precision medicine where therapies will be tailored to suit the genetic profile and epigenetic signature of the tumor, which will grant longer life expectancy and better quality of life. Still, a number of obstacles including potential bias, availability of data for minorities in heterogeneous populations, data protection, and validation and independent testing of the learning algorithms have to be overcome on the way.

The Therapeutic Potential of Nanobodies.

Today, bio-medical efforts are entering the subcellular level, which is witnessed with the fast-developing fields of nanomedicine, nanodiagnostics and nanotherapy in conjunction with the implementation of nanoparticles for disease prevention, diagnosis, therapy and follow-up. Nanoparticles or nanocontainers offer advantages including high sensitivity, lower toxicity and improved safety-characteristics that are especially valued in the oncology field. Cancer cells develop and proliferate in complex microenvironments leading to heterogeneous diseases, often with a fatal outcome for the patient. Although antibody-based therapy is widely used in the clinical care of patients with solid tumours, its efficiency definitely needs improvement. Limitations of antibodies result mainly from their big size and poor penetration in solid tissues. Nanobodies are a novel and unique class of antigen-binding fragments, derived from naturally occurring heavy-chain-only antibodies present in the serum of camelids. Their superior properties such as small size, high stability, strong antigen-binding affinity, water solubility and natural origin make them suitable for development into next-generation biodrugs. Less than 30 years after the discovery of functional heavy-chain-only antibodies, the nanobody derivatives are already extensively used by the biotechnology research community. Moreover, a number of nanobodies are under clinical investigation for a wide spectrum of human diseases including inflammation, breast cancer, brain tumours, lung diseases and infectious diseases. Recently, caplacizumab, a bivalent nanobody, received approval from the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) for treatment of patients with thrombotic thrombocytopenic purpura.

Coding of Glioblastoma Progression and Therapy Resistance through Long Noncoding RNAs.

Glioblastoma is the most aggressive and lethal primary brain malignancy, with an average patient survival from diagnosis of 14 months. Glioblastoma also usually progresses as a more invasive phenotype after initial treatment. A major step forward in our understanding of the nature of glioblastoma was achieved with large-scale expression analysis. However, due to genomic complexity and heterogeneity, transcriptomics alone is not enough to define the glioblastoma "fingerprint", so epigenetic mechanisms are being examined, including the noncoding genome. On the basis of their tissue specificity, long noncoding RNAs (lncRNAs) are being explored as new diagnostic and therapeutic targets. In addition, growing evidence indicates that lncRNAs have various roles in resistance to glioblastoma therapies (e.g., MALAT1, H19) and in glioblastoma progression (e.g., CRNDE, HOTAIRM1, ASLNC22381, ASLNC20819). Investigations have also focused on the prognostic value of lncRNAs, as well as the definition of the molecular signatures of glioma, to provide more precise tumor classification. This review discusses the potential that lncRNAs hold for the development of novel diagnostic and, hopefully, therapeutic targets that can contribute to prolonged survival and improved quality of life for patients with glioblastoma.