Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks

C-Biomarker.net: A Cytoscape app for the identification of cancer biomarker genes from cores of large biomolecular networks

Although there have been many studies revealing that biomarker genes for early cancer detection can be found in biomolecular networks, no proper tool exists to discover the cancer biomarker genes from various biomolecular networks. Accordingly, we developed a novel Cytoscape app called C-Biomarker.net, which can identify cancer biomarker genes from cores of various biomolecular networks. Derived from recent research, we designed and implemented the software based on parallel algorithms proposed in this study for working on high-performance computing devices. We tested our software on various network sizes and found the suitable size for each running mode on CPU or GPU. Interestingly, using the software for 17 cancer signaling pathways, we found that on average 70.59% of the top three nodes residing at the innermost core of each pathway are biomarker genes of the cancer respectively to the pathway. Similarly, by the software, we also found 100% of the top ten nodes at both cores of Human Gene Regulatory (HGR) network and Human Protein-Protein Interaction (HPPI) network are multi-cancer biomarkers. These case studies are reliable evidence for performance of cancer biomarker prediction function in the software. Through the case studies, we also suggest that true cores of directed complex networks should be identified by the algorithm of R-core rather than K-core as usual. Finally, we compared the prediction result of our software with those of other researchers and confirmed that our prediction method outperforms the other methods. Taken together, C-Biomarker.net is a reliable tool that efficiently detects biomarker nodes from cores of various large biomolecular networks. The software is available at https://github.com/trantd/C-Biomarker.net.

Tetrabromobisphenol A effects on differentiating mouse embryonic stem cells reveals unexpected impact on immune system

Tetrabromobisphenol A (TBBPA) is a potent flame retardant used in numerous appliances and a major pollutant in households and ecosystems. In vertebrates, it was shown to affect neurodevelopment, the hypothalamic-pituitary-gonadal axis and thyroid signaling, but its toxicity and modes of actions are still a matter of debate. The molecular phenotype resulting from exposure to TBBPA is only poorly described, especially at the level of transcriptome reprogramming, which further limits our understanding of its molecular toxicity. In this work, we combined functional genomics and system biology to provide a system-wide description of the transcriptomic alterations induced by TBBPA acting on differentiating mESCs, and provide potential new toxicity markers. We found that TBBPA-induced transcriptome reprogramming affect a large collection of genes loosely connected within the network of biological pathways, indicating widespread interferences on biological processes. We also found two hotspots of action: at the level of neuronal differentiation markers, and surprisingly, at the level of immune system functions, which has been largely overlooked until now. This effect is particularly strong, as terminal differentiation markers of both myeloid and lymphoid lineages are strongly reduced: the membrane T cell receptor (Cd79a, Cd79b), interleukin seven receptor (Il7r), macrophages cytokine receptor (Csf1r), monocyte chemokine receptor (Ccr2). Also, the high affinity IgE receptor (Fcer1g), a key mediator of allergic reactions, is strongly induced. Thus, the molecular imbalance induce by TBBPA may be stronger than initially realized.