Discovering cancer genes by integrating network and functional properties

<p>Abstract</p> <p>Background</p> <p>Identification of novel cancer-causing genes is one of the main goals in cancer research. The rapid accumulation of genome-wide protein-protein interaction (PPI) data in humans has provided a new basis for studying the topological features of cancer genes in cellular networks. It is important to integrate multiple genomic data sources, including PPI networks, protein domains and Gene Ontology (GO) annotations, to facilitate the identification of cancer genes.</p> <p>Methods</p> <p>Topological features of the PPI network, as well as protein domain compositions, enrichment of gene ontology categories, sequence and evolutionary conservation features were extracted and compared between cancer genes and other genes. The predictive power of various classifiers for identification of cancer genes was evaluated by cross validation. Experimental validation of a subset of the prediction results was conducted using siRNA knockdown and viability assays in human colon cancer cell line DLD-1.</p> <p>Results</p> <p>Cross validation demonstrated advantageous performance of classifiers based on support vector machines (SVMs) with the inclusion of the topological features from the PPI network, protein domain compositions and GO annotations. We then applied the trained SVM classifier to human genes to prioritize putative cancer genes. siRNA knock-down of several SVM predicted cancer genes displayed greatly reduced cell viability in human colon cancer cell line DLD-1.</p> <p>Conclusion</p> <p>Topological features of PPI networks, protein domain compositions and GO annotations are good predictors of cancer genes. The SVM classifier integrates multiple features and as such is useful for prioritizing candidate cancer genes for experimental validations.</p

Mechanical Actuation Systems for the Phenotype Commitment of Stem Cell-Based Tendon and Ligament Tissue Substitutes

High tensile forces transmitted by tendons and ligaments make them susceptible to tearing or complete rupture. The present standard reparative technique is the surgical implantation of auto- or allografts, which often undergo failure. Currently, different cell types and biomaterials are used to design tissue engineered substitutes. Mechanical stimulation driven by dedicated devices can precondition these constructs to a remarkable degree, mimicking the local in vivo environment. A large number of dynamic culture instruments have been developed and many appealing results collected. Of the cells that have been used, tendon stem cells are the most promising for a reliable stretch-induced tenogenesis, but their reduced availability represents a serious limitation to upscaled production. Biomaterials used for scaffold fabrication include both biological molecules and synthetic polymers, the latter being improved by nanotechnologies which reproduce the architecture of native tendons. In addition to cell type and scaffold material, other variables which must be defined in mechanostimulation protocols are the amplitude, frequency, duration and direction of the applied strain. The ideal conditions seem to be those producing intermittent tension rather than continuous loading. In any case, all physical parameters must be adapted to the specific response of the cells used and the tensile properties of the scaffold. Tendon/ligament grafts in animals usually have the advantage of mechanical preconditioning, especially when uniaxial cyclic forces are applied to cells engineered into natural or decellularized scaffolds. However, due to the scarcity of in vivo research, standard protocols still need to be defined for clinical applications