Cancer is one of the leading causes of death worldwide, and the factors responsible for its progression need to be elucidated. Exosomes are structures with an average size of 100 nm that can transport proteins, lipids, and nucleic acids. This review focuses on the role of exosomes in cancer progression and therapy. We discuss how exosomes are able to modulate components of the tumor microenvironment and influence proliferation and migration rates of cancer cells. We also highlight that, depending on their cargo, exosomes can suppress or promote tumor cell progression and can enhance or reduce cancer cell response to radio- and chemo-therapies. In addition, we describe how exosomes can trigger chronic inflammation and lead to immune evasion and tumor progression by focusing on their ability to transfer non-coding RNAs between cells and modulate other molecular signaling pathways such as PTEN and PI3K/Akt in cancer. Subsequently, we discuss the use of exosomes as carriers of anti-tumor agents and genetic tools to control cancer progression. We then discuss the role of tumor-derived exosomes in carcinogenesis. Finally, we devote a section to the study of exosomes as diagnostic and prognostic tools in clinical courses that is important for the treatment of cancer patients. This review provides a comprehensive understanding of the role of exosomes in cancer therapy, focusing on their therapeutic value in cancer progression and remodeling of the tumor microenvironment. Graphical Abstract

microRNAs (miRNAs) are short (∼23nt) single-stranded non-coding RNAs that act as potent post-transcriptional gene expression regulators. Information about miRNA expression and distribution across cell types and tissues is crucial to the understanding of their function and for their translational use as biomarkers or therapeutic targets. DIANA-miTED is the most comprehensive and systematic collection of miRNA expression values derived from the analysis of 15 183 raw human small RNA-Seq (sRNA-Seq) datasets from the Sequence Read Archive (SRA) and The Cancer Genome Atlas (TCGA). Metadata quality maximizes the utility of expression atlases, therefore we manually curated SRA and TCGA-derived information to deliver a comprehensive and standardized set, incorporating in total 199 tissues, 82 anatomical sublocations, 267 cell lines and 261 diseases. miTED offers rich instant visualizations of the expression and sample distributions of requested data across variables, as well as study-wide diagrams and graphs enabling efficient content exploration. Queries also generate links towards state-of-the-art miRNA functional resources, deeming miTED an ideal starting point for expression retrieval, exploration, comparison, and downstream analysis, without requiring bioinformatics support or expertise. DIANA-miTED is freely available at http://www.microrna.gr/mited.

The blood-brain barrier (BBB) is a selective and semipermeable boundary that maintains homeostasis inside the central nervous system (CNS). The BBB permeability of compounds is an important consideration during CNS-acting drug development and is difficult to formulate in a succinct manner. Clinical experiments are the most accurate method of measuring BBB permeability. However, they are time taking and labor-intensive. Therefore, numerous efforts have been made to predict the BBB permeability of compounds using computational methods. However, the accuracy of BBB permeability prediction models has always been an issue. To improve the accuracy of the BBB permeability prediction, we applied deep learning and machine learning algorithms to a dataset of 3,605 diverse compounds. Each compound was encoded with 1,917 features containing 1,444 physicochemical (1D and 2D) properties, 166 molecular access system fingerprints (MACCS), and 307 substructure fingerprints. The prediction performance metrics of the developed models were compared and analyzed. The prediction accuracy of the deep neural network (DNN), one-dimensional convolutional neural network, and convolutional neural network by transfer learning was found to be 98.07, 97.44, and 97.61%, respectively. The best performing DNN-based model was selected for the development of the “DeePred-BBB” model, which can predict the BBB permeability of compounds using their simplified molecular input line entry system (SMILES) notations. It could be useful in the screening of compounds based on their BBB permeability at the preliminary stages of drug development. The DeePred-BBB is made available at https://github.com/12rajnish/DeePred-BBB.

The Semliki Forest Virus (SFV) is an RNA virus with a positive-strand that belongs to the Togaviridae family’s Alphavirus genus. An epidemic was observed among French troops stationed in the Central African Republic, most likely caused by the SFV virus. The two transmembrane proteins El and E2 and the peripheral protein E3 make up the viral spike protein. The virus binds to the host cell and is internalized via endocytosis; endosome acidification causes the E1/E2 heterodimer to dissociate and the E1 subunits to trimerize. Lupenone was evaluated against the E1 spike protein of SFV in this study based on state-of-the-art cheminformatics approaches, including molecular docking, molecular dynamics simulation, and binding free energy calculation. The molecular docking study envisaged major interactions of Lupenone with binding cavity residues involved non-bonded van der Waal’s and Pi-alkyl interactions. Molecular dynamic simulation of a time scale 200 ns corroborated interaction pattern with molecular docking studies between Lupenone and E1 spike protein. Nevertheless, Lupenone intearcation with the E1 spike protein conforming into a stable complex substantiated by free energy landscape (FEL), PCA analysis. Free energy decomposition of the binding cavity resdiues of E1 spike protein also ensured the efficient non-bonded van der Waal’s interaction contributing most energy to interact with the Lupenone. Therefore, Lupenone interacted strongly at the active site conforming into higher structural stability throughout the dynamic evolution of the complex. Thus, this study perhaps comprehend the efficiency of Lupenone as lead molecule against SFV E1 spike protein for future therapeutic purpose.