TMBcalc: a computational pipeline for identifying pan-cancer Tumor Mutational Burden gene signatures.

Background: In the precision medicine era, identifying predictive factors to select patients most likely to benefit from treatment with immunological agents is a crucial and open challenge in oncology.

Methods: This paper presents a pan-cancer analysis of Tumor Mutational Burden (TMB). We developed a novel computational pipeline, TMBcalc, to calculate the TMB.

NetMe 2.0: a web-based platform for extracting and modeling knowledge from biomedical literature as a labeled graph.

Motivation: The rapid increase of bio-medical literature makes it harder and harder for scientists to keep pace with the discoveries on which they build their studies. Therefore, computational tools have become more widespread, among which network analysis plays a crucial role in several life-science contexts. Nevertheless, building correct and complete networks about some user-defined biomedical topics on top of the available literature is still challenging.

Measuring cancer driving force of chromosomal aberrations through multi-layer Boolean implication networks.

Multi-layer Complex networks are commonly used for modeling and analysing biological entities. This paper presents the advantage of using COMBO (Combining Multi Bio Omics) to suggest a new role of the chromosomal aberration as a cancer driver factor. Exploiting the heterogeneous multi-layer networks, COMBO integrates gene expression and DNA-methylation data in order to identify complex bilateral relationships between transcriptome and epigenome.

Interactions between achiral porphyrins and a mature miRNA.

Recent discoveries have revealed that mature miRNAs could form highly ordered structures similar to aptamers, suggesting diverse functions beyond mRNA recognition and degradation. This study focuses on understanding the secondary structures of human miR-26b-5p (UUCAAGUAAUUCAGGAUAGGU) using circular dichroism (CD) and chiroptical probes; in particular, four achiral porphyrins were utilized to both act as chiroptical probes and influence miRNA thermodynamic stability. Various spectroscopic techniques, including UV-Vis, fluorescence, resonance light scattering (RLS), electronic circular dichroism (ECD), and CD melting, were employed to study their interactions.

tRFUniverse: A comprehensive resource for the interactive analyses of tRNA-derived ncRNAs in human cancer.

tRNA-derived ncRNAs are a heterogeneous class of non-coding RNAs recently proposed to be active regulators of gene expression and be involved in many diseases, including cancer. Consequently, several online resources on tRNA-derived ncRNAs have been released. Although interesting, such resources present only basic features and do not adequately exploit the wealth of knowledge available about tRNA-derived ncRNAs.

Expression signature of human endogenous retroviruses in chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) is one of the most diagnosed forms of leukemia worldwide and it is usually classified into two forms: indolent and aggressive. These two forms are characterized by distinct molecular features that drive different responses to treatment and clinical outcomes. In this context, a better understanding of the molecular landscape of the CLL forms may potentially lead to the development of new drugs or the identification of novel biomarkers.

The Ratio of Key Metabolic Transcripts Is a Predictive Biomarker of Breast Cancer Metastasis to the Lung.

Unlabelled: Understanding the rewired metabolism underlying organ-specific metastasis in breast cancer could help identify strategies to improve the treatment and prevention of metastatic disease. Here, we used a systems biology approach to compare metabolic fluxes used by parental breast cancer cells and their brain- and lung-homing derivatives. Divergent lineages had distinct, heritable metabolic fluxes.

DEGGs: an R package with shiny app for the identification of differentially expressed gene-gene interactions in high-throughput sequencing data.

Summary: The discovery of differential gene-gene correlations across phenotypical groups can help identify the activation/deactivation of critical biological processes underlying specific conditions. The presented R package, provided with a count and design matrix, extract networks of group-specific interactions that can be interactively explored through a shiny user-friendly interface. For each gene-gene link, differential statistical significance is provided through robust linear regression with an interaction term.

Controlling Nutritional Status (CONUT) Score as a Potential Prognostic Indicator of In-Hospital Mortality, Sepsis and Length of Stay in an Internal Medicine Department.

The controlling nutritional status (CONUT) score represents poor nutritional status and has been identified as an indicator of adverse outcomes. Our aim was to evaluate the prognostic role of the CONUT score on in-hospital outcomes in an Internal Medicine Department. This is a retrospective study analyzing data from 369 patients, divided into four groups based on the CONUT score: normal (0-1), mild-high (2-4), moderate-high (5-8), and marked high (9-12).

Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing.

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise.

Transcriptome Analysis of Human Endogenous Retroviruses at Locus-Specific Resolution in Non-Small Cell Lung Cancer.

Lung cancer is the second most commonly diagnosed cancer and the leading cause of cancer deaths worldwide. Among its subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the most common, accounting for more than 85% of lung cancer diagnoses. Despite the incredible efforts and recent advances in lung cancer treatments, patients affected by this condition still have a poor prognosis.

Network analysis of synovial RNA sequencing identifies gene-gene interactions predictive of response in rheumatoid arthritis.

Background: To determine whether gene-gene interaction network analysis of RNA sequencing (RNA-Seq) of synovial biopsies in early rheumatoid arthritis (RA) can inform our understanding of RA pathogenesis and yield improved treatment response prediction models.

