A method for in silico exploration of potential glioblastoma multiforme attractors using single-cell RNA sequencing.

We presented a method to find potential cancer attractors using single-cell RNA sequencing (scRNA-seq) data. We tested our method in a Glioblastoma Multiforme (GBM) dataset, an aggressive brain tumor presenting high heterogeneity. Using the cancer attractor concept, we argued that the GBM's underlying dynamics could partially explain the observed heterogeneity, with the dataset covering a representative region around the attractor.

Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity.

Glioblastoma Multiforme is a brain tumor distinguished by its aggressiveness. We suggested that this aggressiveness leads single-cell RNA-sequence data (scRNA-seq) to span a representative portion of the cancer attractors domain. This conjecture allowed us to interpret the scRNA-seq heterogeneity as reflecting a representative trajectory within the attractor's domain.

Optimizing therapeutic targets for breast cancer using boolean network models.

Studying gene regulatory networks associated with cancer provides valuable insights for therapeutic purposes, given that cancer is fundamentally a genetic disease. However, as the number of genes in the system increases, the complexity arising from the interconnections between network components grows exponentially. In this study, using Boolean logic to adjust the existing relationships between network components has facilitated simplifying the modeling process, enabling the generation of attractors that represent cell phenotypes based on breast cancer RNA-seq data.

A Strategy Utilizing Protein-Protein Interaction Hubs for the Treatment of Cancer Diseases.

We describe a strategy for the development of a rational approach of neoplastic disease therapy based on the demonstration that scale-free networks are susceptible to specific attacks directed against its connective hubs. This strategy involves the (i) selection of up-regulated hubs of connectivity in the tumors interactome, (ii) drug repurposing of these hubs, (iii) RNA silencing of non-druggable hubs, (iv) in vitro hub validation, (v) tumor-on-a-chip, (vi) in vivo validation, and (vii) clinical trial. Hubs are protein targets that are assessed as targets for rational therapy of cancer in the context of personalized oncology.

A Data Science Approach for the Identification of Molecular Signatures of Aggressive Cancers.

The main hallmarks of cancer include sustaining proliferative signaling and resisting cell death. We analyzed the genes of the WNT pathway and seven cross-linked pathways that may explain the differences in aggressiveness among cancer types. We divided six cancer types (liver, lung, stomach, kidney, prostate, and thyroid) into classes of high (H) and low (L) aggressiveness considering the TCGA data, and their correlations between Shannon entropy and 5-year overall survival (OS).

Data-Driven Modeling of Breast Cancer Tumors Using Boolean Networks.

Cancer is a genomic disease involving various intertwined pathways with complex cross-communication links. Conceptually, this complex interconnected system forms a network, which allows one to model the dynamic behavior of the elements that characterize it to describe the entire system's development in its various evolutionary stages of carcinogenesis. Knowing the activation or inhibition status of the genes that make up the network during its temporal evolution is necessary for the rational intervention on the critical factors for controlling the system's dynamic evolution.

Trypanosoma brucei RRP44 is involved in an early stage of large ribosomal subunit RNA maturation.

Ribosomal RNA precursors undergo a series of structural and chemical modifications to generate matured RNA molecules that will comprise ribosomes. This maturation process involves a large set of accessory proteins as well as ribonucleases, responsible for removal of the external and internal transcribed spacers from the pre-rRNA. Early-diverging eukaryotes belonging to the Kinetoplastida class display several unique characteristics, in particular in terms of RNA synthesis and maturation.

Low resolution structure of the human alpha4 protein (IgBP1) and studies on the stability of alpha4 and of its yeast ortholog Tap42.

The yeast Tap42 and mammalian alpha4 proteins belong to a highly conserved family of regulators of the type 2A phosphatases, which participate in the rapamycin-sensitive signaling pathway, connecting nutrient availability to cell growth. The mechanism of regulation involves binding of Tap42 to Sit4 and PPH21/22 in yeast and binding of alpha4 to the catalytic subunits of type 2A-related phosphatases PP2A, PP4 and PP6 in mammals. Both recombinant proteins undergo partial proteolysis, generating stable N-terminal fragments.

Concurrence of fragile X and Klinefelter syndromes: report of a new case of paternal nondisjunction.

The concurrence of fragile X and Klinefelter syndromes would be expected occasionally. Therefore, the analysis of the literature showed that the concurrence of both conditions was found at least 16 times. Among them, only seven cases were analyzed for the parental origin of the extra chromosome X, suggesting that the maternal nondisjunction was preferentially inherited.