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Additional file 1: Supplement 1. Identified 411 unique genes related to the studied traits.

Prospects of data and tools for Elixir-Greece

The onset and constant advancement of molecular technologies has enabled the parallel, high-throughput processing of millions of sequence reads, thus ushering a new era with numerous, novel applications in basic, applied and clinical research. In this work we present an analysis framework of NGS genomic data, integrating functional and pathway analyses, for the inference of gene signatures with diagnostic and/or prognostic value. In addition, a system for the integration and processing of multi-layered data is presented, with most of the steps already operated through the Galaxy platform. In the interests of reproducibility, streamlined tools (variant calling, VCF analyzer, BioTranslator) were used, resulting in standardised workflows. A pilot analysis —part of the first national effort for the genomic characterisation of primary cutaneous melanoma— was performed including ten patients, resulting in a short list of candidate genes with a probable causative role in the disease. Ultimate aim is to develop a gene signature, which will be constantly updated through the incorporation of new cases, allowing the accurate patient classification and clustering, towards a precision medicine approach.

Designing a comprehensive workflow for detection and functional annotation of lncRNA. Our pipeline provides tools for RNA-seq analysis, focusing on the detection and functional annotation of lncRNA

Unfolded Protein Response (UPR) and IRE1-XBP1/IRE1-RIDD pathway, in particular, have been highlighted as key contributors to Glioblastoma multiforme (GBM). By now, mRNA and miRNA profiling is the protagonist of GBM research, whereas little is known about the long non-coding RNAs’ (lncRNAs) role. We developed a user-friendly, Galaxy-based Workflow for the mRNA/lncRNA profiling, with implementation on the analysis of RNA-seq data derived from Glioblastoma cell lines with over-expressed, wild-type and mutated IRΕ1 protein. mRNA Differential Expression Analysis (DEA) has been performed and DEA of lncRNA is in progress, with use of lncRNA Workflow, embedded in our local Galaxy instance. Specifically, the lncRNA Workflow branch performs the following processes: 1. FastQC and Trimmomatic 2. RNA STAR alignment 3. lncRNA Assembly and Quantification with Cufflinks 4. Novel prediction of lncRNAs and known lncRNA identification with CPAT and Blast 4. DEA of lncRNAs with EdgeR. Differentially Expressed Gene lists are introduced into BioTranslator platform for functional enrichment analysis and prioritization, while exploration of mRNA/lncRNA relations is performed with LncTar/RIBlast. The implementation of this workflow not only provides efficient mRNA/lncRNA network analysis but alsoensuresreproducibility of results, data sharing and step-by-step monitoring. &nbsp

This article belongs to the Special Issue Epigenetic Therapy: The State of Play in Highly Aggressive Diseases., Epigenetic dysregulation has been recognized as a critical factor contributing to the development of resistance against standard chemotherapy and to breast cancer progression via epithelial-to-mesenchymal transition. Although the efficacy of the first-generation epigenetic drugs (epi-drugs) in solid tumor management has been disappointing, there is an increasing body of evidence showing that epigenome modulation, in synergy with other therapeutic approaches, could play an important role in cancer treatment, reversing acquired therapy resistance. However, the epigenetic therapy of solid malignancies is not straightforward. The emergence of nanotechnologies applied to medicine has brought new opportunities to advance the targeted delivery of epi-drugs while improving their stability and solubility, and minimizing off-target effects. Furthermore, the omics technologies, as powerful molecular epidemiology screening tools, enable new diagnostic and prognostic epigenetic biomarker identification, allowing for patient stratification and tailored management. In combination with new-generation epi-drugs, nanomedicine can help to overcome low therapeutic efficacy in treatment-resistant tumors. This review provides an overview of ongoing clinical trials focusing on combination therapies employing epi-drugs for breast cancer treatment and summarizes the latest nano-based targeted delivery approaches for epi-drugs. Moreover, it highlights the current limitations and obstacles associated with applying these experimental strategies in the clinics., This research was funded by ERA-NET EuroNanoMed II INNOCENT, by the Slovak Research and Development Agency (APVV), grant No. APVV-16-0010, APVV-16-0178, by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 857381, project VISION (Strategies to strengthen scientific excellence and innovation capacity for early diagnosis of gastrointestinal cancers), by ERA-NET EuroNanoMed III CELLUX, H2020-NMBP-TO-IND-2019 SABYDOMA, contract No. 862296 and by Scientific Grant Agency (VEGA), contracts No. 2/0271/17, 2/0138/20 and 2/0052/18.

The ontological tree based on DM genes. Figure S2. Methylation of HOX and IRX gene clusters in the short- and long-TtD subgroups. Figure S3. B-cell distribution in controls and CLL cases. (PDF 1281 kb)

Glucocorticoids play an essential part in anti-leukemic therapies. Resistance is considered crucial for disease prognosis. Glucocorticoids influence the metabolic properties of the cell and consequently the leukemic cells. We have previously outlined the differences that emerge from glucocorticoid treatment used in various concentrations, and lower concentrations manifested a mitogenic effect. A critical established glucocorticoid action is the apoptotic effect they exert on leukemic cells. However, little is known about the molecular response of malignant cells following glucocorticoid exposure. Even less is known about the cell proliferation dynamics governing leukemic cells under glucocorticoid influence. Growth and metabolic features are assumed to be of nonlinear nature. A model based prediction of glucocorticoid effects is derived by applying a non-linear fitting approximation to the measured parameters. Additionally, borrowing principles from the metabolic engineering and thermodynamics disciplines, we calculated the required energetics for cell proliferation under prednisolone treatment. Finally, we utilized a previously reported microarray dataset, to examine whether the predicted and measured parameters of the metabolism and proliferation under glucocorticoids are reflected in gene expression. Hence, making such an approach more pragmatic since those genes could shed light into the mechanisms of glucocorticoid-induced apoptotic resistance action, and subsequently identify novel targets for more efficient glucocorticoid treatments. We have eventually attempted to answer the basic question of what the thermodynamic mechanisms in the transition of the cell population from one state to the next are.

Text. Results on WBC composition in case and control subjects, omic profiles in different subgroups of subjects and assessment of the profile robustness across the cohorts. (PDF 614 kb)

Recent prospective studies have shown that dysregulation of the immune system may precede the development of B‐cell lymphomas (BCL) in immunocompetent individuals. However, to date, the studies were restricted to a few immune markers, which were considered separately. Using a nested case–control study within two European prospective cohorts, we measured plasma levels of 28 immune markers in samples collected a median of 6 years before diagnosis (range 2.01–15.97) in 268 incident cases of BCL (including multiple myeloma [MM]) and matched controls. Linear mixed models and partial least square analyses were used to analyze the association between levels of immune marker and the incidence of BCL and its main histological subtypes and to investigate potential biomarkers predictive of the time to diagnosis. Linear mixed model analyses identified associations linking lower levels of fibroblast growth factor‐2 (FGF‐2 p = 7.2 × 10−4) and transforming growth factor alpha (TGF‐α, p = 6.5 × 10−5) and BCL incidence. Analyses stratified by histological subtypes identified inverse associations for MM subtype including FGF‐2 (p = 7.8 × 10−7), TGF‐α (p = 4.08 × 10−5), fractalkine (p = 1.12 × 10−3), monocyte chemotactic protein‐3 (p = 1.36 × 10−4), macrophage inflammatory protein 1‐alpha (p = 4.6 × 10−4) and vascular endothelial growth factor (p = 4.23 × 10−5). Our results also provided marginal support for already reported associations between chemokines and diffuse large BCL (DLBCL) and cytokines and chronic lymphocytic leukemia (CLL). Case‐only analyses showed that Granulocyte‐macrophage colony stimulating factor levels were consistently higher closer to diagnosis, which provides further evidence of its role in tumor progression. In conclusion, our study suggests a role of growth‐factors in the incidence of MM and of chemokine and cytokine regulation in DLBCL and CLL.