Clinical and Immunologic Characteristics of Colorectal Cancer Tumors Expressing LY6G6D.

The identification of targets that are expressed on the cell membrane is a main goal in cancer research. The Lymphocyte Antigen 6 Family Member G6D (LY6G6D) gene codes for a protein that is mainly present on the surface of colorectal cancer (CRC) cells. Therapeutic strategies against this protein like the development of T cell engagers (TCE) are currently in the early clinical stage. In the present work, we interrogated public genomic datasets including TCGA to evaluate the genomic and immunologic cell profile present in tumors with high expression of LY6G6D. We used data from TCGA, among others, and the Tumor Immune Estimation Resource (TIMER2.0) platform for immune cell estimations and Spearman correlation tests. LY6G6D expression was exclusively present in CRC, particularly in the microsatellite stable (MSS) subtype, and was associated with left-side tumors and the canonical genomic subgroup. Tumors with mutations of APC and p53 expressed elevated levels of LY6G6D. This protein was expressed in tumors with an inert immune microenvironment with an absence of immune cells and co-inhibitory molecules. In conclusion, we described clinical, genomic and immune-pathologic characteristics that can be used to optimize the clinical development of agents against this target. Future studies should be performed to confirm these findings and potentially explore the suggested clinical development options.

Harnessing Hyperthermia: Molecular, Cellular, and Immunological Insights for Enhanced Anticancer Therapies.

Hyperthermia, the raising of tumor temperature (≥39°C), holds great promise as an adjuvant treatment for cancer therapy. This review focuses on 2 key aspects of hyperthermia: its molecular and cellular effects and its impact on the immune system. Hyperthermia has profound effects on critical biological processes. Increased temperatures inhibit DNA repair enzymes, making cancer cells more sensitive to chemotherapy and radiation. Elevated temperatures also induce cell cycle arrest and trigger apoptotic pathways. Furthermore, hyperthermia modifies the expression of heat shock proteins, which play vital roles in cancer therapy, including enhancing immune responses. Hyperthermic treatments also have a significant impact on the body's immune response against tumors, potentially improving the efficacy of immune checkpoint inhibitors. Mild systemic hyperthermia (39°C-41°C) mimics fever, activating immune cells and raising metabolic rates. Intense heat above 50°C can release tumor antigens, enhancing immune reactions. Using photothermal nanoparticles for targeted heating and drug delivery can also modulate the immune response. Hyperthermia emerges as a cost-effective and well-tolerated adjuvant therapy when integrated with immunotherapy. This comprehensive review serves as a valuable resource for the selection of patient-specific treatments and the guidance of future experimental studies.

Therapeutic Potential of Tumor Metabolic Reprogramming in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, with clinical features of high metastatic potential, susceptibility to relapse, and poor prognosis. TNBC lacks the expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). It is characterized by genomic and transcriptional heterogeneity and a tumor microenvironment (TME) with the presence of high levels of stromal tumor-infiltrating lymphocytes (TILs), immunogenicity, and an important immunosuppressive landscape. Recent evidence suggests that metabolic changes in the TME play a key role in molding tumor development by impacting the stromal and immune cell fractions, TME composition, and activation. Hence, a complex inter-talk between metabolic and TME signaling in TNBC exists, highlighting the possibility of uncovering and investigating novel therapeutic targets. A better understanding of the interaction between the TME and tumor cells, and the underlying molecular mechanisms of cell-cell communication signaling, may uncover additional targets for better therapeutic strategies in TNBC treatment. In this review, we aim to discuss the mechanisms in tumor metabolic reprogramming, linking these changes to potential targetable molecular mechanisms to generate new, physical science-inspired clinical translational insights for the cure of TNBC.

Proteotranscriptomic Discrimination of Tumor and Normal Tissues in Renal Cell Carcinoma.

Clear cell renal carcinoma is the most frequent type of kidney cancer, with an increasing incidence rate worldwide. In this research, we used a proteotranscriptomic approach to differentiate normal and tumor tissues in clear cell renal cell carcinoma (ccRCC). Using transcriptomic data of patients with malignant and paired normal tissue samples from gene array cohorts, we identified the top genes over-expressed in ccRCC. We collected surgically resected ccRCC specimens to further investigate the transcriptomic results on the proteome level. The differential protein abundance was evaluated using targeted mass spectrometry (MS). We assembled a database of 558 renal tissue samples from NCBI GEO and used these to uncover the top genes with higher expression in ccRCC. For protein level analysis 162 malignant and normal kidney tissue samples were acquired. The most consistently upregulated genes were IGFBP3, PLIN2, PLOD2, PFKP, VEGFA, and CCND1 (p < 10-5 for each gene). Mass spectrometry further validated the differential protein abundance of these genes (IGFBP3, p = 7.53 × 10-18; PLIN2, p = 3.9 × 10-39; PLOD2, p = 6.51 × 10-36; PFKP, p = 1.01 × 10-47; VEGFA, p = 1.40 × 10-22; CCND1, p = 1.04 × 10-24). We also identified those proteins which correlate with overall survival. Finally, a support vector machine-based classification algorithm using the protein-level data was set up. We used transcriptomic and proteomic data to identify a minimal panel of proteins highly specific for clear cell renal carcinoma tissues. The introduced gene panel could be used as a promising tool in the clinical setting.

Comparison of scientometric achievements at PhD and scientific output ten years later for 4,790 academic researchers.

INTRODUCTION: PhD is the highest awarded degree offered by universities in different disciplines. Owners of a PhD can teach at universities, start independent research and receive a higher salary while further building a scientific career. We examined whether the publication output before the PhD degree has a correlation with subsequent research activities. METHODS: We downloaded publication and citation data from the Hungarian Scientific Bibliography for Hungarian researchers who obtained PhD between the ages of 24 and 45. The researchers were grouped into eleven scientific sections. We examined the number of Q1 publications published in the previous 5 years, the H-index, the total number of citations for the last complete year, and the biological age of the researcher. Each parameter was computed for the year at which the PhD was obtained and ten years later. Pre-PhD publications (and citations for these) were excluded when assessing post-PhD track records. Spearman rank correlation and Kruskal-Wallis test were computed. RESULTS: We analyzed all together 4,790 researchers. We obtained a positive correlation between the number of Q1 publications before and after PhD (corr. coeff. = 0.21-0.54, p<0.01 in all sections), between the H-index before and after PhD (corr. coeff. = 0.32-0.56, p<0.01 in all sections), and between the citations received before and after PhD (corr. coeff. = 0.34-0.51, p<0.01 in all sections). All three metrics measured ten years after the PhD were negatively correlated with the age of the researcher at the time of obtaining the PhD (number of publications corr. coeff. = -0.09-0.22, p<0.05; H-index corr. coeff. = -0.09-0.29, p<0.08; number of citations corr. coeff. = -0.14-0.30, p<0.01). Among all disciplines, Philosophy and History and Engineering sciences show the strongest correlation between pre- and post-PhD output. When running multiple regression analysis for all three metrics as dependent variables and the number of articles, the H-index, the number of citations in the year of the PhD, the calendar year of PhD, and the gender of the researcher as independent variables, the number of articles and the H-index in the year of PhD reached the strongest positive correlations while gender had a negative correlation. CONCLUSIONS: We independently evaluated pre- and post-PhD publication performance. In connection with age, the discipline-specific reference values of scientometric parameters at the time of obtaining the PhD can help to select candidates for postdoctoral grants and positions.

Gene Expression Profiling in Early Breast Cancer-Patient Stratification Based on Molecular and Tumor Microenvironment Features.

Patients with early-stage hormone receptor-positive, human epidermal growth factor receptor 2-negative (HER2-) breast cancer (BC) are typically treated with surgery, followed by adjuvant systemic endocrine therapy with or without adjuvant chemotherapy and radiation therapy. Current guidelines regarding the use of adjuvant systemic therapy depend on clinical and pathological factors, such as the morphological assessment of tumor subtype; histological grade; tumor size; lymphovascular invasion; and lymph node status combined with estrogen receptor, progesterone receptor, and HER2 biomarker profiles assessed using immunohistochemistry and in situ hybridization. Additionally, the prognostic and predictive value of tumor-infiltrating lymphocytes and their composition is emerging as a key marker in triple negative (TNBC) and HER2-enriched molecular breast tumor subtypes. However, all these factors do not necessarily reflect the molecular heterogeneity and complexity of breast cancer. In the last two decades, gene expression signatures or profiling (GEP) tests have been developed to predict the risk of disease recurrence and estimate the potential benefit of receiving adjuvant systemic chemotherapy in patients with luminal breast cancer. GEPs have been utilized to help physicians to refine decision-making process, complementing clinicopathological parameters, and can now be used to classify the risk of recurrence and tailoring personalized treatments. Several clinical trials using GEPs validate the increasing value of such assays in different clinical settings, addressing relevant clinical endpoints. Finally, the recent approval of immune checkpoint inhibitors in TNBC and the increasing use of immunotherapy in different molecular BC populations highlight the opportunity to refine current GEPs by including a variety of immune-related genes that may help to improve predicting drug response and finetune prognosis.

Pancancer survival analysis of cancer hallmark genes.

Cancer hallmark genes are responsible for the most essential phenotypic characteristics of malignant transformation and progression. In this study, our aim was to estimate the prognostic effect of the established cancer hallmark genes in multiple distinct cancer types. RNA-seq HTSeq counts and survival data from 26 different tumor types were acquired from the TCGA repository. DESeq was used for normalization. Correlations between gene expression and survival were computed using the Cox proportional hazards regression and by plotting Kaplan-Meier survival plots. The false discovery rate was calculated to correct for multiple hypothesis testing. Signatures based on genes involved in genome instability and invasion reached significance in most individual cancer types. Thyroid and glioblastoma were independent of hallmark genes (61 and 54 genes significant, respectively), while renal clear cell cancer and low grade gliomas harbored the most prognostic changes (403 and 419 genes significant, respectively). The eight genes with the highest significance included BRCA1 (genome instability, HR 4.26, p < 1E-16), RUNX1 (sustaining proliferative signaling, HR 2.96, p = 3.1E-10) and SERPINE1 (inducing angiogenesis, HR 3.36, p = 1.5E-12) in low grade glioma, CDK1 (cell death resistance, HR = 5.67, p = 2.1E-10) in kidney papillary carcinoma, E2F1 (tumor suppressor, HR 0.38, p = 2.4E-05) and EREG (enabling replicative immortality, HR 3.23, p = 2.1E-07) in cervical cancer, FBP1 (deregulation of cellular energetics, HR 0.45, p = 2.8E-07) in kidney renal clear cell carcinoma and MYC (invasion and metastasis, HR 1.81, p = 5.8E-05) in bladder cancer. We observed unexpected heterogeneity and tissue specificity when correlating cancer hallmark genes and survival. These results will help to prioritize future targeted therapy development in different types of solid tumors.

Currently favored sampling practices for tumor sequencing can produce optimal results in the clinical setting.

Tumor heterogeneity is a consequence of clonal evolution, resulting in a fractal-like architecture with spatially separated main clones, sub-clones and single-cells. As sequencing an entire tumor is not feasible, we ask the question whether there is an optimal clinical sampling strategy that can handle heterogeneity and hypermutations? Here, we tested the effect of sample size, pooling strategy as well as sequencing depth using whole-exome sequencing of ovarian tumor specimens paired with normal blood samples. Our study has an emphasis on clinical application-hence we compared single biopsy, combined local biopsies and combined multi-regional biopsies. Our results show that sequencing from spatially neighboring regions show similar genetic compositions, with few private mutations. Pooling samples from multiple distinct regions of the primary tumor did not increase the overall number of identified mutations but may increase the robustness of detecting clonal mutations. Hypermutating tumors are a special case, since increasing sample size can easily dilute sub-clonal private mutations below detection thresholds. In summary, we compared the effects of sampling strategies (single biopsy, multiple local samples, pooled global sample) on mutation detection by next generation sequencing. In view of the limitations of present tools and technologies, only one sequencing run per sample combined with high coverage (100-300 ×) sequencing is affordable and practical, regardless of the number of samples taken from the same patient.

Independent validation of induced overexpression efficiency across 242 experiments shows a success rate of 39.

Although numerous studies containing induced gene expression have already been published, independent authentication of their results has not yet been performed. Here, we utilized available transcriptomic data to validate the achieved efficiency in overexpression studies. Microarray data of experiments containing cell lines with induced overexpression in one or more genes were analyzed. All together 342 studies were processed, these include 242 different genes overexpressed in 184 cell lines. The final database includes 4,755 treatment-control sample pairs. Successful gene induction (fold change induction over 1.44) was validated in 39.3% of all genes at p < 0.05. Number of repetitions within a study (p < 0.0001) and type of used vector (p = 0.023) had significant impact on successful overexpression efficacy. In summary, over 60% of studies failed to deliver a reproducible overexpression. To achieve higher efficiency, robust and strict study design with multi-level quality control will be necessary.

DUSP4 is associated with increased resistance against anti-HER2 therapy in breast cancer.

The majority of patients develop resistance against suppression of HER2-signaling mediated by trastuzumab in HER2 positive breast cancer (BC). HER2 overexpression activates multiple signaling pathways, including the mitogen-activated protein kinase (MAPK) cascade. MAPK phosphatases (MKPs) are essential regulators of MAPKs and participate in many facets of cellular regulation, including proliferation and apoptosis. We aimed to identify whether differential MKPs are associated with resistance to targeted therapy in patients previously treated with trastuzumab. Using gene chip data of 88 HER2-positive, trastuzumab treated BC patients, candidate MKPs were identified by Receiver Operator Characteristics analysis performed in R. Genes were ranked using their achieved area under the curve (AUC) values and were further restricted to markers significantly associated with worse survival. Functional significance of the two strongest predictive markers was evaluated in vitro by gene silencing in HER2 overexpressing, trastuzumab resistant BC cell lines SKTR and JIMT-1. The strongest predictive MKPs were DUSP4/MKP-2 (AUC=0.75, p=0.0096) and DUSP6/MKP-3 (AUC=0.77, p=5.29E-05). Higher expression for these correlated to worse survival (DUSP4: HR=2.05, p=0.009 and DUSP6: HR=2, p=0.0015). Silencing of DUSP4 had significant sensitization effects - viability of DUSP4 siRNA transfected, trastuzumab treated cells decreased significantly compared to scramble-siRNA transfected controls (SKTR: p=0.016; JIMT-1: p=0.016). In contrast, simultaneous treatment with DUSP6 siRNA and trastuzumab did not alter cell proliferation. Our findings suggest that DUSP4 may represent a new potential target to overcome trastuzumab resistance.

miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients.

PURPOSE: The proper validation of prognostic biomarkers is an important clinical issue in breast cancer research. MicroRNAs (miRNAs) have emerged as a new class of promising breast cancer biomarkers. In the present work, we developed an integrated online bioinformatic tool to validate the prognostic relevance of miRNAs in breast cancer. METHODS: A database was set up by searching the GEO, EGA, TCGA, and PubMed repositories to identify datasets with published miRNA expression and clinical data. Kaplan-Meier survival analysis was performed to validate the prognostic value of a set of 41 previously published survival-associated miRNAs. RESULTS: All together 2178 samples from four independent datasets were integrated into the system including the expression of 1052 distinct human miRNAs. In addition, the web-tool allows for the selection of patients, which can be filtered by receptors status, lymph node involvement, histological grade, and treatments. The complete analysis tool can be accessed online at: www.kmplot.com/mirpower . We used this tool to analyze a large number of deregulated miRNAs associated with breast cancer features and outcome, and confirmed the prognostic value of 26 miRNAs. A significant correlation in three out of four datasets was validated only for miR-29c and miR-101. CONCLUSIONS: In summary, we established an integrated platform capable to mine all available miRNA data to perform a survival analysis for the identification and validation of prognostic miRNA markers in breast cancer.

Validation of RNAi Silencing Efficiency Using Gene Array Data shows 18.5% Failure Rate across 429 Independent Experiments.

No independent cross-validation of success rate for studies utilizing small interfering RNA (siRNA) for gene silencing has been completed before. To assess the influence of experimental parameters like cell line, transfection technique, validation method, and type of control, we have to validate these in a large set of studies. We utilized gene chip data published for siRNA experiments to assess success rate and to compare methods used in these experiments. We searched NCBI GEO for samples with whole transcriptome analysis before and after gene silencing and evaluated the efficiency for the target and off-target genes using the array-based expression data. Wilcoxon signed-rank test was used to assess silencing efficacy and Kruskal-Wallis tests and Spearman rank correlation were used to evaluate study parameters. All together 1,643 samples representing 429 experiments published in 207 studies were evaluated. The fold change (FC) of down-regulation of the target gene was above 0.7 in 18.5% and was above 0.5 in 38.7% of experiments. Silencing efficiency was lowest in MCF7 and highest in SW480 cells (FC = 0.59 and FC = 0.30, respectively, P = 9.3E-06). Studies utilizing Western blot for validation performed better than those with quantitative polymerase chain reaction (qPCR) or microarray (FC = 0.43, FC = 0.47, and FC = 0.55, respectively, P = 2.8E-04). There was no correlation between type of control, transfection method, publication year, and silencing efficiency. Although gene silencing is a robust feature successfully cross-validated in the majority of experiments, efficiency remained insufficient in a significant proportion of studies. Selection of cell line model and validation method had the highest influence on silencing proficiency.

TP53 mutation hits energy metabolism and increases glycolysis in breast cancer.

Promising new hallmarks of cancer is alteration of energy metabolism that involves molecular mechanisms shifting cancer cells to aerobe glycolysis. Our goal was to evaluate the correlation between mutation in the commonly mutated tumor suppressor gene TP53 and metabolism. We established a database comprising mutation and RNA-seq expression data of the TCGA repository and performed receiver operating characteristics (ROC) analysis to compare expression of each gene between TP53 mutated and wild type samples. All together 762 breast cancer samples were evaluated of which 215 had TP53 mutation. Top up-regulated metabolic genes include glycolytic enzymes (e.g. HK3, GPI, GAPDH, PGK1, ENO1), glycolysis regulator (PDK1) and pentose phosphate pathway enzymes (PGD, TKT, RPIA). Gluconeogenesis enzymes (G6PC3, FBP1) were down-regulated. Oxygen consumption and extracellular acidification rates were measured in TP53 wild type and mutant breast cell lines with a microfluorimetric analyzer. Applying metabolic inhibitors in the presence and absence of D-glucose and L-glutamine in cell culture experiments resulted in higher glycolytic and mitochondrial activity in TP53 mutant breast cancer cell lines. In summary, TP53 mutation influences energy metabolism at multiple levels. Our results provide evidence for the synergistic activation of multiple hallmarks linking to these the mutation status of a key driver gene.

Aberrant DNA methylation impacts gene expression and prognosis in breast cancer subtypes.

DNA methylation has a substantial impact on gene expression, affecting the prognosis of breast cancer (BC) patients dependent on molecular subtypes. In this study, we investigated the prognostic relevance of the expression of genes reported as aberrantly methylated, and the link between gene expression and DNA methylation in BC subtypes. The prognostic value of the expression of 144 aberrantly methylated genes was evaluated in ER+/HER2-, HER2+, and ER-/HER2- molecular BC subtypes, in a meta-analysis of two large transcriptomic cohorts of BC patients (n = 1,938 and n = 1,640). The correlation between gene expression and DNA methylation in distinct gene regions was also investigated in an independent dataset of 104 BCs. Survival and Pearson correlation analyses were computed for each gene separately. The expression of 48 genes was significantly associated with BC prognosis (p < 0.05), and 32 of these prognostic genes exhibited a direct expression-methylation correlation. The expression of several immune-related genes, including CD3D and HLA-A, was associated with both relapse-free survival (HR = 0.42, p = 3.5E-06; HR = 0.35, p = 1.7E-08) and overall survival (HR = 0.50, p = 5.5E-04; HR = 0.68, p = 4.5E-02) in ER-/HER2- BCs. On the overall, the distribution of both positive and negative expression-methylation correlation in distinct gene regions have different effects on gene expression and prognosis in BC subtypes. This large-scale meta-analysis allowed the identification of several genes consistently associated with prognosis, whose DNA methylation could represent a promising biomarker for prognostication and clinical stratification of patients with distinct BC subtypes.

Gene expression-based prognostic and predictive tools in breast cancer.

Genomic assays measuring the expression of multiple genes have made their way into clinical practice and their utilization is now recommended by major international guidelines. A basic property of these tests is their capability to sub-divide patients into high- and low-risk cohorts thereby providing prognostic, and in certain settings, predictive decision support. Here, we summarize commercially available assays for breast cancer including RT-PCR and gene chip-based tests. Given the relative uncertainty in cancer treatment, multigene tests have the potential for a significant cost reduction as they can pinpoint those patients for whom chemotherapy proves to be unnecessary. However, concordance of risk assessment for an individual patient is still far from optimal. Additionally, emerging multigene approaches focus on predicting therapy response, which is a black spot of current tests. Promising techniques include the homologous recombination deficiency score, utilization of massive parallel sequencing to identify driver genes, employment of internet-based meta-analysis tools and investigation of miRNA expression signatures. Combination of multiple simultaneous analyses at diagnosis, including classical histopathological diagnostics, monogenic markers, genomic signatures and clinical parameters will most likely bring maximal benefit for patients. As the main driving force behind such genomic tests is the power to achieve cost reduction due to avoiding unnecessary systemic treatment, the future is most likely to hold a further proliferation of such assays.

Meta-analysis of gene expression profiles associated with histological classification and survival in 829 ovarian cancer samples.

Transcriptomic analysis of global gene expression in ovarian carcinoma can identify dysregulated genes capable to serve as molecular markers for histology subtypes and survival. The aim of our study was to validate previous candidate signatures in an independent setting and to identify single genes capable to serve as biomarkers for ovarian cancer progression. As several datasets are available in the GEO today, we were able to perform a true meta-analysis. First, 829 samples (11 datasets) were downloaded, and the predictive power of 16 previously published gene sets was assessed. Of these, eight were capable to discriminate histology subtypes, and none was capable to predict survival. To overcome the differences in previous studies, we used the 829 samples to identify new predictors. Then, we collected 64 ovarian cancer samples (median relapse-free survival 24.5 months) and performed TaqMan Real Time Polimerase Chain Reaction (RT-PCR) analysis for the best 40 genes associated with histology subtypes and survival. Over 90% of subtype-associated genes were confirmed. Overall survival was effectively predicted by hormone receptors (PGR and ESR2) and by TSPAN8. Relapse-free survival was predicted by MAPT and SNCG. In summary, we successfully validated several gene sets in a meta-analysis in large datasets of ovarian samples. Additionally, several individual genes identified were validated in a clinical cohort.

Promoter analysis suggests the implication of NFkappaB/C-Rel transcription factors in biliary atresia.

BACKGROUND/AIMS: Microarray studies used to describe altered gene expression patterns in tissues are a promising new way to provide insight into the pathomechanism of biliary atresia (BA). The hypothesis in this study was that altered gene expression in BA may be linked to regulatory transcription factors (TF). The overrepresentation of transcription factor binding sites (TFBS) in the promoter regions of genes with altered expression would support this hypothesis. METHODOLOGY: Using previously published data, the prevalence of TFBSs in the promoter regions of genes with altered expression in BA was analyzed and compared with the overall prevalence in known promoter sequences. RESULTS: In the pooled BA gene list 195 different TFBS were identified. The prevalence of TFBSs of the members of NFkappaB/c-Rel family was higher compared with the background model. CONCLUSIONS: NFkappaB/c-Rel play a role in the development and function of the immune system. Thus, the results of this study support current theories and experiments which link the immune system to perinatal BA. The information obtained in this analysis will help to understand the pathogenesis of BA, but further experimental and human data are required to validate the significance of these TFs in disease pathogenesis.

[RNA interference and its clinical applications]. / Az RNS-interferencia és klinikai alkalmazásai.

RNA interference is a type of posttranscriptional gene silencing, when short RNA molecules suppress the function of RNAs and block gene expression. Double-stranded RNAs or short interfering RNAs injected into cells activate the RNA-induced silencing complex which degrades the target messenger RNA. The short RNAs produced inside the cell are called micro RNAs. These form a hairpin and then have the same function as double-stranded RNAs. RNA interference is an evolutionary important mechanism having a role in the protection against transposon and viral infection and regulate gene expression. While a number of studies demonstrate the in vivo applicability of RNAi, the first potential clinical trials are arising. So far it has been used to treat viral infections, inhibit macula degeneration, decrease the level of cholesterol in blood, treat cancer and neurodegenerative diseases. However, its application is hampered by ineffective bioinformatics algorithms unable to design effective short interfering RNAs, by low delivery efficiency and by the limited use to temporary antagonist gene silencing. The most important advantage of its application is the exceptional specificity resulting minimal side-effects. For this reason therapies based on RNA interference can be expected to spread in the near future.

[The Danubian Biobank Initiative: synchronizing the biobanking activities of the Danube universities]. / Danubian Biobank Konzorcium: a Duna menti egyetemek "biobanking" tevékenységének összehangolása.

Aging disorders pose an increasing challenge for the public health care systems in Europe. An important approach to cope with this task is the identification of relevant novel disease genes and the control of risk factors using new technological capabilities. A key element in this process is the availability of well classified, large enough patient cohorts and the establishment of quality-controlled central banks for DNA, serum, plasma, and cells/tissues/RNA/proteins together with the development of an IT based infrastructure to provide samples and data required for biomedical studies. The Danubian Biobank initiative connects universities, associated teaching hospitals and endpoint-related rehabilitation clinics along the Danube river and in neighbouring regions. The scientific network focuses on diabetes-related endpoints, vascular disease (e.g. myocardial infarction, stroke, arterial thrombosis, kidney failure), metabolic disease (e.g. obesity, diabetes, metabolic syndrome), and neurodegenerative disorders (e.g. dementia syndromes, Parkinsonism). Task forces are set up for the relevant topics of the biobank project including patient recruitment, sample and data management, public health, epidemiology and genetics, enabling technologies, and research strategies. The project aims to select the most relevant and promising scientific targets utilizing the core competences developed in the individual partner institutions. For this purpose a series of dedicated workshops and conferences are organized as well as joint research grant proposals are submitted.