Methods: We utilized four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We extracted specific gene-gene interaction networks in synovial RNA-Seq to characterize histologically defined pathotypes in early RA and leverage these synovial specific gene-gene networks to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC).

Virus finding tools: current solutions and limitations.

Motivation: The study of the Human Virome remains challenging nowadays. Viral metagenomics, through high-throughput sequencing data, is the best choice for virus discovery. The metagenomics approach is culture-independent and sequence-independent, helping search for either known or novel viruses.

BioTAGME: A Comprehensive Platform for Biological Knowledge Network Analysis.

The inference of novel knowledge and new hypotheses from the current literature analysis is crucial in making new scientific discoveries. In bio-medicine, given the enormous amount of literature and knowledge bases available, the automatic gain of knowledge concerning relationships among biological elements, in the form of semantically related terms (or entities), is rising novel research challenges and corresponding applications. In this regard, we propose BioTAGME, a system that combines an entity-annotation framework based on Wikipedia corpus (i.

Computational Resources for the Interpretation of Variations in Cancer.

A broad ecosystem of resources, databases, and systems to analyze cancer variations is present in the literature. These are a strategic element in the interpretation of NGS experiments. However, the intrinsic wealth of data from RNA-seq, ChipSeq, and DNA-seq can be fully exploited only with the proper skill and knowledge.

Pathway Analysis for Cancer Research and Precision Oncology Applications.

With the advent of OMICs technologies, several bioinformatics methods have been developed to infer biological knowledge from such data. Pathway analysis methodologies help integrate multi-OMICs data and find altered function in known metabolic and signaling pathways. As widely known, such alterations promote the cancer cells' progression and the maintenance of the malignant state.

Computational Methods for Drug Repurposing.

The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms.

NETME: on-the-fly knowledge network construction from biomedical literature.

Background: The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling.

Results: We introduce a novel system called , which, starting from a set of full-texts obtained from , through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements.

Novel Mechanisms of Tumor Promotion by the Insulin Receptor Isoform A in Triple-Negative Breast Cancer Cells.

The insulin receptor isoform A (IR-A) plays an increasingly recognized role in fetal growth and tumor biology in response to circulating insulin and/or locally produced IGF2. This role seems not to be shared by the IR isoform B (IR-B). We aimed to dissect the specific impact of IR isoforms in modulating insulin signaling in triple negative breast cancer (TNBC) cells.

AKT3-mediated IWS1 phosphorylation promotes the proliferation of EGFR-mutant lung adenocarcinomas through cell cycle-regulated U2AF2 RNA splicing.

AKT-phosphorylated IWS1 regulates alternative RNA splicing via a pathway that is active in lung cancer. RNA-seq studies in lung adenocarcinoma cells lacking phosphorylated IWS1, identified a exon 2-deficient U2AF2 splice variant. Here, we show that exon 2 inclusion in the U2AF2 mRNA is a cell cycle-dependent process that is regulated by LEDGF/SRSF1 splicing complexes, whose assembly is controlled by the IWS1 phosphorylation-dependent deposition of histone H3K36me3 marks in the body of target genes.

Psychopathological outcomes and defence mechanisms in clinically healed adults with a paediatric cancer history: an exploratory study.

Background: The incidence of paediatric cancers has increased in recent years; however, with advances in the treatment of paediatric cancer, almost 80% of children and adolescents who receive a diagnosis of cancer become long-term survivors. Given the high stress levels associated with cancer, it becomes important to ascertain the risk and likelihood of psychiatric disorders in adult paediatric cancer survivors.

Aims: This study aims to investigate the relationship between defence styles and predisposition to psychiatric diseases in adults with a history of paediatric cancer.

Bioinformatics Approach to Mitigate Mislabeling in EU Seafood Market and Protect Consumer Health.

Fisheries products are some of the most traded commodities world-wide and the potential for fraud is a serious concern. Fish fraud represents a threat to human health and poses serious concerns due to the consumption of toxins, highly allergenic species, contaminates or zoonotic parasites, which may be present in substituted fish. The substitution of more expensive fish by cheaper species, with similar morphological characteristics but different origins, reflects the need for greater transparency and traceability upon which which the security of the entire seafood value-chain depends.

VECTOR: An Integrated Correlation Network Database for the Identification of CeRNA Axes in Uveal Melanoma.

Uveal melanoma (UM) is the most common primary intraocular malignant tumor in adults and, although its genetic background has been extensively studied, little is known about the contribution of non-coding RNAs (ncRNAs) to its pathogenesis. Indeed, its competitive endogenous RNA (ceRNA) regulatory network comprising microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and mRNAs has been insufficiently explored. Thanks to UM findings from The Cancer Genome Atlas (TCGA), it is now possible to statistically elaborate these data to identify the expression relationships among RNAs and correlative interaction data.

PHENSIM: Phenotype Simulator.

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues' physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. Here we propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways.